Update app.py

app.py

@@ -1,11 +1,19 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
+# Load your fine-tuned model
+model_id = "ragunath-ravi/distilgpt2-lmsys-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
+# Set padding token to be the same as EOS token if not set
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Move model to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
 
 def respond(
     message,
@@ -15,50 +23,68 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
+    # Format the conversation history as expected by the model
+    prompt = system_message + "\n\n"
+    
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            prompt += f"User: {user_msg}\n"
+        if assistant_msg:
+            prompt += f"Assistant: {assistant_msg}\n"
+    
+    # Add the latest user message
+    prompt += f"User: {message}\nAssistant:"
+    
+    # Tokenize the prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    
+    # Generate response
+    with torch.no_grad():
+        output = model.generate(
+            inputs["input_ids"],
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            attention_mask=inputs["attention_mask"],
+        )
+    
+    # Decode the generated response
+    full_response = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Extract only the assistant's part from the full response
+    assistant_response = full_response[len(prompt):].strip()
+    
+    # Sometimes the model might continue with "User:" - we need to cut that off
+    if "User:" in assistant_response:
+        assistant_response = assistant_response.split("User:")[0].strip()
+    
+    # Stream the response token by token (simulated for this model)
+    response_so_far = ""
+    tokens = assistant_response.split()
+    for token in tokens:
+        response_so_far += token + " "
+        yield response_so_far.strip()
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create the Gradio chat interface
 demo = gr.ChatInterface(
     respond,
     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.Textbox(value="You are a helpful assistant.", label="System message"),
+        gr.Slider(minimum=1, maximum=512, value=128, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
-            value=0.95,
+            value=0.9,
             step=0.05,
             label="Top-p (nucleus sampling)",
         ),
     ],
+    title="DistilGPT-2 Chat Assistant",
+    description="A simple chatbot powered by a fine-tuned DistilGPT-2 model on the LMSYS Chat 1M dataset.",
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()