formatting fixes

app.py

@@ -38,14 +38,15 @@ def get_available_models():
     return {
         "text_models": AVAILABLE_ISSUE_MODELS_BY_PROVIDER,
         "vision_models": AVAILABLE_SKETCH_MODELS_BY_PROVIDER,
-        "providers": list(AVAILABLE_ISSUE_MODELS_BY_PROVIDER.keys())
+        "providers": list(AVAILABLE_ISSUE_MODELS_BY_PROVIDER.keys()),
     }
-    
+
+
 def fetch_all_pages(url: str, headers: dict, progress: gr.Progress | None = None, desc: str = ""):
     """Helper function to fetch all pages from a paginated GitHub API endpoint."""
     results = []
     page_num = 1
-    base_url = url.split('&page=')[0]
+    base_url = url.split("&page=")[0]
 
     while True:
         paginated_url = f"{base_url}&page={page_num}"
@@ -67,14 +68,18 @@ def fetch_all_pages(url: str, headers: dict, progress: gr.Progress | None = None
     return results
 
 
-def sync_repository(repo_url: str, github_token: str | None = None, progress=gr.Progress(track_tqdm=True)):
+def sync_repository(
+    repo_url: str,
+    github_token: str | None = None,
+    progress=gr.Progress(track_tqdm=True),
+):
     """
     Performs a lightweight sync of a GitHub repository with the local database.
-    
+
     Args:
         repo_url (str): The full URL of the GitHub repository (e.g., 'https://github.com/gradio-app/gradio').
         github_token (str, optional): A GitHub Personal Access Token. Optional. If not provided, the tool will work but may hit public API rate limits.
-    
+
     Returns:
         str: A status message indicating completion.
     """
@@ -137,7 +142,13 @@ def sync_repository(repo_url: str, github_token: str | None = None, progress=gr.
                 VALUES (%s, %s, %s, %s, %s)
                 ON CONFLICT (tag_name) DO NOTHING;
                 """,
-                (repo_slug, release["tag_name"], release["name"], release.get("body"), release["published_at"]),
+                (
+                    repo_slug,
+                    release["tag_name"],
+                    release["name"],
+                    release.get("body"),
+                    release["published_at"],
+                ),
             )
         conn.commit()
         conn.close()
@@ -145,14 +156,15 @@ def sync_repository(repo_url: str, github_token: str | None = None, progress=gr.
 
     except Exception as e:
         import traceback
+
         traceback.print_exc()
-                
-        error_msg = str(e)        
+
+        error_msg = str(e)
         if "429" in error_msg or "Rate" in error_msg:
-            detailed_msg = f"Rate Limit Error: The AI provider is busy. Please wait a moment."
+            detailed_msg = "Rate Limit Error: The AI provider is busy. Please wait a moment."
         else:
             detailed_msg = f"System Error: {error_msg}"
-        
+
         raise gr.Error(detailed_msg)
 
 
@@ -164,7 +176,7 @@ def analyze_github_issue(
     github_token: str | None = None,
     llm_api_key: str | None = None,
     request: gr.Request = None,
-    progress=gr.Progress(track_tqdm=True)
+    progress=gr.Progress(track_tqdm=True),
 ):
     """
     Analyzes a single GitHub issue to determine its resolution status by checking PRs and Releases.
@@ -176,16 +188,15 @@ def analyze_github_issue(
         llm_model (str, optional): The specific model name. Defaults to 'gemini-2.0-flash'.
         github_token (str, optional): GitHub Token. Optional. Recommended for higher rate limits.
         llm_api_key (str, optional): API Key for the LLM. **OPTIONAL**. If not provided (empty string), the server will use its internal environment keys to perform the analysis.
-    
+
     Returns:
         str: An HTML-formatted analysis report.
     """
-    try:
-        _validate_api_keys(llm_api_key, request)
-         
-        if not repo_url or not issue_number:
-            return "## Error\nPlease provide both a repository URL and an issue number."
+    _validate_api_keys(llm_api_key, request)
 
+    if not repo_url or not issue_number:
+        return "## Error\nPlease provide both a repository URL and an issue number."
+    try:
         repo_slug = "/".join(repo_url.strip().replace("https://github.com/", "").split("/")[:2])
         headers = {"Authorization": f"token {github_token}"} if github_token else {}
         conn = connect()
@@ -214,14 +225,20 @@ def analyze_github_issue(
                 pr_urls_to_fetch.append(f"https://api.github.com/repos/{repo_slug}/pulls/{pr_num}")
 
         for i, url in enumerate(pr_urls_to_fetch):
-            progress(0.4 + (0.2 * (i / len(pr_urls_to_fetch))) if pr_urls_to_fetch else 0.4, desc=f"Fetching details for PR {i + 1}/{len(pr_urls_to_fetch)}...")
+            progress(
+                0.4 + (0.2 * (i / len(pr_urls_to_fetch))) if pr_urls_to_fetch else 0.4,
+                desc=f"Fetching details for PR {i + 1}/{len(pr_urls_to_fetch)}...",
+            )
             pr_res = requests.get(url, headers=headers)
             if pr_res.status_code == 200:
                 linked_prs_details.append(pr_res.json())
 
         # 2. GET RELEASE DATA FROM CACHE
         progress(0.6, desc="Querying local release cache...")
-        cursor.execute("SELECT * FROM releases WHERE repo = %s ORDER BY published_at DESC LIMIT 50", (repo_slug,))
+        cursor.execute(
+            "SELECT * FROM releases WHERE repo = %s ORDER BY published_at DESC LIMIT 50",
+            (repo_slug,),
+        )
         releases = cursor.fetchall()
         conn.close()
 
@@ -237,7 +254,7 @@ def analyze_github_issue(
                     llm_model,
                     messages=[{"role": "user", "content": summarizer_prompt}],
                     temperature=0.0,
-                    api_key=llm_api_key
+                    api_key=llm_api_key,
                 )
             except Exception:
                 additional_problems_summary = "Could not summarize comments due to an error."
@@ -258,7 +275,10 @@ def analyze_github_issue(
 
         release_notes_summary = (
             "\n\n".join(
-                [f"- **Release {r['tag_name']} ({str(r['published_at'])[:10]}):**\n{str(r['body'])[:500] if r['body'] else 'No description.'}..." for r in releases]
+                [
+                    f"- **Release {r['tag_name']} ({str(r['published_at'])[:10]}):**\n{str(r['body'])[:500] if r['body'] else 'No description.'}..."
+                    for r in releases
+                ]
             )
             if releases
             else "No releases found in local cache."
@@ -270,7 +290,7 @@ def analyze_github_issue(
             "issue_state": issue_data["state"],
             "issue_body": issue_data["body"] or "No description provided.",
             "additional_problems_summary": additional_problems_summary.strip(),
-            "issue_labels": json.dumps([label["name"] for label in issue_data["labels"]]) if issue_data["labels"] else "None",
+            "issue_labels": (json.dumps([label["name"] for label in issue_data["labels"]]) if issue_data["labels"] else "None"),
             "pull_requests_summary": pull_requests_summary,
             "release_notes_summary": release_notes_summary,
         }
@@ -286,9 +306,9 @@ def analyze_github_issue(
             messages=messages,
             temperature=0.1,
             max_tokens=2048,
-            api_key=llm_api_key
+            api_key=llm_api_key,
         )
-       
+
         report_start_marker = f"## Analysis of Issue #{issue_number}"
         report_start_index = raw_output.find(report_start_marker)
 
