Upload streamlit_app.py

modified streamlit related code

streamlit_app.py

@@ -0,0 +1,98 @@
+# app.py
+import os
+
+
+# Make Streamlit write config locally (avoids PermissionError in Spaces)
+os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"  # disable usage stats
+os.environ["STREAMLIT_CONFIG_DIR"] = os.getcwd()  # store Streamlit configs locally
+
+# os.environ["STREAMLIT_CONFIG_DIR"] = ".streamlit"
+os.environ["STREAMLIT_LOG_FOLDER"] = ".streamlit"
+os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
+
+
+import streamlit as st
+# from dotenv import load_dotenv
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.vectorstores import FAISS
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+
+# ------------------ Load environment variables ------------------
+# load_dotenv()
+OPENAI_API_KEY = os.environ.getenv("OPENAI_API_KEY")
+
+# ------------------ Paths ------------------
+VECTORSTORE_PATH = os.path.join("storage", "faiss_index")  # folder containing index.faiss and index.pkl
+
+# ------------------ Load vectorstore ------------------
+@st.cache_resource
+def load_vectorstore(path):
+    if not os.path.exists(path):
+        st.error(f"FAISS index not found at {path}. Please run ingest.py first.")
+        return None
+    embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+    vectorstore = FAISS.load_local(
+        path,
+        embeddings,
+        allow_dangerous_deserialization=True
+    )
+    return vectorstore
+
+vectorstore = load_vectorstore(VECTORSTORE_PATH)
+if vectorstore is None:
+    st.stop()
+
+# ------------------ Load LLM ------------------
+@st.cache_resource
+def load_llm():
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0,
+        openai_api_key=OPENAI_API_KEY
+    )
+    return llm
+
+llm = load_llm()
+
+# ------------------ Memory ------------------
+memory = ConversationBufferMemory(
+    memory_key="chat_history",
+    return_messages=True
+)
+
+# ------------------ Conversational Retrieval Chain ------------------
+qa_chain = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
+    memory=memory,
+    output_key="answer"
+)
+
+# ------------------ Streamlit UI ------------------
+st.title("💉 Diabetes Chatbot")
+st.write("Chat with the bot about diabetes. It remembers your questions during this session!")
+
+# Initialize chat history
+if "chat_history" not in st.session_state:
+    st.session_state["chat_history"] = []
+
+# ------------------ Chat Interface ------------------
+user_input = st.chat_input("Type your question here...")
+
+if user_input:
+    # Display user message instantly
+    st.session_state["chat_history"].append((user_input, None))
+
+    # Run QA chain and generate answer
+    with st.spinner("Bot is thinking..."):
+        result = qa_chain({"question": user_input, "chat_history": st.session_state["chat_history"]})
+        answer = result["answer"]
+        # Update the last user message with the bot response
+        st.session_state["chat_history"][-1] = (user_input, answer)
+
+# Display chat history using Streamlit chat messages
+for q, a in st.session_state["chat_history"]:
+    st.chat_message("user").write(q)
+    st.chat_message("assistant").write(a)