app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "ministral/Ministral-3b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_response(prompt, max_length=400):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=max_length)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

def chat(message, history):
    history_text = "\n".join([f"Human: {h[0]}\nAI: {h[1]}" for h in history])
    prompt = f"{history_text}\nHuman: {message}\nAI:"
    response = generate_response(prompt)
    return response

# Create the Gradio interface
iface = gr.ChatInterface(
    fn=chat,
    title="Ministral 3B Chat",
    description="Chat with the Ministral 3B model. Type your message below.",
    examples=[
        ["Tell me a short story about a robot."],
        ["What are the benefits of exercise?"],
        ["Explain quantum computing in simple terms."]
    ],
    cache_examples=False
)

# Launch the interface
iface.launch()