@@ -316,16 +336,17 @@ def analyze_github_issue(
 
     except Exception as e:
         import traceback
+
         traceback.print_exc()
-                
+
         error_msg = str(e)
         if "Auth" in error_msg or "Key" in error_msg or "401" in error_msg:
             detailed_msg = f"Authentication Error: The provided API Key for {llm_provider} is invalid or expired."
         elif "429" in error_msg or "Rate" in error_msg:
-            detailed_msg = f"Rate Limit Error: The AI provider is busy. Please wait a moment."
+            detailed_msg = "Rate Limit Error: The AI provider is busy. Please wait a moment."
         else:
             detailed_msg = f"System Error: {error_msg}"
-        
+
         raise gr.Error(detailed_msg)
 
 
@@ -337,11 +358,11 @@ def find_duplicate_issues(
     github_token: str | None = None,
     llm_api_key: str | None = None,
     request: gr.Request = None,
-    progress=gr.Progress()
+    progress=gr.Progress(),
 ):
     """
     Finds potential duplicate issues for a given issue using mentions and keyword search.
-    
+
     Args:
         repo_url (str): The full URL of the GitHub repository.
         issue_number (str): The main issue number to check.
@@ -349,12 +370,12 @@ def find_duplicate_issues(
         llm_model (str, optional): The model name. Defaults to 'gemini-2.0-flash'.
         github_token (str, optional): GitHub Token. Optional.
         llm_api_key (str, optional): API Key for the LLM. **OPTIONAL**. If not provided, the server uses its own keys.
-    
+
     Returns:
         str: A Markdown list of potential duplicates.
     """
     _validate_api_keys(llm_api_key, request)
-            
+
     if not repo_url or not issue_number:
         return "Please provide the repository and the main issue number."
 
@@ -366,9 +387,12 @@ def find_duplicate_issues(
         cursor = conn.cursor()
 
         progress(0, desc="Fetching main issue from cache...")
-        cursor.execute("SELECT * FROM items WHERE repo = %s AND number = %s", (repo_slug, issue_number))
+        cursor.execute(
+            "SELECT * FROM items WHERE repo = %s AND number = %s",
+            (repo_slug, issue_number),
+        )
         main_issue = cursor.fetchone()
-        
+
         if not main_issue:
             conn.close()
             return "Main issue not found in cache. Please synchronize the repository first."
@@ -426,46 +450,48 @@ def find_duplicate_issues(
             llm_model,
             messages=messages,
             temperature=0.0,
-            api_key=llm_api_key
+            api_key=llm_api_key,
         )
         return analysis
 
     except Exception as e:
         import traceback
+
         traceback.print_exc()
-                
+
         error_msg = str(e)
         if "Auth" in error_msg or "Key" in error_msg or "401" in error_msg:
             detailed_msg = f"Authentication Error: The provided API Key for {llm_provider} is invalid or expired."
         elif "429" in error_msg or "Rate" in error_msg:
-            detailed_msg = f"Rate Limit Error: The AI provider is busy. Please wait a moment."
+            detailed_msg = "Rate Limit Error: The AI provider is busy. Please wait a moment."
         else:
             detailed_msg = f"System Error: {error_msg}"
-        
+
         raise gr.Error(detailed_msg)
 
+
 def prioritize_open_issues(
     repo_url: str,
     llm_provider: str = "gemini",
     llm_model: str = "gemini-2.0-flash",
     llm_api_key: str | None = None,
     request: gr.Request = None,
-    progress=gr.Progress(track_tqdm=True)
+    progress=gr.Progress(track_tqdm=True),
 ):
     """
     Analyzes open issues from the cache to create a prioritized backlog.
-    
+
     Args:
         repo_url (str): The URL of the GitHub repository.
         llm_provider (str, optional): The LLM provider. Defaults to 'gemini'.
         llm_model (str, optional): The model name. Defaults to 'gemini-2.0-flash'.
         llm_api_key (str, optional): API Key for the LLM. **OPTIONAL**. If not provided, the server uses its own keys.
-    
+
     Returns:
         str: A Markdown priority list.
     """
     _validate_api_keys(llm_api_key, request)
-    
+
     if not repo_url:
         return "Please provide the repository URL."
 
@@ -476,7 +502,8 @@ def prioritize_open_issues(
 
         progress(0, desc="Fetching open issues from cache...")
         cursor.execute(
-            "SELECT * FROM items WHERE repo = %s AND is_pr = FALSE AND state = 'open' ORDER BY comments DESC, reactions DESC LIMIT 50", (repo_slug,)
+            "SELECT * FROM items WHERE repo = %s AND is_pr = FALSE AND state = 'open' ORDER BY comments DESC, reactions DESC LIMIT 50",
+            (repo_slug,),
         )
         open_issues = cursor.fetchall()
         conn.close()
@@ -505,36 +532,38 @@ def prioritize_open_issues(
             messages=messages,
             temperature=0.1,
             max_tokens=4096,
-            api_key=llm_api_key
+            api_key=llm_api_key,
         )
         return analysis
 
     except Exception as e:
         import traceback
+
         traceback.print_exc()
-                
+
         error_msg = str(e)
         if "Auth" in error_msg or "Key" in error_msg or "401" in error_msg:
             detailed_msg = f"Authentication Error: The provided API Key for {llm_provider} is invalid or expired."
         elif "429" in error_msg or "Rate" in error_msg:
-            detailed_msg = f"Rate Limit Error: The AI provider is busy. Please wait a moment."
+            detailed_msg = "Rate Limit Error: The AI provider is busy. Please wait a moment."
         else:
             detailed_msg = f"System Error: {error_msg}"
-        
+
         raise gr.Error(detailed_msg)
 
+
 def reply_and_close_issue(
     repo_url: str,
     issue_number: str,
     comment_body: str,
     close_issue: bool = False,
     github_token: str | None = None,
-    request: gr.Request = None
+    request: gr.Request = None,
 ):
     """
     Posts a comment on a GitHub issue and optionally closes it.
     Crucial for the 'Actionable AI' capability.
-    
+
     Args:
         repo_url (str): Full repository URL.
         issue_number (str): The issue number.
@@ -551,7 +580,7 @@ def reply_and_close_issue(
         repo_slug = f"{owner}/{repo}"
         headers = {
             "Authorization": f"token {github_token}",
-            "Accept": "application/vnd.github.v3+json"
+            "Accept": "application/vnd.github.v3+json",
         }
 
