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import streamlit as st
import os
import os.path
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, ServiceContext, StorageContext
from llama_index.readers.file import PDFReader
from llama_index.llms.openai import OpenAI
from dotenv import load_dotenv
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.agent.openai import OpenAIAgent
load_dotenv()
storage_path = "./vectorstore"
documents_path = "./documents"
llm = OpenAI(model="gpt-3.5-turbo")
service_context = ServiceContext.from_defaults(llm=llm)
@st.cache_resource(show_spinner=False)
def initialize():
if not os.path.exists(storage_path):
documents = SimpleDirectoryReader(documents_path).load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist(persist_dir=storage_path)
else:
storage_context = StorageContext.from_defaults(persist_dir=storage_path)
index = load_index_from_storage(storage_context)
return index
index = initialize()
fitness_engine = index.as_query_engine(similarity_top_k=3,similarity_cutoff=0.5)
tools = [
QueryEngineTool(
query_engine=fitness_engine,
metadata=ToolMetadata(
name="fitnessdata",
description="this gives information about diets , workoutplan and mental health",
),
)]
agent = OpenAIAgent.from_tools(tools, verbose=True)
# # Retrieve input (e.g., from user input)
# input_text = "what are your views on processed food?"
# # Query the index
# results = agent.query(input_text)
# # Process results (e.g., select the most relevant result)
# if results:
# response = results
# else:
# response = "Sorry, I couldn't find a relevant response."
# # Display response
# print("Response:", response)
st.title("Fitness App")
input_text = st.text_input("Enter your question here:")
if st.button("Ask"):
if input_text:
# Query the index
results = agent.query(input_text)
# Process results
if results:
response = results.response
else:
response = "Sorry, I couldn't find a relevant response."
# Display response
st.write("Response:", response)
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