Update app.py

app.py

@@ -1,57 +1,90 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Initialize an empty list to store the conversation history
+# 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
     
-    # 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
-    conversation_history.insert(0, system_message)
-
-    # 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
+    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-client="ca-pub-4124087181208916"
+     data-ad-slot="8617397243"
+     data-ad-format="auto"
+     data-full-width-responsive="true"></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.")
     
-    # Add the assistant's response to the conversation history
-    conversation_history.append({"role": "assistant", "content": response})
+    # 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")
     
-    return response
+    # Create the output display
+    output = gr.Textbox(label="Response")
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=chat_with_model, 
-    inputs=[
-        gr.Textbox(lines=5, placeholder="Ask me anything about coding..."), 
-        gr.Textbox(lines=1, placeholder="Enter your Hugging Face API key", type="password")  # API key input
-    ], 
-    outputs="text", 
-    title="Code Assistant with Qwen2.5-Coder", 
-    description="A code assistant that helps you with code generation, debugging, and explanations using the Qwen2.5-Coder model via Hugging Face Inference API."
-)
-
-# Launch the interface
-iface.launch()
+    # 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()