         # 1. Post the Comment
@@ -567,10 +596,14 @@ def reply_and_close_issue(
         if close_issue:
             issue_url = f"https://api.github.com/repos/{repo_slug}/issues/{issue_number}"
             # state_reason can be 'completed' or 'not_planned'
-            close_resp = requests.patch(issue_url, headers=headers, json={"state": "closed", "state_reason": "completed"})
+            close_resp = requests.patch(
+                issue_url,
+                headers=headers,
+                json={"state": "closed", "state_reason": "completed"},
+            )
             close_resp.raise_for_status()
             action_log += f"\n🔒 Issue #{issue_number} has been CLOSED."
-        
+
         return f"## Success\n{action_log}"
 
     except requests.exceptions.HTTPError as e:
@@ -581,19 +614,21 @@ def reply_and_close_issue(
         raise gr.Error(f"GitHub API Error: {e}")
     except Exception as e:
         import traceback
+
         traceback.print_exc()
         raise gr.Error(f"System Error: {str(e)}")
 
+
 def generate_theme(
     prompt: str,
     llm_provider: str = "gemini",
     llm_model: str = "gemini-2.0-flash",
     llm_api_key: str | None = None,
-    request: gr.Request = None
+    request: gr.Request = None,
 ):
     """
     Generates a Gradio theme based on a text prompt.
-    
+
     Args:
         prompt (str): Description of the desired theme.
         llm_provider (str, optional): LLM provider. Defaults to 'gemini'.
@@ -601,10 +636,20 @@ def generate_theme(
         llm_api_key (str, optional): API Key. **OPTIONAL**. If empty, uses server keys.
     """
     _validate_api_keys(llm_api_key, request)
-    
+
     try:
-        messages = [{"role": "system", "content": SYSTEM_PROMPT_THEME_GENERATOR_EN}, {"role": "user", "content": prompt}]
-        llm_output = LLMFactory.call(llm_provider=llm_provider, llm_model=llm_model, messages=messages, temperature=0.1, max_tokens=2048, api_key=llm_api_key)
+        messages = [
+            {"role": "system", "content": SYSTEM_PROMPT_THEME_GENERATOR_EN},
+            {"role": "user", "content": prompt},
+        ]
+        llm_output = LLMFactory.call(
+            llm_provider=llm_provider,
+            llm_model=llm_model,
+            messages=messages,
+            temperature=0.1,
+            max_tokens=2048,
+            api_key=llm_api_key,
+        )
 
         if isinstance(llm_output, str) and llm_output.startswith("Error:"):
             raise Exception(llm_output)
@@ -629,11 +674,11 @@ def generate_theme(
 
         if "font" in constructor_kwargs:
             fonts = constructor_kwargs.get("font", [])
-            constructor_kwargs["font"] = [gr.themes.GoogleFont(name) if name not in ["ui-sans-serif", "system-ui", "sans-serif"] else name for name in fonts]
+            constructor_kwargs["font"] = [(gr.themes.GoogleFont(name) if name not in ["ui-sans-serif", "system-ui", "sans-serif"] else name) for name in fonts]
         if "font_mono" in constructor_kwargs:
             mono_fonts = constructor_kwargs.get("font_mono", [])
             constructor_kwargs["font_mono"] = [
-                gr.themes.GoogleFont(name) if name not in ["ui-monospace", "Consolas", "monospace"] else name for name in mono_fonts
+                (gr.themes.GoogleFont(name) if name not in ["ui-monospace", "Consolas", "monospace"] else name) for name in mono_fonts
             ]
 
         theme_object = gr.themes.Default(**constructor_kwargs)
@@ -644,9 +689,10 @@ def generate_theme(
         css_str = theme_object._get_theme_css()
 
         def format_arg(val):
-            if isinstance(val, str): return f"'{val}'"
+            if isinstance(val, str):
+                return f"'{val}'"
             if isinstance(val, list):
-                font_list = [f"gr.themes.GoogleFont('{f.name}')" if isinstance(f, gr.themes.GoogleFont) else f"'{f}'" for f in val]
+                font_list = [(f"gr.themes.GoogleFont('{f.name}')" if isinstance(f, gr.themes.GoogleFont) else f"'{f}'") for f in val]
                 return f"[{', '.join(font_list)}]"
             return str(val)
 
@@ -659,16 +705,17 @@ def generate_theme(
 
     except Exception as e:
         import traceback
+
         traceback.print_exc()
-                
+
         error_msg = str(e)
         if "Auth" in error_msg or "Key" in error_msg or "401" in error_msg:
             detailed_msg = f"Authentication Error: The provided API Key for {llm_provider} is invalid or expired."
         elif "429" in error_msg or "Rate" in error_msg:
-            detailed_msg = f"Rate Limit Error: The AI provider is busy. Please wait a moment."
+            detailed_msg = "Rate Limit Error: The AI provider is busy. Please wait a moment."
         else:
             detailed_msg = f"System Error: {error_msg}"
-        
+
         raise gr.Error(detailed_msg)
 
 
@@ -695,11 +742,11 @@ def generate_ui_from_sketch(
     llm_provider: str = "sambanova",
     llm_model: str = "Llama-4-Maverick-17B-128E-Instruct",
     llm_api_key: str | None = None,
-    request: gr.Request = None
+    request: gr.Request = None,
 ):
     """
     Generates Python code for a Gradio UI from a sketch image.
-    
+
     Args:
         sketch_image (Image): The input sketch image.
         text_description (str): Optional text description.
@@ -715,10 +762,21 @@ def generate_ui_from_sketch(
     try:
         messages = [
             {"role": "system", "content": SYSTEM_PROMPT_SKETCH_EN},
-            {"role": "user", "content": f"Additional text description: {text_description if text_description else 'None'}"},
+            {
+                "role": "user",
+                "content": f"Additional text description: {text_description if text_description else 'None'}",
+            },
         ]
 
-        llm_output = LLMFactory.call(llm_provider=llm_provider, llm_model=llm_model, messages=messages, image=sketch_image, temperature=0.1, max_tokens=8192, api_key=llm_api_key)
+        llm_output = LLMFactory.call(
+            llm_provider=llm_provider,
+            llm_model=llm_model,
+            messages=messages,
+            image=sketch_image,
+            temperature=0.1,
+            max_tokens=8192,
+            api_key=llm_api_key,
+        )
 
         if isinstance(llm_output, str) and llm_output.startswith("Error:"):
             raise Exception(llm_output)
@@ -727,16 +785,17 @@ def generate_ui_from_sketch(
         return code.strip()
     except Exception as e:
         import traceback
+
         traceback.print_exc()
-                
+
         error_msg = str(e)
         if "Auth" in error_msg or "Key" in error_msg or "401" in error_msg:
             detailed_msg = f"Authentication Error: The provided API Key for {llm_provider} is invalid or expired."
         elif "429" in error_msg or "Rate" in error_msg:
-            detailed_msg = f"Rate Limit Error: The AI provider is busy. Please wait a moment."
+            detailed_msg = "Rate Limit Error: The AI provider is busy. Please wait a moment."
         else:
             detailed_msg = f"System Error: {error_msg}"
-        
+
         raise gr.Error(detailed_msg)
 
 
@@ -745,17 +804,24 @@ def generate_ui_from_sketch(
 
 # region HELPER FUNCTIONS
 def _validate_api_keys(llm_api_key, request):
-    USE_SERVER_KEYS = os.getenv("USE_SERVER_KEYS", "false").lower() in ("True", "true", "1", "yes")
+    USE_SERVER_KEYS = os.getenv("USE_SERVER_KEYS", "false").lower() in (
+        "True",
+        "true",
+        "1",
+        "yes",
+    )
     if not USE_SERVER_KEYS and request and request.headers.get("referer"):
         if not llm_api_key or not llm_api_key.strip():
             raise gr.Error("⚠️ LLM API Key Required! Please enter your own API Key to use this tool in the demo UI.")
-        
+
+
 def _check_env():
     if not os.getenv("GOOGLE_API_KEY"):
         print("WARNING: The GOOGLE_API_KEY environment variable was not set.")
     if not os.getenv("SAMBANOVA_API_KEY"):
         print("WARNING: The SAMBANOVA_API_KEY environment variable was not set.")
 
