Update app.py

app.py

@@ -1,12 +1,11 @@
 import os
 import gradio as gr
 from langchain.chains import RetrievalQA
-from langchain_community.vectorstores import Chroma  # ✅ Fixed Import
-from langchain.llms import OpenAI
-from langchain_huggingface import HuggingFaceEndpoint  # ✅ Corrected Import
-from langchain.embeddings import OpenAIEmbeddings
-from langchain_community.embeddings import HuggingFaceEmbeddings  # ✅ Corrected Import
-from langchain_community.document_loaders import PyPDFLoader  # ✅ Corrected Import
+from langchain_community.vectorstores import Chroma
+from langchain_community.llms import OpenAI  # ✅ Fixed Import
+from langchain_huggingface import HuggingFaceEndpoint  # ✅ Fixed Import
+from langchain_community.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings  # ✅ Fixed Import
+from langchain_community.document_loaders import PyPDFLoader  # ✅ Fixed Import
 import time
 
 # Define paths for cybersecurity training PDFs
@@ -41,8 +40,8 @@ vector_db = load_data()
 # Load LLM from Hugging Face securely
 llm = HuggingFaceEndpoint(
     repo_id="google/flan-t5-large",
-    temperature=0.5,  # ✅ Explicitly set temperature
-    max_length=512,  # ✅ Explicitly set max_length
+    temperature=0.5,  # ✅ Ensure temperature is explicit
+    max_new_tokens=250,  # ✅ Ensure API limit is followed
     huggingfacehub_api_token=HUGGINGFACE_API_KEY
 )
 
@@ -52,7 +51,7 @@ qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_db.as_retriever
 # Function to simulate futuristic typing effect
 def chatbot_response(question):
     """Handles chatbot queries with a typing effect"""
-    response = qa_chain.run(question)
+    response = qa_chain.invoke(question)  # ✅ Use invoke instead of deprecated `run`
     displayed_response = ""
     for char in response:
         displayed_response += char