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app.py
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import gradio as gr
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import os
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# Simple chat function - replace with actual AI model integration
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def chat(message, history):
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"""
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Chat function that processes user messages.
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Replace this with actual AI model calls (e.g., Gemini, OpenAI, HuggingFace models).
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"""
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# Check if user wants to generate an image
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image_keywords = ["generate", "create", "draw", "make", "show me"]
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is_image_request = any(keyword in message.lower() for keyword in image_keywords) and \
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any(word in message.lower() for word in ["image", "picture", "photo", "art", "drawing"])
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if is_image_request:
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# Placeholder for image generation
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# In production, integrate with DALL-E, Stable Diffusion, etc.
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response = f"🎨 I would generate an image for: '{message}'\n\n"
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response += "To enable image generation, integrate with:\n"
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response += "- OpenAI DALL-E API\n"
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response += "- Stable Diffusion (HuggingFace)\n"
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response += "- Google Imagen\n\n"
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response += "Add your API keys in Space secrets and update this function."
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else:
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# Placeholder for text chat
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# In production, integrate with Gemini, GPT-4, Llama, etc.
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response = f"You said: {message}\n\n"
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response += "This is a placeholder response. To enable AI chat, integrate with:\n"
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response += "- Google Gemini API\n"
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response += "- OpenAI GPT-4 API\n"
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response += "- HuggingFace models (Llama, Mistral, etc.)\n\n"
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response += "Add your API keys in Space secrets and update the chat function."
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return response
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# Create Gradio Chat Interface
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demo = gr.ChatInterface(
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fn=chat,
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title="🤖 AI Chat Assistant",
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description="Chat with AI - Ask questions or request image generation!",
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examples=[
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"Hello! How are you?",
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"Generate an image of a red sports car",
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"What is machine learning?",
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"Create a picture of a sunset over mountains"
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]
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)
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=7860)
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