+
 def _inject_theme_helper_js():
     js_code = """
         function apply_gradio_theme(css) {
@@ -771,16 +837,26 @@ def _inject_theme_helper_js():
     """
     return {"js": js_code}
 
+
 def _add_interactive_inputs():
     with gr.Row():
         with gr.Column(variant="panel", scale=1):
             gr.Markdown("### Interactive Controls")
             radio = gr.Radio(["A", "B", "C"], label="Radio")
             drop = gr.Dropdown(["Option 1", "Option 2"], show_label=False)
-            drop_2 = gr.Dropdown(["A", "B", "C"], multiselect=True, value=["A"], label="Multiple Dropdown")
+            drop_2 = gr.Dropdown(
+                ["A", "B", "C"],
+                multiselect=True,
+                value=["A"],
+                label="Multiple Dropdown",
+            )
             check = gr.Checkbox(label="Checkbox")
         with gr.Column(variant="panel", scale=2):
-            gr.Image("https://gradio-static-files.s3.us-west-2.amazonaws.com/header-image.jpg", label="Image", height=320)
+            gr.Image(
+                "https://gradio-static-files.s3.us-west-2.amazonaws.com/header-image.jpg",
+                label="Image",
+                height=320,
+            )
             with gr.Row():
                 gr.Button("Primary", variant="primary")
                 gr.Button("Secondary")
@@ -790,18 +866,29 @@ def _add_interactive_inputs():
                 gr.Button("Stop", variant="stop", size="sm")
     return radio, drop, drop_2, check
 
+
 def _add_data_display_components():
     with gr.Row():
         gr.Dataframe(value=[[1, 2], [3, 4]], label="Dataframe"), gr.JSON(value={"a": 1}), gr.Label(value={"cat": 0.7}), gr.File()
 
+
 def _add_media_components():
     with gr.Row():
         (
             gr.ColorPicker(label="Color Picker"),
             gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4"),
-            gr.Gallery([("https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg", "lion")], height="200px"),
+            gr.Gallery(
+                [
+                    (
+                        "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
+                        "lion",
+                    )
+                ],
+                height="200px",
+            ),
         )
 
+
 def _add_chatbot_component():
     with gr.Row():
         with gr.Column(scale=2):
@@ -813,19 +900,35 @@ def _add_chatbot_component():
                 gr.Slider(label="Temperature"), gr.Checkbox(label="Streaming")
     return chatbot, msg_input, add_msg_btn
 
+
 def _create_example_app():
     with gr.Column(scale=3, elem_id="app"):
         with gr.Tabs():
             with gr.TabItem("Common Inputs"):
-                gr.Textbox(label="Text Box", info="A standard text field.", placeholder="Write something...")
-                gr.Interface(lambda x: x, "number", "textbox", title="Interface Component (Compact)", show_api=False)
+                gr.Textbox(
+                    label="Text Box",
+                    info="A standard text field.",
+                    placeholder="Write something...",
+                )
+                gr.Interface(
+                    lambda x: x,
+                    "number",
+                    "textbox",
+                    title="Interface Component (Compact)",
+                    show_api=False,
+                )
                 with gr.Row():
                     gr.Slider(label="Slider 1")
                     gr.Slider(label="Slider 2")
                     gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
                 radio, drop, drop_2, check = _add_interactive_inputs()
                 gr.Examples(
-                    examples=[["A", "Option 1", ["B"], True], ["B", "Option 2", ["A", "C"], False]], inputs=[radio, drop, drop_2, check], label="Input Examples"
+                    examples=[
+                        ["A", "Option 1", ["B"], True],
+                        ["B", "Option 2", ["A", "C"], False],
+                    ],
+                    inputs=[radio, drop, drop_2, check],
+                    label="Input Examples",
                 )
             with gr.TabItem("Data and Media"):
                 _add_data_display_components()
@@ -834,6 +937,7 @@ def _create_example_app():
                 chatbot, msg_input, add_msg_btn = _add_chatbot_component()
     return chatbot, msg_input, add_msg_btn
 
+
 def _create_gradio_lite_html(python_code: str) -> str:
     """
     Wraps the Python code in a Gradio-Lite HTML structure with an iframe.
@@ -841,7 +945,7 @@ def _create_gradio_lite_html(python_code: str) -> str:
     """
     # Escape any existing script tags to prevent injection issues inside srcdoc
     safe_code = python_code.replace("<", "&lt;").replace(">", "&gt;")
-    
+
     # To make sure demo.launch() is present for Lite to render
     if "demo.launch()" not in safe_code:
         safe_code += "\n\ndemo.launch()"
@@ -874,6 +978,8 @@ def _create_gradio_lite_html(python_code: str) -> str:
     </div>
     """
     return html_template
+
+
 # endregion
 
 # region MAIN LAYOUT DEFINITION
@@ -890,34 +996,34 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
 
     gr.Markdown("# 🤖 GitRepo Inspector (MCP Server) 🤖")
 
-    # --- GLOBAL CONFIGURATION ---
+    # GLOBAL CONFIGURATION
     with gr.Accordion("⚙️ Global Configuration (Configure tools here)", open=True):
         with gr.Row():
             global_github_token = gr.Textbox(
-                label="GitHub Token", 
+                label="GitHub Token",
                 type="password",
-                placeholder="Optional for public repos, required for higher rate limits."
+                placeholder="Optional for public repos, required for higher rate limits.",
             )
             global_api_key = gr.Textbox(
-                label="LLM API Key", 
+                label="LLM API Key",
                 type="password",
-                placeholder="Required for UI demo. (Matches selected Provider)"
+                placeholder="Required for UI demo. (Matches selected Provider)",
             )
         with gr.Row():
             global_provider = gr.Dropdown(
-                choices=list(AVAILABLE_ISSUE_MODELS_BY_PROVIDER.keys()), 
-                value="gemini", 
-                label="LLM Provider (For all tools)"
+                choices=list(AVAILABLE_ISSUE_MODELS_BY_PROVIDER.keys()),
+                value="gemini",
+                label="LLM Provider (For all tools)",
             )
             global_model = gr.Dropdown(
-                choices=AVAILABLE_ISSUE_MODELS_BY_PROVIDER["gemini"], 
-                value=AVAILABLE_ISSUE_MODELS_BY_PROVIDER["gemini"][0], 
-                label="Main Model (Text/Chat)", 
-                interactive=True
+                choices=AVAILABLE_ISSUE_MODELS_BY_PROVIDER["gemini"],
+                value=AVAILABLE_ISSUE_MODELS_BY_PROVIDER["gemini"][0],
+                label="Main Model (Text/Chat)",
+                interactive=True,
             )
-        with gr.Row():        
-            models_info_output = gr.JSON(label="Available Models Registry", visible=False) 
-            get_models_btn = gr.Button("Refresh Models Info", visible=False) 
+        with gr.Row():
+            models_info_output = gr.JSON(label="Available Models Registry", visible=False)
+            get_models_btn = gr.Button("Refresh Models Info", visible=False)
     # -----------------------------
 
