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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()
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