Update app.py

app.py

@@ -1,32 +1,14 @@
-import os
 import gradio as gr
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-access_token = os.getenv('HF_TOKEN')
-
-# Define the repository ID and access token
-repo_id = "Mikhil-jivus/Llama-32-3B-FineTuned"
-
-# Load the tokenizer and model from the Hugging Face repository
-tokenizer = AutoTokenizer.from_pretrained(repo_id, token=access_token)
+"""
+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
+"""
 
-model = AutoModelForCausalLM.from_pretrained(
-    repo_id, 
-    token=access_token, 
-    torch_dtype=torch.bfloat16,  # or use torch.bfloat16 if supported
-    device_map="auto"  # Automatically use available GPU/CPU efficiently
-)
+access_token = os.getenv('HF_TOKEN')
+client = InferenceClient("Mikhil-jivus/Llama-32-3B-FineTuned",api_key = access_token)
 
-# Define a function to clean up any repeated segments in the generated response
-def clean_response(response, history):
-    # Check for repetition in the response and remove it
-    if len(history) > 0:
-        last_user_message, last_bot_response = history[-1]
-        if last_bot_response in response:
-            response = response.replace(last_bot_response, "").strip()
-    
-    return response
 
 def respond(
     message,
@@ -36,52 +18,37 @@ def respond(
     temperature,
     top_p,
 ):
-    # Add system prompt only once at the beginning of the conversation
-    if len(history) == 0:
-        input_text = f"system: {system_message}\nuser: {message}\n"
-    else:
-        input_text = f"user: {message}\n"
+    messages = [{"role": "system", "content": system_message}]
 
-    # Append previous conversation history to the input text
-    for user_msg, bot_msg in history:
-        input_text += f"user: {user_msg}\nassistant: {bot_msg}\n"
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
 
-    # Tokenize the input messages
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    
-    # Move input_ids to the GPU
-    input_ids = input_ids.to("cuda")
-    
-    # Create attention mask and move to GPU
-    attention_mask = input_ids.ne(tokenizer.pad_token_id).long().to("cuda")
-    
-    # Generate a response
-    chat_history_ids = model.generate(
-        input_ids,
-        max_new_tokens=max_tokens,
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
         temperature=temperature,
         top_p=top_p,
-        pad_token_id=tokenizer.eos_token_id,
-        do_sample=True,
-        attention_mask=attention_mask,
-    )
-    
-    # Decode the response
-    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    ):
+        token = message.choices[0].delta.content
 
-    # Clean the response to remove any repeated or unnecessary text
-    response = clean_response(response, history)
-    
-    # Update history with the new user message and bot response
-    history.append((message, response))
+        response += token
+        yield response
 
-    return response
-
-# Set up the Gradio app interface
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
+        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(
@@ -94,5 +61,6 @@ demo = gr.ChatInterface(
     ],
 )
 
+
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()