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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load Mistral-7B Model
MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float16,
device_map="auto"
)
# Chatbot function
def chat_with_mistral(prompt, history=[]):
history.append(f"User: {prompt}")
input_text = "\n".join(history) + "\nAssistant:"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
output = model.generate(**inputs, max_new_tokens=200)
response = tokenizer.decode(output[0], skip_special_tokens=True).split("Assistant:")[-1].strip()
history.append(f"Assistant: {response}")
return response, history
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("### 🤖 Mistral-7B Chatbot on Hugging Face Spaces")
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Type your message here...")
clear = gr.Button("Clear Chat")
history = gr.State([])
msg.submit(chat_with_mistral, inputs=[msg, history], outputs=[chatbot, history])
clear.click(lambda: ([], []), inputs=[], outputs=[chatbot, history])
# Launch Gradio app
demo.launch()
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