Update app.py

app.py

@@ -4,65 +4,62 @@ from huggingface_hub import InferenceClient
 client = InferenceClient("Grandediw/lora_model")
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    # Convert tuple-based history to messages if needed
-    messages = [{"role": "system", "content": system_message}]
+    # Build the prompt from system_message and the conversation history
+    # history is a list of (user_message, assistant_message) tuples
+    prompt = system_message.strip() + "\n\n"
+
     for user_msg, assistant_msg in history:
         if user_msg:
-            messages.append({"role": "user", "content": user_msg})
+            prompt += f"User: {user_msg}\n"
         if assistant_msg:
-            messages.append({"role": "assistant", "content": assistant_msg})
-    messages.append({"role": "user", "content": message})
+            prompt += f"Assistant: {assistant_msg}\n"
+    
+    # Add the latest user message
+    prompt += f"User: {message}\nAssistant:"
 
     response = ""
-    for partial in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    # Use text_generation instead of chat_completion
+    for partial in client.text_generation(
+        prompt=prompt,
+        max_new_tokens=max_tokens,
         temperature=temperature,
         top_p=top_p,
+        stream=True
     ):
-        token = partial.choices[0].delta.content
+        # partial is a TextGenerationStreamResponse
+        token = partial.token.text  # Extract the generated token text
         response += token
         yield response
 
-with gr.Blocks(title="Enhanced LORA Chat Interface") as demo:
-    gr.Markdown(
-        """
-        # LORA Chat Assistant
-        Welcome! This is a demo of a LORA-based Chat Assistant.  
-        Start by entering your prompt below.
-        """
-    )
-
-    with gr.Row():
-        # System message and other parameters
-        with gr.Column():
-            system_message = gr.Textbox(
-                value="You are a friendly Chatbot.",
-                label="Initial Behavior (System Message)",
-                lines=3,
-                placeholder="Describe how the assistant should behave..."
-            )
-            max_tokens = gr.Slider(
-                minimum=1, maximum=2048, value=512, step=1,
-                label="Max new tokens"
-            )
-            temperature = gr.Slider(
-                minimum=0.1, maximum=4.0, value=0.7, step=0.1,
-                label="Temperature"
-            )
-            top_p = gr.Slider(
-                minimum=0.1, maximum=1.0, value=0.95, step=0.05,
-                label="Top-p (nucleus sampling)"
-            )
+with gr.Blocks(title="Text Generation Interface") as demo:
+    gr.Markdown("# LORA Text Generation Demo")
 
-        # Create the chat interface using tuple format
-        # Note: `type='tuple'` preserves the (user, assistant) tuple format.
-        chat = gr.ChatInterface(
-            fn=respond,
-            additional_inputs=[system_message, max_tokens, temperature, top_p],
-            type='tuples'
+    with gr.Column():
+        system_message = gr.Textbox(
+            value="You are a helpful and friendly assistant.",
+            label="System Prompt",
+            lines=3,
+        )
+        max_tokens = gr.Slider(
+            minimum=1, maximum=2048, value=512, step=1,
+            label="Max new tokens"
         )
+        temperature = gr.Slider(
+            minimum=0.1, maximum=4.0, value=0.7, step=0.1,
+            label="Temperature"
+        )
+        top_p = gr.Slider(
+            minimum=0.1, maximum=1.0, value=0.95, step=0.05,
+            label="Top-p"
+        )
+
+    # Use type='tuple' if you want to maintain old style conversation format
+    # or omit it to use the default message format.
+    chat = gr.ChatInterface(
+        fn=respond,
+        additional_inputs=[system_message, max_tokens, temperature, top_p],
+        type='tuples'
+    )
 
 if __name__ == "__main__":
     demo.launch()