Cleaned app.py and added secure token handling

app.py

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+import streamlit as st
+import os
+from transformers import AutoModel, AutoTokenizer
+
+# Access the Hugging Face token from environment variables
+hf_token = os.getenv('HUGGING_FACE_HUB_TOKEN')
+
+# Load the model and tokenizer with the token
+model = AutoModel.from_pretrained('naver/cocom-v1-128-mistral-7b', trust_remote_code=True, use_auth_token=hf_token)
+model = model.to('cuda')
+tokenizer = AutoTokenizer.from_pretrained('naver/cocom-v1-128-mistral-7b')
+
+def generate_answer(contexts, questions):
+    inputs = tokenizer(questions, contexts, return_tensors='pt', padding=True, truncation=True)
+    inputs = {key: value.to('cuda') for key, value in inputs.items()}
+    outputs = model(**inputs)
+    return ["Generated answer here"]  # Replace with actual generation logic
+
+st.title("LLM Model Testing")
+
+context = st.text_area("Enter context:")
+question = st.text_input("Enter your question:")
+
+if st.button("Generate Answer"):
+    with st.spinner("Generating..."):
+        try:
+            answers = generate_answer([context], [question])
+            st.success("Generated Answer:")
+            st.write(answers[0])
+        except Exception as e:
+            st.error(f"Error generating answer: {e}")

requirements.txt

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+streamlit
+transformers
+torch
+flash-attn --no-build-isolation