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app.py

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+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")
+# 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)
+    if response.status_code == 200:
+        return response.json().get("generated_text", "No output generated")
+    else:
+        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!")

requirements.txt

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+streamlit>=1.28.0
+pandas>=2.1.0
+Pillow>=10.0.0
+numpy>=1.24.0
+protobuf>=4.21.0
+watchdog>=3.0.0
+python-dotenv==1.0.1
+requests
+
+
+tqdm>=4.65.0
+transformers>=4.30.0
+sentence-transformers>=2.2.2
+scikit-learn>=1.3.0