app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the Mistral-7B-v0.2 model and tokenizer
model_name = "mistralai/Mistral-7B-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the chatbot function
def chatbot(input_text, history=[]):
    # Combine history and current input
    chat_history = " ".join([f"User: {h[0]} Assistant: {h[1]}" for h in history])
    input_prompt = f"{chat_history} User: {input_text} Assistant:"
    inputs = tokenizer(input_prompt, return_tensors="pt", truncation=True)
    
    # Generate response
    outputs = model.generate(
        **inputs,
        max_length=512,
        do_sample=True,
        temperature=0.7,
        top_p=0.9
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("Assistant:")[-1].strip()
    
    # Update history
    history.append((input_text, response))
    return response, history

# Gradio Interface
with gr.Blocks() as interface:
    gr.Markdown("### Mistral-7B-v0.2 Chatbot")
    gr.Markdown("This chatbot is powered by the Mistral-7B-v0.2 model for summarization and general conversation.")

    with gr.Row():
        chatbot_box = gr.Chatbot(label="Chatbot")
    
    with gr.Row():
        user_input = gr.Textbox(label="Your Input", placeholder="Type your message here...")
        submit_button = gr.Button("Submit")

    with gr.Row():
        reset_button = gr.Button("Reset Chat")
    
    # Functionality
    submit_button.click(chatbot, inputs=[user_input, chatbot_box], outputs=[chatbot_box, chatbot_box])
    reset_button.click(lambda: [], inputs=[], outputs=[chatbot_box])

# Launch the app
interface.launch()