app.py

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

# Step 1: Load token from repository secret (environment variable)
hf_token = os.getenv("HF_TOKEN")

# Step 2: Login to Hugging Face using token
if hf_token is not None:
    login(token=hf_token)
else:
    raise EnvironmentError("HF_TOKEN not found in environment. Please set it in repository secrets.")

# Step 3: Load tokenizer and model with auth token
model_name = "mistralai/Mistral-7B-Instruct-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)

# Step 4: Create a pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Step 5: Define a simple Gradio UI
def predict_completion(prompt):
    output = pipe(prompt, max_new_tokens=10, num_return_sequences=1, do_sample=True)
    return output[0]['generated_text']

# Step 6: Launch Gradio interface
interface = gr.Interface(fn=predict_completion, 
                         inputs=gr.Textbox(label="Input Prompt"), 
                         outputs="text", 
                         title="Predictive Keyboard using Mistral")

interface.launch()