app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Initialize a list to store the conversation history
conversation_history = []

# Function to interact with the model using the Inference API
def chat_with_model(user_input, hf_api_key):
    global conversation_history
    
    if not hf_api_key:
        return "Error: Please provide your Hugging Face API key."

    try:
        # Initialize the InferenceClient with the provided API key
        client = InferenceClient(api_key=hf_api_key)

        # Add the user's message to the conversation history
        conversation_history.append({"role": "user", "content": user_input})

        # Define the system message (defining the assistant role)
        system_message = {
            "role": "system", 
            "content": "You are a code assistant that helps with code generation, debugging, and explanations."
        }

        # Add system message to the conversation history
        if len(conversation_history) == 1:  # Add system message only once
            conversation_history.insert(0, system_message)

        # Ensure the conversation history doesn't exceed token limits
        if len(conversation_history) > 10:  # Keep the last 10 messages
            conversation_history = [system_message] + conversation_history[-10:]

        # Create a stream for chat completions using the API
        stream = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct", 
            messages=conversation_history, 
            max_tokens=500,
            stream=True
        )
        
        # Collect the generated response from the model
        response = ""
        for chunk in stream:
            response += chunk.choices[0].delta.content
        
        # Add the assistant's response to the conversation history
        conversation_history.append({"role": "assistant", "content": response})
        
        return response

    except Exception as e:
        return f"Error: {e}"

# AdSense HTML Code
adsense_code = """
<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-4124087181208916"
     crossorigin="anonymous"></script>
<ins class="adsbygoogle"
     style="display:block"
     data-ad-format="fluid"
     data-ad-layout-key="-gw-3+1f-3d+2z"
     data-ad-client="ca-pub-4124087181208916"
     data-ad-slot="1309752867"></ins>
<script>
     (adsbygoogle = window.adsbygoogle || []).push({});
</script>
"""

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.HTML(adsense_code)  # Add Google AdSense
    gr.Markdown("### Code Assistant with Qwen2.5-Coder")
    gr.Markdown("Ask me anything about coding! Enter your Hugging Face API key to start.")
    
    # Create the input and output interface
    with gr.Row():
        user_input = gr.Textbox(lines=5, placeholder="Ask me anything about coding...")
        api_key = gr.Textbox(lines=1, placeholder="Enter your Hugging Face API key", type="password")
    
    # Create the output display
    output = gr.Textbox(label="Response")

    # Button for submitting queries
    submit_button = gr.Button("Submit")
    submit_button.click(chat_with_model, inputs=[user_input, api_key], outputs=output)

# Launch the app
demo.launch()