changing from chainlit to streamlit

streamlit-rag-app.py

@@ -0,0 +1,96 @@
+import streamlit as st
+import os
+import json
+from dotenv import load_dotenv
+
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import Document
+from langchain.embeddings import HuggingFaceEmbeddings
+
+# Load environment variables
+load_dotenv()
+
+# Get the OpenAI API key from the environment
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    st.error("OPENAI_API_KEY is not set. Please add it to your .env file.")
+
+# Initialize session state variables
+if 'vector_store' not in st.session_state:
+    st.session_state.vector_store = None
+if 'qa_chain' not in st.session_state:
+    st.session_state.qa_chain = None
+
+def load_json_file(file_path):
+    """Load JSON data from a file."""
+    with open(file_path, "r", encoding="utf-8") as file:
+        data = json.load(file)
+    return data
+
+def setup_vector_store_from_json(json_data):
+    """Create a vector store from JSON data."""
+    documents = [Document(page_content=item["content"], metadata={"url": item["url"]}) for item in json_data]
+    
+    # Use HuggingFace embeddings
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    
+    vector_store = FAISS.from_documents(documents, embeddings)
+    return vector_store
+
+def setup_qa_chain(vector_store):
+    """Set up the QA chain with a retriever."""
+    retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+    llm = ChatOpenAI(model="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY)
+    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
+    return qa_chain
+
+def main():
+    # Set page title and header
+    st.set_page_config(page_title="Football Players RAG App", page_icon="⚽")
+    st.title("Football Players Knowledge Base 🏆")
+
+    # Sidebar for initialization
+    st.sidebar.header("Initialize Knowledge Base")
+    if st.sidebar.button("Load Data"):
+        try:
+            # Load and preprocess the JSON file
+            json_data = load_json_file("football_players.json")
+            st.session_state.vector_store = setup_vector_store_from_json(json_data)
+            st.session_state.qa_chain = setup_qa_chain(st.session_state.vector_store)
+            st.sidebar.success("Knowledge base loaded successfully!")
+        except Exception as e:
+            st.sidebar.error(f"Error loading data: {e}")
+
+    # Query input and processing
+    st.header("Ask a Question")
+    query = st.text_input("Enter your question about football players:")
+
+    if query:
+        # Check if vector store and QA chain are initialized
+        if st.session_state.qa_chain is None:
+            st.warning("Please load the knowledge base first using the sidebar.")
+        else:
+            # Run the query
+            try:
+                response = st.session_state.qa_chain({"query": query})
+                
+                # Display answer
+                st.subheader("Answer")
+                st.write(response["result"])
+
+                # Display sources
+                st.subheader("Sources")
+                sources = response["source_documents"]
+                for i, doc in enumerate(sources, 1):
+                    with st.expander(f"Source {i}"):
+                        st.write(f"**Content:** {doc.page_content}")
+                        st.write(f"**URL:** {doc.metadata.get('url', 'No URL available')}")
+
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    main()