Update app.py

app.py

@@ -4,6 +4,7 @@ from langchain.embeddings import GooglePalmEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.chains import RetrievalQA
 from langchain.llms import GooglePalm
+from secret1 import GOOGLE_API as google_api
 from langchain.text_splitter import CharacterTextSplitter
 # Define chatbot response function
 def chatbot_response(user_input):
@@ -36,9 +37,19 @@ def text_extract(file):
     for page_num in range(num_pages):
         page = pdf_reader.pages[page_num]
         text += page.extract_text() or ""
-    text_splitter = text_splitter_function(text)  # Split extracted text into chunks
+    text_splitter = text_splitter_function(text) 
+    embeddings = GooglePalmEmbeddings(google_api_key=google_api)
+    db = FAISS.from_texts(text_splitter, embeddings)
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
+    llm=GooglePalm(google_api_key=google_api)
+    qa = RetrievalQA.from_chain_type(
+        llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True
+    )
+    result1 = qa.invoke(({"query":}))
+    print("FitBot:",result1['result'])
+    # Split extracted text into chunks
     # result = helper(text_splitter)  # Call helper to process text chunks
-    return text_splitter
+    return result1['result']
 
 # Define Gradio interface
 with gr.Blocks() as demo:
@@ -63,6 +74,6 @@ with gr.Blocks() as demo:
 
 # Initialize embeddings and launch the app
 if __name__ == "__main__":
-    google_api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your actual Google API key
-    embeddings = GooglePalmEmbeddings(google_api_key=google_api_key)
+  # Replace with your actual Google API key
+   
     demo.launch()