     with gr.Tabs():
@@ -931,7 +1037,10 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
         with gr.TabItem("2. Issue Analyzer"):
             gr.Markdown("### 🔍 Analyze a GitHub Issue")
             with gr.Row():
-                issue_repo_url = gr.Textbox(label="GitHub Repository URL", value="https://github.com/gradio-app/gradio")
+                issue_repo_url = gr.Textbox(
+                    label="GitHub Repository URL",
+                    value="https://github.com/gradio-app/gradio",
+                )
                 issue_number_input = gr.Number(label="Issue Number", precision=0)
             issue_analyze_button = gr.Button("Analyze Issue 🕵️", variant="primary")
             issue_report_output = gr.HTML(label="Analysis Report")
@@ -953,28 +1062,36 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
         with gr.TabItem("5. Action Runner (Reply & Close)"):
             gr.Markdown("### ⚡ Action Runner")
             gr.Markdown("This tool allows the Agent (or you) to take action: post comments and close resolved issues.")
-            
+
             with gr.Row():
                 action_repo_url = gr.Textbox(label="Repository URL", value="https://github.com/gradio-app/gradio")
                 action_issue_number = gr.Number(label="Issue Number", precision=0)
-            
+
             action_comment = gr.Textbox(
-                label="Comment Body (Markdown supported)", 
-                lines=5, 
-                placeholder="Ex: This issue is resolved in PR #123. Closing now."
+                label="Comment Body (Markdown supported)",
+                lines=5,
+                placeholder="Ex: This issue is resolved in PR #123. Closing now.",
+            )
+
+            action_close_checkbox = gr.Checkbox(
+                label="Close this issue?",
+                value=False,
+                info="Check to verify the fix and close the issue on GitHub.",
             )
-            
-            action_close_checkbox = gr.Checkbox(label="Close this issue?", value=False, info="Check to verify the fix and close the issue on GitHub.")
-          
-            action_button = gr.Button("Execute Action 🚀", variant="stop") # Variant stop (vermelho) para indicar cuidado
+
+            action_button = gr.Button("Execute Action 🚀", variant="stop")  # Variant stop (vermelho) para indicar cuidado
             action_output = gr.Markdown(label="Execution Result")
-            
+
         with gr.TabItem("BONUS 1 - Theme Generator"):
             gr.Markdown("### 🖌️ Create a Theme with Natural Language")
             with gr.Row():
                 with gr.Column(scale=1, min_width=450):
                     with gr.Group():
-                        theme_prompt_input = gr.Textbox(label="Describe your theme", placeholder="Ex: a dark theme with purple tones...", lines=4)
+                        theme_prompt_input = gr.Textbox(
+                            label="Describe your theme",
+                            placeholder="Ex: a dark theme with purple tones...",
+                            lines=4,
+                        )
                         theme_generate_button = gr.Button("Generate Theme ✨", variant="primary")
                     status_output = gr.Textbox(label="Status", interactive=False)
                     with gr.Tabs():
@@ -1000,7 +1117,11 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
                 )
             with gr.Row():
                 with gr.Column(scale=1):
-                    sketch_input = gr.Image(type="pil", label="Upload the sketch", sources=["upload", "clipboard"])
+                    sketch_input = gr.Image(
+                        type="pil",
+                        label="Upload the sketch",
+                        sources=["upload", "clipboard"],
+                    )
                     text_desc_input = gr.Textbox(label="Additional Description (Optional)")
                     sketch_generate_button = gr.Button("1. Generate Code", variant="secondary")
                 with gr.Column(scale=2):
@@ -1010,45 +1131,49 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
                         with gr.TabItem("👀 Live Preview"):
                             sketch_preview_output = gr.HTML(label="Gradio-Lite Preview")
 
-    # --- EVENTS & LOGIC ---
+    # EVENTS & LOGIC
 
     # Unified Model Updater: Updates BOTH the Text Model (Global) and Vision Model (Sketch) dropdowns
     get_models_btn.click(
         fn=get_available_models,
         inputs=None,
         outputs=[models_info_output],
-        api_name="get_available_models"
+        api_name="get_available_models",
     )
+
     def update_all_model_choices(provider):
         text_models = AVAILABLE_ISSUE_MODELS_BY_PROVIDER.get(provider, [])
         vision_models = AVAILABLE_SKETCH_MODELS_BY_PROVIDER.get(provider, [])
         return (
             gr.update(choices=text_models, value=text_models[0] if text_models else None),
-            gr.update(choices=vision_models, value=vision_models[0] if vision_models else None)
+            gr.update(choices=vision_models, value=vision_models[0] if vision_models else None),
         )
 
     global_provider.change(
         fn=update_all_model_choices,
         inputs=[global_provider],
-        outputs=[global_model, sketch_model_dropdown], # Update both dropdowns
-        show_api=False
+        outputs=[global_model, sketch_model_dropdown],  # Update both dropdowns
+        show_api=False,
     )
 
     # Tool 1: Sync
     sync_button.click(
-        fn=sync_repository, 
-        inputs=[sync_repo_url, global_github_token], 
-        outputs=[sync_status_output], 
-        api_name="sync_repository"
+        fn=sync_repository,
+        inputs=[sync_repo_url, global_github_token],
+        outputs=[sync_status_output],
+        api_name="sync_repository",
     )
 
     # Tool 2: Analyzer
     issue_analyze_button.click(
         fn=analyze_github_issue,
         inputs=[
-            issue_repo_url, issue_number_input, 
-            global_provider, global_model, 
-            global_github_token, global_api_key
+            issue_repo_url,
+            issue_number_input,
+            global_provider,
+            global_model,
+            global_github_token,
+            global_api_key,
         ],
         outputs=[issue_report_output],
         api_name="analyze_github_issue",
@@ -1058,9 +1183,12 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
     dup_find_button.click(
         fn=find_duplicate_issues,
         inputs=[
-            dup_repo_url, dup_issue_number, 
-            global_provider, global_model, 
-            global_github_token, global_api_key
+            dup_repo_url,
+            dup_issue_number,
+            global_provider,
+            global_model,
+            global_github_token,
+            global_api_key,
         ],
         outputs=[dup_report_output],
         api_name="find_duplicate_issues",
@@ -1069,63 +1197,66 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
     # Tool 4: Prioritizer
     prio_run_button.click(
         fn=prioritize_open_issues,
-        inputs=[
-            prio_repo_url, 
-            global_provider, global_model, 
-            global_api_key
-        ],
+        inputs=[prio_repo_url, global_provider, global_model, global_api_key],
         outputs=[prio_report_output],
-        api_name="prioritize_open_issues"
+        api_name="prioritize_open_issues",
     )
 
