Update app.py

app.py

@@ -1,70 +1,163 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
+# app.py - Hugging Face Space Application
+# This creates a chat interface for your fine-tuned model
 
-    messages.extend(history)
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-    messages.append({"role": "user", "content": message})
+# Configuration
+BASE_MODEL = "microsoft/phi-2"
+ADAPTER_MODEL = "sepo25/my-finetuned-model"
 
-    response = ""
+# Global variables to store loaded model
+model = None
+tokenizer = None
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+def load_model():
+    """Load the base model and fine-tuned adapter"""
+    global model, tokenizer
+    
+    print("Loading model... This may take a minute.")
+    
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+    tokenizer.pad_token = tokenizer.eos_token
+    
+    # Load base model
+    base_model = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    
+    # Load fine-tuned adapter
+    model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL)
+    
+    print("Model loaded successfully!")
+    return model, tokenizer
 
-        response += token
-        yield response
+def generate_response(message, chat_history, temperature=0.7, max_tokens=200):
+    """Generate a response from the model"""
+    global model, tokenizer
+    
+    # Load model if not already loaded
+    if model is None or tokenizer is None:
+        load_model()
+    
+    # Format the prompt
+    prompt = f"### Instruction:\n{message}\n\n### Response:\n"
+    
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            do_sample=True,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode response
+    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract only the response part (after "### Response:")
+    if "### Response:" in full_response:
+        response = full_response.split("### Response:")[-1].strip()
+    else:
+        response = full_response
+    
+    # Update chat history
+    chat_history.append((message, response))
+    
+    return "", chat_history
 
+def clear_chat():
+    """Clear the chat history"""
+    return [], []
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
+# Create Gradio interface
+with gr.Blocks(title="My Fine-tuned Model Chat", theme=gr.themes.Soft()) as demo:
+    
+    gr.Markdown(
+        """
+        # 🤖 Chat with My Fine-tuned Model
+        
+        This model has been fine-tuned on custom text data and can answer questions and provide summaries.
+        
+        **Tips:**
+        - Ask questions about the content it was trained on
+        - Request summaries of information
+        - Be specific in your questions for best results
+        """
+    )
+    
+    chatbot = gr.Chatbot(
+        label="Conversation",
+        height=400,
+        show_label=True
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="Your message",
+            placeholder="Type your question here...",
+            scale=4
+        )
+        submit_btn = gr.Button("Send", variant="primary", scale=1)
+    
+    with gr.Accordion("⚙️ Advanced Settings", open=False):
+        temperature = gr.Slider(
             minimum=0.1,
             maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
+            value=0.7,
+            step=0.1,
+            label="Temperature (higher = more creative)",
+        )
+        max_tokens = gr.Slider(
+            minimum=50,
+            maximum=500,
+            value=200,
+            step=50,
+            label="Max tokens (response length)",
+        )
+    
+    with gr.Row():
+        clear_btn = gr.Button("🗑️ Clear Chat")
+    
+    gr.Markdown(
+        """
+        ---
+        ### Example Questions:
+        - "What is this text about?"
+        - "Summarize the main points"
+        - "Tell me about [specific topic from your text]"
+        """
+    )
+    
+    # Event handlers
+    submit_btn.click(
+        generate_response,
+        inputs=[msg, chatbot, temperature, max_tokens],
+        outputs=[msg, chatbot]
+    )
+    
+    msg.submit(
+        generate_response,
+        inputs=[msg, chatbot, temperature, max_tokens],
+        outputs=[msg, chatbot]
+    )
+    
+    clear_btn.click(
+        clear_chat,
+        outputs=[chatbot, msg]
+    )
 
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()