Update streamlit_app.py

streamlit_app.py

@@ -1,98 +1,92 @@
-# 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)
+# app.py
+import os
+
+
+
+
+
+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)