Create app.py

app.py

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+import gradio as gr
+from groq import Groq
+
+# Initialize Groq client with your API key
+client = Groq(api_key="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")
+
+def chat_with_groq(message, history, model_choice, temperature, max_tokens):
+    """
+    Function to interact with Groq API
+    """
+    try:
+        # Prepare conversation history in the format Groq expects
+        messages = []
+        
+        # Add conversation history
+        for human, assistant in history:
+            messages.append({"role": "user", "content": human})
+            messages.append({"role": "assistant", "content": assistant})
+        
+        # Add current message
+        messages.append({"role": "user", "content": message})
+        
+        # Create chat completion
+        chat_completion = client.chat.completions.create(
+            messages=messages,
+            model=model_choice,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=1,
+            stream=False
+        )
+        
+        # Get the response
+        response = chat_completion.choices[0].message.content
+        
+        return response
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Available Groq models
+available_models = [
+    "llama-3.1-8b-instant",
+    "llama-3.1-70b-versatile", 
+    "mixtral-8x7b-32768",
+    "gemma2-9b-it"
+]
+
+def predict(message, chat_history, model_choice, temperature, max_tokens):
+    """
+    Predict function for Gradio ChatInterface
+    """
+    if not message.strip():
+        return chat_history
+    
+    # Get response from Groq
+    bot_response = chat_with_groq(message, chat_history, model_choice, temperature, max_tokens)
+    
+    # Add to chat history
+    chat_history.append((message, bot_response))
+    
+    return chat_history
+
+# Custom CSS for better styling
+custom_css = """
+#chatbot {
+    min-height: 500px;
+}
+.container {
+    max-width: 1200px;
+    margin: auto;
+}
+"""
+
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    title="Groq AI Chatbot",
+    css=custom_css
+) as demo:
+    
+    gr.Markdown(
+        """
+        # 🤖 Groq AI Chatbot
+        Fast AI-powered chatbot powered by Groq API
+        
+        **Note**: This chatbot uses Groq's inference API for fast responses.
+        """
+    )
+    
+    # Store chat history
+    chatbot = gr.Chatbot(
+        label="Conversation",
+        height=500,
+        show_copy_button=True,
+        elem_id="chatbot"
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="Your Message",
+            placeholder="Type your message here and press Enter...",
+            lines=2,
+            scale=4,
+            container=False
+        )
+    
+    with gr.Row():
+        send_btn = gr.Button("Send 🚀", variant="primary", size="lg")
+        clear_btn = gr.Button("Clear Chat 🗑️", variant="secondary")
+    
+    with gr.Accordion("⚙️ Model Settings", open=False):
+        with gr.Row():
+            model_choice = gr.Dropdown(
+                choices=available_models,
+                value="llama-3.1-8b-instant",
+                label="Select Model",
+                info="Choose which AI model to use"
+            )
+        with gr.Row():
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature",
+                info="Controls creativity: Lower = more deterministic, Higher = more creative"
+            )
+            max_tokens = gr.Slider(
+                minimum=100,
+                maximum=4096,
+                value=1024,
+                step=100,
+                label="Max Tokens",
+                info="Maximum length of the response"
+            )
+    
+    # Function to handle user message
+    def user_message(user_msg, history):
+        return "", history + [[user_msg, None]]
+    
+    # Function to handle bot response
+    def bot_message(history, model, temp, tokens):
+        user_msg = history[-1][0]
+        bot_msg = chat_with_groq(user_msg, history[:-1], model, temp, tokens)
+        history[-1][1] = bot_msg
+        return history
+    
+    # Event handlers
+    msg.submit(
+        user_message,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot]
+    ).then(
+        bot_message,
+        inputs=[chatbot, model_choice, temperature, max_tokens],
+        outputs=[chatbot]
+    )
+    
+    send_btn.click(
+        user_message,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot]
+    ).then(
+        bot_message,
+        inputs=[chatbot, model_choice, temperature, max_tokens],
+        outputs=[chatbot]
+    )
+    
+    clear_btn.click(
+        lambda: None,
+        outputs=[chatbot],
+        queue=False
+    ).then(
+        lambda: [],
+        None,
+        [chatbot]
+    )
+    
+    gr.Markdown(
+        """
+        ### 💡 About the Models:
+        - **Llama 3.1 8B Instant**: Fast and efficient for general conversations
+        - **Llama 3.1 70B Versatile**: More capable for complex tasks
+        - **Mixtral 8x7B**: Excellent for coding and reasoning tasks
+        - **Gemma2 9B**: Balanced performance across various tasks
+        
+        ### ⚠️ Important:
+        - Your conversations are processed through Groq's API
+        - The API key is embedded in this application
+        - For personal use only
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True, debug=True)