app.py

import requests
import streamlit as st
from dotenv import load_dotenv
import os

# .env 파일 로드
load_dotenv()

# Hugging Face API 정보
API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
API_KEY = os.getenv("HUGGINGFACE_API_KEY")
print(os.getenv("HUGGINGFACE_API_KEY"))


# 모델 호출 함수
def query_model(prompt):
    headers = {"Authorization": f"Bearer {API_KEY}"}
    data = {"inputs": prompt}
    
    response = requests.post(API_URL, headers=headers, json=data)
    print(response)
    
    if response.status_code == 200:
        result = response.json()
        print("Response:", result)  # API 응답 전체를 출력
        if isinstance(result, list) and len(result) > 0:
            return result[0].get("generated_text", "No output generated")
        else:
            return "No valid output from API"
    else:
        print(f"Error: {response.status_code}, {response.text}")
        return f"Error: {response.status_code}, {response.text}"

# Streamlit UI 구성
st.title("Meta-Llama Text Generator")
st.write("Enter a prompt to generate text using the Meta-Llama-3B model.")

# 사용자 입력
prompt = st.text_area("Enter your prompt:", height=200)

if st.button("Generate"):
    if prompt.strip():
        st.write("Generating...")
        output = query_model(prompt)
        st.write("### Output:")
        st.write(output)
    else:
        st.warning("Please enter a valid prompt!")