Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+import os
+
+# Load API Token from environment variable
+API_TOKEN = os.getenv("HF_API_TOKEN")  # Ensure you've set this environment variable
+
+# Hugging Face Inference API URL
+API_URL = "https://api-inference.huggingface.co/models/enhanceaiteam/Flux-uncensored"
+
+# Function to call Hugging Face API and get the generated image
+def generate_image(prompt):
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    data = {"inputs": prompt}
+    
+    response = requests.post(API_URL, headers=headers, json=data)
+    
+    if response.status_code == 200:
+        image_bytes = BytesIO(response.content)
+        image = Image.open(image_bytes)
+        return image
+    else:
+        return f"Error: {response.status_code}, {response.text}"
+
+# Create Gradio interface
+def create_ui():
+    with gr.Blocks() as ui:
+        gr.Markdown("## Flux Uncensored - Text to Image Generator")
+        
+        with gr.Row():
+            prompt_input = gr.Textbox(label="Enter a Prompt", placeholder="Describe the image you want to generate", lines=3)
+            generate_button = gr.Button("Generate Image")
+        
+        with gr.Row():
+            output_image = gr.Image(label="Generated Image")
+        
+        # Link the button to the function
+        generate_button.click(fn=generate_image, inputs=prompt_input, outputs=output_image)
+    
+    return ui
+
+# Run the interface
+if __name__ == "__main__":
+    create_ui().launch()