app.py

import streamlit as st
import os
import requests

# Set your model and project details
project_name = 'my_autotrain_llm'
model_name = 'abhishek/llama-2-7b-hf-small-shards'
hf_token = 'YOUR_HUGGING_FACE_TOKEN'  # Replace with your Hugging Face token

# Set Hugging Face API endpoint
hf_api_endpoint = 'https://huggingface.co/am-nandeesh/jql'

st.title("Streamlit App for Hugging Face Spaces")

user_input = st.text_area("Enter Text:", "Type your text here...")


if st.button("Run Model"):
    headers = {"Authorization": f"Bearer {hf_token}"}
    data = {"inputs": user_input}

    response = requests.post(
        f"{hf_api_endpoint}{model_name}/tasks/text-generation",
        headers=headers,
        json=data
    )

    if response.status_code == 200:
        output_text = response.json()['predictions'][0]
        st.success(f"Model Output: {output_text}")
    else:
        st.error(f"Error running the model. Status code: {response.status_code}")