Upload app.py

app.py

@@ -0,0 +1,74 @@
+# app.py
+import os
+from dotenv import load_dotenv
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_community.chat_models import ChatOpenAI
+import gradio as gr
+
+# ------------------ Load environment variables ------------------
+# load_dotenv()
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+
+# ------------------ Paths ------------------
+VECTORSTORE_PATH = os.path.join("storage", "faiss_index")  # folder containing index.faiss and index.pkl
+
+# ------------------ Load vectorstore ------------------
+def load_vectorstore(path):
+    if not os.path.exists(path):
+        raise ValueError(f"FAISS index not found at {path}. Please run ingest.py first.")
+    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)
+
+# ------------------ Load LLM ------------------
+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"
+)
+
+# ------------------ Gradio Chat ------------------
+def respond(user_message, chat_history):
+    if user_message:
+        try:
+            result = qa_chain({"question": user_message, "chat_history": memory.chat_memory.messages})
+            answer = result["answer"]
+        except Exception as e:
+            answer = f"Error: {str(e)}"
+        chat_history.append((user_message, answer))
+    return chat_history, chat_history
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 💉 Diabetes Chatbot\nChat with the bot about diabetes. It remembers your questions during this session!")
+    chatbot = gr.Chatbot()
+    user_input = gr.Textbox(label="Type your question here...", placeholder="Ask anything about diabetes...", lines=1)
+    user_input.submit(respond, [user_input, chatbot], [chatbot, chatbot])
+
+demo.launch()