     # Tool 5: Action Runner
     action_button.click(
         fn=reply_and_close_issue,
         inputs=[
-            action_repo_url, 
-            action_issue_number, 
-            action_comment, 
-            action_close_checkbox, 
-            global_github_token
+            action_repo_url,
+            action_issue_number,
+            action_comment,
+            action_close_checkbox,
+            global_github_token,
         ],
         outputs=[action_output],
-        api_name="reply_and_close_issue"
+        api_name="reply_and_close_issue",
     )
-    
+
     # Bonus 1: Theme Gen
     theme_generate_button.click(
         fn=generate_theme,
-        inputs=[
-            theme_prompt_input, 
-            global_provider, global_model, 
-            global_api_key
+        inputs=[theme_prompt_input, global_provider, global_model, global_api_key],
+        outputs=[
+            generated_theme_state,
+            python_code_output,
+            status_output,
+            css_data_transport,
         ],
-        outputs=[generated_theme_state, python_code_output, status_output, css_data_transport],
     ).then(
         fn=None,
         inputs=[css_data_transport],
         outputs=None,
         js="(css) => { const action = () => apply_gradio_theme(css); if (typeof apply_gradio_theme === 'function') { action(); } else { document.addEventListener('theme-helper-injected', action, { once: true }); } }",
     )
-    upload_button.click(fn=handle_upload, inputs=[generated_theme_state, hub_theme_name, hub_hf_token], outputs=[upload_status])
+    upload_button.click(
+        fn=handle_upload,
+        inputs=[generated_theme_state, hub_theme_name, hub_hf_token],
+        outputs=[upload_status],
+    )
 
     # Bonus 2: Sketch (Uses Global Provider & Key, but LOCAL Vision Model)
     generate_event = sketch_generate_button.click(
         fn=generate_ui_from_sketch,
         inputs=[
-            sketch_input, text_desc_input, 
-            global_provider, sketch_model_dropdown, 
-            global_api_key
+            sketch_input,
+            text_desc_input,
+            global_provider,
+            sketch_model_dropdown,
+            global_api_key,
         ],
         outputs=[sketch_code_output],
-    )    
+    )
     generate_event.success(
         fn=_create_gradio_lite_html,
         inputs=[sketch_code_output],
         outputs=[sketch_preview_output],
-        show_api=False
+        show_api=False,
     )
-    
+
     def add_message_to_chat(history, message):
         """
         A simple function to add a user message to the chat history
@@ -1142,15 +1273,22 @@ with gr.Blocks(css=css, title="GitRepo Inspector (MCP Server)") as app:
         time.sleep(0.5)
         history.append({"role": "assistant", "content": "Thank you for your message!"})
         return history
-    
+
     # Chat Example Logic
-    add_msg_btn_comp.click(fn=add_message_to_chat, inputs=[chatbot_comp, msg_input_comp], outputs=[chatbot_comp], show_api=False).then(fn=lambda: "", outputs=[msg_input_comp], show_api=False)
-    
+    add_msg_btn_comp.click(
+        fn=add_message_to_chat,
+        inputs=[chatbot_comp, msg_input_comp],
+        outputs=[chatbot_comp],
+        show_api=False,
+    ).then(fn=lambda: "", outputs=[msg_input_comp], show_api=False)
+
     # Init
-    app.load(fn=initialize_database, inputs=None, outputs=None, show_api=False).then(fn=_inject_theme_helper_js, inputs=None, outputs=[html_injector], show_api=False)
+    app.load(fn=initialize_database, inputs=None, outputs=None, show_api=False).then(
+        fn=_inject_theme_helper_js, inputs=None, outputs=[html_injector], show_api=False
+    )
 
 # endregion
 if __name__ == "__main__":
     _check_env()
     app.allowed_paths = ["."]
-    app.launch(mcp_server=True, server_port=7860)
+    app.launch(mcp_server=True, server_port=7860)

config/clients/gemini_cli.py

@@ -14,6 +14,7 @@ BASE_URL = os.getenv("GEMINI_BASE_URL", "https://generativelanguage.googleapis.c
 DEFAULT_TEXT_MODEL = "gemini-2.0-flash"  # Default text model
 VISION_MODEL = "gemini-2.5-flash-lite"  # Model with vision capability
 
+
 def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
     """
     Returns the provided key if valid, otherwise falls back to the environment variable.
@@ -110,9 +111,18 @@ def call_api(
 
     # Add generation configuration to the payload
     payload["safetySettings"] = [
-        {"category": f"HARM_CATEGORY_{cat}", "threshold": "BLOCK_NONE"} for cat in ["SEXUALLY_EXPLICIT", "HATE_SPEECH", "HARASSMENT", "DANGEROUS_CONTENT"]
+        {"category": f"HARM_CATEGORY_{cat}", "threshold": "BLOCK_NONE"}
+        for cat in [
+            "SEXUALLY_EXPLICIT",
+            "HATE_SPEECH",
+            "HARASSMENT",
+            "DANGEROUS_CONTENT",
+        ]
     ]
-    payload["generationConfig"] = {"temperature": temperature, "maxOutputTokens": max_tokens}
+    payload["generationConfig"] = {
+        "temperature": temperature,
+        "maxOutputTokens": max_tokens,
+    }
 
     stream_param = "streamGenerateContent" if stream else "generateContent"
     request_url = f"{BASE_URL}{model_id}:{stream_param}?key={final_api_key}"
@@ -123,10 +133,10 @@ def call_api(
         if response.status_code != 200:
             try:
                 error_details = response.json()
-                error_msg = error_details.get('error', {}).get('message', response.text)
-            except:
+                error_msg = error_details.get("error", {}).get("message", response.text)
+            except json.JSONDecodeError:
                 error_msg = response.text
-            
+
             if response.status_code in [400, 401, 403]:
                 raise ValueError(f"Gemini Auth Error: {error_msg}")
             else:

config/clients/nebius_cli.py

@@ -3,15 +3,17 @@ import os
 from io import BytesIO
 from typing import Dict, List, Optional
 
-from PIL import Image
 from openai import APIConnectionError, AuthenticationError, OpenAI, RateLimitError
+from PIL import Image
+
 
 # Configuration
 API_KEY_ENV_VAR = "NEBIUS_API_KEY"
-BASE_URL = os.getenv("NEBIUS_BASE_URL", "https://api.studio.nebius.ai/v1/") 
+BASE_URL = os.getenv("NEBIUS_BASE_URL", "https://api.studio.nebius.ai/v1/")
 DEFAULT_TEXT_MODEL = "meta-llama/Meta-Llama-3.1-70B-Instruct"
 DEFAULT_VISION_MODEL = "meta-llama/Llama-3.2-11B-Vision-Instruct"
 
+
 def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
     """
     Returns the provided key if valid, otherwise falls back to the environment variable.
@@ -20,13 +22,14 @@ def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
         return provided_key.strip()
     return os.getenv(API_KEY_ENV_VAR)
 
+
 def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Optional[Image.Image] = None, api_key: Optional[str] = None, **kwargs):
     """
     Calls the Nebius AI Studio API (OpenAI-compatible).
     """
     # 1. Resolve API Key
     final_api_key = _get_api_key(api_key)
-    
+
     if not final_api_key:
         return "Error: Authentication required. Please provide a Nebius API Key."
 
