Update app.py

app.py

@@ -1,5 +1,3 @@
-# prompt: currently the image generation function starts after user give a single letter. but for user friendly reason it should start to process after user gives his entrie input. here generate button is big make it small
-
 from transformers import pipeline
 import gradio as gr
 # from diffusers import DiffusionPipeline
@@ -13,9 +11,13 @@ from PIL import Image
 
 # Load models
 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-dra-en")
-summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+# summarizer = pipeline("summarization", model="Falconsai/text_summarization")
 # image_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16).to("cpu")
 
+# for summarizer api
+SUMMARIZER_API_URL = "https://api.groq.com/openai/v1/chat/completions"
+summarizer_headers = {"Authorization": f"Bearer {os.getenv('GROQ_API_TOKEN')}",
+                     "Content-Type": "application/json"}
 
 
 # for image api
@@ -37,8 +39,28 @@ def translate_tamil_to_english(text):
 # Summarize English Paragraph
 def summarize_english_text(paragraph):
     time.sleep(2)
-    summary = summarizer(paragraph, max_length=100, min_length=25, do_sample=False)
-    return summary[0]['summary_text']
+    # Request payload
+    payload = {
+        "model": "mixtral-8x7b-32768",
+        "messages": [
+            {"role": "system", "content": "Create a summary of below paragraph in 30 words max"},
+            {"role": "user", "content": paragraph}
+        ],
+        "max_tokens": 100  # number of words in the output.
+    }
+
+    # Send POST request to Groq API
+    response = requests.post(SUMMARIZER_API_URL, json=payload, headers=summarizer_headers)
+
+    # Check if the request was successful
+    if response.status_code == 200:
+        # Parse the JSON response
+        result = response.json()
+        # Extract and print the generated text
+        generated_text = result['choices'][0]['message']['content']
+        return generated_text
+    else:
+        return f"Error: {response.status_code}, {response.text}"
 
 
 # Generate image from English text