Update app.py

app.py

@@ -1,24 +1,14 @@
-import streamlit as st
-from llama_index.llms.huggingface import HuggingFaceLLM
-from transformers import AutoTokenizer
-
-# Load Hugging Face model (replace with your desired model)
-model_name = "openai/ada-002"  # Choose a suitable model from Hugging Face Hub
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = HuggingFaceLLM(model_name, tokenizer=tokenizer)
-
-# Streamlit app
-st.title("Text Analysis with LlamaIndex and Hugging Face")
-user_input = st.text_area("Enter your text here:")
-
-if user_input:
-  # Prompt for the model (modify as needed)
-  prompt = f"Analyze the following text: {user_input}"
-
-  # Generate response using LlamaIndex with the Hugging Face model
-  response = model.generate_text(prompt, max_length=150)
-
-  # Display the response
-  st.write("Model Response:")
-  st.write(response)
-
+from llama_index import VectorStoreIndex,SimpleDirectoryReader,ServiceContext
+from llama_index.llms import HuggingFaceLLM
+from llama_index.prompts.prompts import SimpleInputPrompt
+import os
+from huggingface_hub import login
+
+documents=SimpleDirectoryReader("/state-of-the-unioon.txt").load_data()
+system_prompt="""
+You are a Q&A assistant. Your goal is to answer questions as
+accurately as possible based on the instructions and context provided.
+"""
+## Default format supportable by LLama2
+query_wrapper_prompt=SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
+login(token=HF_TOKEN)