@@ -38,28 +41,25 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
 
     # 3. Prepare Messages
     final_messages = []
-    
+
     if image:
         print("Making a VISION call to Nebius.")
         buffered = BytesIO()
         image.save(buffered, format="PNG")
         image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        
+
         user_text = "Analyze this image."
         for msg in messages:
             if msg["role"] == "user":
                 user_text = msg["content"]
                 break
-        
-        content = [
-            {"type": "text", "text": user_text},
-            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}
-        ]
-        
+
+        content = [{"type": "text", "text": user_text}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}]
+
         for msg in messages:
             if msg["role"] == "system":
                 final_messages.append(msg)
-                
+
         final_messages.append({"role": "user", "content": content})
         final_model = model_name or DEFAULT_VISION_MODEL
     else:
@@ -70,11 +70,7 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
     # 4. Call API
     try:
         print(f"Calling Nebius API with model: {final_model}")
-        completion = client.chat.completions.create(
-            model=final_model,
-            messages=final_messages,
-            **kwargs
-        )
+        completion = client.chat.completions.create(model=final_model, messages=final_messages, **kwargs)
         return completion.choices[0].message.content
     except AuthenticationError as e:
         raise ValueError(f"Authentication Failed: {e.body.get('message', str(e)) if e.body else str(e)}")
@@ -83,4 +79,4 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
     except APIConnectionError:
         raise ConnectionError("Failed to connect to API. Check your internet.")
     except Exception as e:
-        raise RuntimeError(f"API Error: {str(e)}")
+        raise RuntimeError(f"API Error: {str(e)}")

config/clients/openai_cli.py

@@ -3,14 +3,16 @@ import os
 from io import BytesIO
 from typing import Dict, List, Optional
 
-from PIL import Image
 from openai import APIConnectionError, AuthenticationError, OpenAI, RateLimitError
+from PIL import Image
+
 
 # Configuration
 API_KEY_ENV_VAR = "OPENAI_API_KEY"
 DEFAULT_TEXT_MODEL = "gpt-4o-mini"
 DEFAULT_VISION_MODEL = "gpt-4o"
 
+
 def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
     """
     Returns the provided key if valid, otherwise falls back to the environment variable.
@@ -19,13 +21,14 @@ def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
         return provided_key.strip()
     return os.getenv(API_KEY_ENV_VAR)
 
+
 def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Optional[Image.Image] = None, api_key: Optional[str] = None, **kwargs):
     """
     Calls the OpenAI API (GPT models).
     """
     # 1. Resolve API Key
     final_api_key = _get_api_key(api_key)
-    
+
     if not final_api_key:
         return "Error: Authentication required. Please provide an OpenAI API Key."
 
@@ -37,32 +40,29 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
 
     # 3. Prepare Messages & Payload
     final_messages = []
-    
+
     # Handle Vision (Multimodal)
     if image:
         print("Making a VISION call to OpenAI.")
         buffered = BytesIO()
         image.save(buffered, format="PNG")
         image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        
+
         # Extract user text from messages to combine with image
         user_text = "Analyze this image."
         for msg in messages:
             if msg["role"] == "user":
                 user_text = msg["content"]
                 break
-        
+
         # Format strictly for OpenAI Vision
-        content = [
-            {"type": "text", "text": user_text},
-            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}
-        ]
-        
+        content = [{"type": "text", "text": user_text}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}]
+
         # Add system prompt if exists
         for msg in messages:
             if msg["role"] == "system":
                 final_messages.append(msg)
-                
+
         final_messages.append({"role": "user", "content": content})
         final_model = model_name or DEFAULT_VISION_MODEL
 
@@ -75,11 +75,7 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
     # 4. Call API
     try:
         print(f"Calling OpenAI API with model: {final_model}")
-        completion = client.chat.completions.create(
-            model=final_model,
-            messages=final_messages,
-            **kwargs
-        )
+        completion = client.chat.completions.create(model=final_model, messages=final_messages, **kwargs)
         return completion.choices[0].message.content
     except AuthenticationError as e:
         raise ValueError(f"Authentication Failed: {e.body.get('message', str(e)) if e.body else str(e)}")
@@ -88,4 +84,4 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
     except APIConnectionError:
         raise ConnectionError("Failed to connect to API. Check your internet.")
     except Exception as e:
-        raise RuntimeError(f"API Error: {str(e)}")
+        raise RuntimeError(f"API Error: {str(e)}")

config/clients/sambanova_cli.py

@@ -3,8 +3,8 @@ import os
 from io import BytesIO
 from typing import Dict, List, Optional
 
-from PIL import Image
 from openai import APIConnectionError, AuthenticationError, RateLimitError
+from PIL import Image
 from sambanova import SambaNova
 
 
@@ -14,6 +14,7 @@ BASE_URL = os.getenv("SAMBANOVA_BASE_URL", "https://api.sambanova.ai/v1")
 DEFAULT_TEXT_MODEL = "Meta-Llama-3.1-8B-Instruct"
 DEFAULT_VISION_MODEL = "Llama-4-Maverick-17B-128E-Instruct"
 
+
 def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
     """
     Returns the provided key if valid, otherwise falls back to the environment variable.
@@ -22,13 +23,20 @@ def _get_api_key(provided_key: Optional[str] = None) -> Optional[str]:
         return provided_key.strip()
     return os.getenv(API_KEY_ENV_VAR)
 
-def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Optional[Image.Image] = None, api_key: Optional[str] = None, **kwargs):
+
+def call_api(
+    messages: List[Dict],
+    model_name: Optional[str] = None,
+    image: Optional[Image.Image] = None,
+    api_key: Optional[str] = None,
+    **kwargs,
+):
     """
     Calls the SambaNova API, handling text or vision requests.
     """
-   
+
     final_api_key = _get_api_key(api_key)
-    
+
     if not final_api_key:
         return "Error: Authentication required. Please provide a SambaNova API Key."
 
