app.py

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login

# Initialize the Hugging Face token
token = 'hf_your_actual_token_here'
login(token=token)

# Initialize the text generation pipeline
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Define the function to handle chat
def chat(message):
    # Generate the response using the model
    response = pipe(message, max_length=50)
    # Extract and return the generated text
    return response[0]['generated_text']

# Create the Gradio interface
interface = gr.Interface(
    fn=chat,
    inputs=gr.inputs.Textbox(label="Enter your message"),
    outputs="text",
    title="Text Generation Bot",
    description="Chat with the Mistral-7B-Instruct model to get responses to your queries."
)

# Launch the Gradio interface
interface.launch()