Create app.py

app.py

@@ -0,0 +1,76 @@
+import streamlit as st
+import os
+import os.path
+
+from dotenv import load_dotenv
+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 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)