@@ -47,10 +55,18 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
 
         # Extracts the prompt text
         # The SambaNova vision API seems to expect a simple text prompt
-        user_prompt_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
+        user_prompt_text = "\n".join(
+            [f"{msg['role']}: {msg['content']}" for msg in messages]
+        )
 
         # Assembles the message content for vision
-        message_content = [{"type": "text", "text": user_prompt_text}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}]
+        message_content = [
+            {"type": "text", "text": user_prompt_text},
+            {
+                "type": "image_url",
+                "image_url": {"url": f"data:image/png;base64,{image_base64}"},
+            },
+        ]
 
         final_messages = [{"role": "user", "content": message_content}]
         final_model = model_name or DEFAULT_VISION_MODEL  # type: ignore
@@ -64,13 +80,13 @@ def call_api(messages: List[Dict], model_name: Optional[str] = None, image: Opti
     try:
         print(f"Calling SambaNova API with model: {final_model}")
         completion = client.chat.completions.create(
-            model=final_model,
-            messages=final_messages,
-            **kwargs
+            model=final_model, messages=final_messages, **kwargs
         )
         return completion.choices[0].message.content
     except AuthenticationError as e:
-        raise ValueError(f"Authentication Failed: {e.body.get('message', str(e)) if e.body else str(e)}")
+        raise ValueError(
+            f"Authentication Failed: {e.body.get('message', str(e)) if e.body else str(e)}"
+        )
     except RateLimitError:
         raise RuntimeError("Rate Limit Exceeded (429). Please try again later.")
     except APIConnectionError:

config/constants.py

@@ -9,23 +9,30 @@ PROVIDER_LOGOS = {
 }
 
 AVAILABLE_SKETCH_MODELS_BY_PROVIDER = {
-    "gemini": ["gemini-2.5-flash", "gemini-2.5-flash-lite"], 
+    "gemini": ["gemini-2.5-flash", "gemini-2.5-flash-lite"],
     "sambanova": ["Llama-4-Maverick-17B-128E-Instruct"],
     "openai": ["gpt-4o-mini", "gpt-4o"],
-    "nebius": [ 
-        "Qwen/Qwen2.5-VL-72B-Instruct",         
-    ]
+    "nebius": [
+        "Qwen/Qwen2.5-VL-72B-Instruct",
+    ],
 }
 AVAILABLE_ISSUE_MODELS_BY_PROVIDER = {
     "gemini": ["gemini-2.0-flash"],
-    "sambanova": ["DeepSeek-R1", "DeepSeek-V3-0324", "DeepSeek-V3.1", "Meta-Llama-3.1-8B-Instruct", "Meta-Llama-3.3-70B-Instruct", "gpt-oss-120b"],     
+    "sambanova": [
+        "DeepSeek-R1",
+        "DeepSeek-V3-0324",
+        "DeepSeek-V3.1",
+        "Meta-Llama-3.1-8B-Instruct",
+        "Meta-Llama-3.3-70B-Instruct",
+        "gpt-oss-120b",
+    ],
     "openai": ["gpt-4o-mini", "gpt-4o"],
     "nebius": [
-        "deepseek-ai/DeepSeek-R1-0528",        
+        "deepseek-ai/DeepSeek-R1-0528",
         "meta-llama/Llama-3.3-70B-Instruct",
         "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
-        "meta-llama/Meta-Llama-3.1-8B-Instruct-fast"
-    ]
+        "meta-llama/Meta-Llama-3.1-8B-Instruct-fast",
+    ],
 }
 
 # Prompt template
@@ -105,9 +112,9 @@ You are an expert Gradio UI developer. Your task is to analyze a sketch and gene
 2.  **NO EXTERNAL LIBRARIES:** Only use the `gradio` library.
 3.  **NO FUNCTIONS OR CLASSES:** Start directly with `import gradio as gr` then `with gr.Blocks() as demo:`.
 4.  **NO `demo.launch()`:** Do NOT include the `demo.launch()` call.
-5.  **FORMATTING:** 
+5.  **FORMATTING:**
     - Use standard Python indentation (4 spaces).
-    - **DO NOT** put multiple statements on one line using semicolons (`;`). 
+    - **DO NOT** put multiple statements on one line using semicolons (`;`).
     - Each component must be on its own line.
 6.  **STRINGS:** Do NOT use triple quotes (`"""
     "..."

config/database.py

@@ -1,16 +1,19 @@
 import os
+
 import psycopg2
 from psycopg2.extras import DictCursor
 
+
 def connect():
     """Establishes a connection to the PostgreSQL database using the DATABASE_URL."""
     database_url = os.getenv("DATABASE_URL")
     if not database_url:
         raise ValueError("DATABASE_URL environment variable is not set.")
-    
+
     conn = psycopg2.connect(database_url, cursor_factory=DictCursor)
     return conn
 
+
 def initialize_database():
     """Creates the cache tables in PostgreSQL if they don't exist."""
     conn = connect()

config/database_sqlite.py

@@ -13,7 +13,7 @@ def connect():
 def initialize_database():
     """Creates the cache tables if they don't exist."""
     conn = connect()
-    cursor = conn.cursor()    
+    cursor = conn.cursor()
     # Table for both Issues and Pull Requests
     cursor.execute("""
         CREATE TABLE IF NOT EXISTS items (

config/model_factory.py

@@ -5,13 +5,15 @@ It abstracts the specific client implementations and provides a unified interfac
 """
 
 from typing import Any, Callable, Dict, List, Optional
+
 from PIL import Image
 
+
 # Step 1: Import the client-specific API call functions
 # It is assumed that there is a 'clients' directory with modules such as gemini.py, sambanova.py, etc.
 # Each module should expose a `call_api` function.
 try:
-    from .clients import gemini_cli, sambanova_cli, openai_cli, nebius_cli
+    from .clients import gemini_cli, nebius_cli, openai_cli, sambanova_cli
 except ImportError:
     print("Warning: Could not import all LLM clients. Please ensure clients/gemini.py, clients/sambanova.py, etc. exist.")
 
@@ -62,7 +64,14 @@ class LLMFactory:
         return PROVIDER_REGISTRY[provider_key]
 
     @staticmethod
-    def call(llm_provider: str, llm_model: Optional[str] = None, messages: List[Dict] = [], image: Optional[Image.Image] = None, api_key: Optional[str] = None, **kwargs: Any) -> str:
+    def call(
+        llm_provider: str,
+        llm_model: Optional[str] = None,
+        messages: List[Dict] = [],
+        image: Optional[Image.Image] = None,
+        api_key: Optional[str] = None,
+        **kwargs: Any,
+    ) -> str:
         """
         The main factory method. It routes the request to the correct provider client.
         This method is designed to be the single point of contact for all LLM calls.
@@ -80,7 +89,7 @@ class LLMFactory:
             str: The text response from the LLM.
         """
         print(f"LLM Factory: Routing call to provider '{llm_provider}' with model '{llm_model or 'default'}'.")
-    
+
         # Get the correct API function from the registry
         api_call_function = LLMFactory.get_provider(llm_provider)
 
@@ -96,5 +105,3 @@ class LLMFactory:
             return str(result)
 
         return result
-
-    

requirements.txt

@@ -5,5 +5,4 @@ requests
 sambanova
 markdown
 psycopg2-binary
-langchain-google-genai
 openai