Delete app.py

app.py

@@ -1,147 +0,0 @@
-import gradio as gr
-import os
-from openai import OpenAI
-import base64
-from PIL import Image
-import io
-
-# OpenRouter API key
-OPENROUTER_API_KEY = "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"
-
-# Available models
-MODELS = {
-    "Mistral Small": "mistralai/mistral-small-3.2-24b-instruct:free",
-    "Kimi Vision": "moonshotai/kimi-vl-a3b-thinking:free",
-    "Gemini Pro": "google/gemini-2.5-pro-exp-03-25",
-    "Qwen VL": "qwen/qwen2.5-vl-32b-instruct:free",
-    "Mistral 3.1": "mistralai/mistral-small-3.1-24b-instruct:free",
-    "Gemma": "google/gemma-3-4b-it:free",
-    "Llama 3.2 Vision": "meta-llama/llama-3.2-11b-vision-instruct:free",
-}
-
-def image_to_base64(image):
-    """Convert an image to base64 encoding"""
-    # If image is a file path
-    if isinstance(image, str):
-        with open(image, "rb") as img_file:
-            return base64.b64encode(img_file.read()).decode()
-    
-    # If image is already a PIL Image
-    buffered = io.BytesIO()
-    image.save(buffered, format="JPEG")
-    return base64.b64encode(buffered.getvalue()).decode()
-
-def analyze_image(image, prompt, model_name):
-    """Analyze an image using the selected OpenRouter model"""
-    try:
-        # Initialize OpenAI client with OpenRouter base URL
-        client = OpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=OPENROUTER_API_KEY,
-        )
-        
-        # Convert image to base64
-        img_base64 = image_to_base64(image)
-        
-        # Create the completion request
-        completion = client.chat.completions.create(
-            extra_headers={
-                "HTTP-Referer": "https://gradio-openrouter-interface.com",
-                "X-Title": "Gradio OpenRouter Interface",
-            },
-            model=MODELS[model_name],
-            messages=[
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": prompt
-                        },
-                        {
-                            "type": "image_url",
-                            "image_url": {
-                                "url": f"data:image/jpeg;base64,{img_base64}"
-                            }
-                        }
-                    ]
-                }
-            ]
-        )
-        
-        # Return the model's response
-        return completion.choices[0].message.content
-    
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Create the Gradio interface
-with gr.Blocks(title="OpenRouter AI Vision Interface", css="style.css", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🔍 OpenRouter AI Vision Interface
-        
-        Upload an image and ask a question about it. The AI will analyze the image and respond.
-        
-        *Powered by OpenRouter API with multiple vision-language models*
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            # Input components with custom styling
-            with gr.Box(elem_classes=["input-container"]):
-                image_input = gr.Image(type="pil", label="Upload Image", elem_classes=["image-upload-container"])
-                prompt_input = gr.Textbox(label="Your Question", placeholder="What is in this image?", value="What is in this image?")
-                model_dropdown = gr.Dropdown(
-                    choices=list(MODELS.keys()), 
-                    value="Mistral Small", 
-                    label="Select AI Model",
-                    info="Choose from different vision-language models"
-                )
-                submit_button = gr.Button("Analyze Image", variant="primary")
-        
-        with gr.Column():
-            # Output component with custom styling
-            with gr.Box(elem_classes=["output-container"]):
-                output_text = gr.Textbox(label="AI Response", lines=12)
-                
-    gr.Markdown(
-        """
-        ### Available Models
-        - **Mistral Small**: Powerful vision-language model from Mistral AI
-        - **Kimi Vision**: Specialized vision model from Moonshot AI
-        - **Gemini Pro**: Google's advanced multimodal model
-        - **Qwen VL**: Alibaba's vision-language model
-        - **Mistral 3.1**: Earlier version of Mistral's vision model
-        - **Gemma**: Google's lightweight vision model
-        - **Llama 3.2 Vision**: Meta's vision-enabled large language model
-        """
-    )
-    
-    # Set up the submit action
-    submit_button.click(
-        fn=analyze_image,
-        inputs=[image_input, prompt_input, model_dropdown],
-        outputs=output_text
-    )
-    
-    # Add example
-    gr.Examples(
-        examples=[
-            ["examples/nature.jpg", "What is in this image?", "Mistral Small"],
-            ["examples/nature.jpg", "Describe this scene in detail", "Kimi Vision"],
-        ],
-        inputs=[image_input, prompt_input, model_dropdown],
-    )
-
-# Create examples directory if it doesn't exist
-os.makedirs("examples", exist_ok=True)
-
-# For Hugging Face Spaces compatibility
-if __name__ == "__main__":
-    # Launch the interface
-    demo.launch(share=True)
-else:
-    # For Hugging Face Spaces, we need to expose the app
-    app = demo.launch(share=False, show_api=False)