Update app.py

app.py

@@ -1,21 +1,23 @@
 import os
-import pickle
 from langchain.chains import RetrievalQA
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_openai import ChatOpenAI
+from langchain_community.vectorstores import FAISS
 import gradio as gr
+from secret_key import huggingface_api_key  # if you use a separate secret file
 
-# Load API key from environment
+# Load OpenAI API key from environment
 openai_api_key = os.environ.get("OPENAI_API_KEY")
-
-# Ensure key is available
 if not openai_api_key:
-    raise ValueError("OPENAI_API_KEY is not set in the environment variables.")
+    raise ValueError("OPENAI_API_KEY is not set in environment variables.")
 
-# Load vectorstore
+# Load vectorstore from saved FAISS directory
 def load_vectorstore():
-    with open("vectorstore.pkl", "rb") as f:
-        return pickle.load(f)
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        huggingfacehub_api_token=huggingface_api_key
+    )
+    return FAISS.load_local("faiss_index", embeddings)
 
 vectorstore = load_vectorstore()
 
@@ -34,9 +36,9 @@ def chat_with_pdf(query):
     if not query.strip():
         return "❗ Please enter a question."
 
-    result = qa_chain.invoke(query)
-    answer = result['result']
-    sources = result.get('source_documents', [])
+    result = qa_chain.invoke({"query": query})
+    answer = result["result"]
+    sources = result.get("source_documents", [])
 
     formatted_sources = ""
     for i, doc in enumerate(sources):
@@ -51,7 +53,7 @@ def chat_with_pdf(query):
 with gr.Blocks() as demo:
     gr.Markdown("# 📚 PDF Chatbot using RAG")
     gr.Markdown("Ask a question based on pre-embedded PDF documents.")
-    
+
     with gr.Row():
         query_input = gr.Textbox(lines=2, placeholder="Type your question here...", label="Question")
         submit_button = gr.Button("Submit")
@@ -62,4 +64,4 @@ with gr.Blocks() as demo:
 
 # Launch app
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)