app.py

import gradio as gr
from unsloth import FastLanguageModel
from transformers import TextIteratorStreamer
from threading import Thread
from torch import bfloat16

# Load your fine-tuned model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=".",  # Path to your fine-tuned model
    max_seq_length=8192,
    dtype=bfloat16,
    load_in_4bit=False,
)
FastLanguageModel.for_inference(model)  # Enable optimized inference

def get_streaming_generator(model, tokenizer, history, max_new_tokens=8192):
    """Function that returns a generator yielding streaming outputs"""
    # Convert history to the format expected by tokenizer
    formatted_history = []
    for exchange in history:
        formatted_history.append({"role": "user", "content": exchange[0]})
        if len(exchange) > 1 and exchange[1]:
            formatted_history.append({"role": "assistant", "content": exchange[1]})
    
    inputs = tokenizer(
        [
            tokenizer.apply_chat_template(formatted_history, 
                                        tokenize=False, 
                                        add_generation_prompt=True),
        ], 
        return_tensors="pt",
        padding=True,
        return_attention_mask=True
    ).to("cuda")
    
    # Create the streamer
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
    
    # Run generation in a separate thread
    generation_kwargs = dict(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        streamer=streamer,
        max_new_tokens=max_new_tokens
    )
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    return streamer

def predict(message, history):
    # Add user message to history in the format Gradio expects
    history = history or []
    history.append([message, ""])
    
    # Get the streamer with properly formatted history
    streamer = get_streaming_generator(model, tokenizer, history)
    
    # Stream the response
    full_response = ""
    for text_chunk in streamer:
        full_response += text_chunk
        # Update the last message with the current full response
        history[-1][1] = full_response
        yield history

def clear_chat():
    return [], ""

# Create the Gradio interface with Markdown support
with gr.Blocks(css=".message { white-space: pre-wrap; }") as iface:
    chatbot = gr.Chatbot(
        show_label=False,
        container=True,
        height=600,
        bubble_full_width=False,
        render_markdown=True,
        latex_delimiters=[
            {"left": "$$", "right": "$$", "display": True},
            {"left": "$", "right": "$", "display": False},
        ],
    )
    msg = gr.Textbox(
        label="Message",
        placeholder="Type your message here... (Markdown supported)",
        lines=2
    )
    submit = gr.Button("Submit")
    clear = gr.Button("Clear")
    
    # Set up the chat interface with streaming
    msg.submit(
        predict,
        [msg, chatbot],
        [chatbot],
        api_name="predict"
    ).then(
        lambda: "", None, [msg]  # Clear input after submission
    )
    
    submit.click(
        predict,
        [msg, chatbot],
        [chatbot]
    ).then(
        lambda: "", None, [msg]  # Clear input after submission
    )
    
    clear.click(
        clear_chat,
        None, 
        [chatbot, msg], 
        queue=False
    )

if __name__ == "__main__":
    iface.launch()