Initial commit

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer from Hugging Face
+model_id = "huggingface/llama-2-7b-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+st.title("Food Nutrition Analysis with LLaMA")
+
+user_input = st.text_area("Enter food label text:")
+
+if st.button("Analyze"):
+    inputs = tokenizer(user_input, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    st.write(response)

requirements.txt

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+transformers
+torch
+streamlit  # If you're using Streamlit