Update app.py

app.py

@@ -1,5 +1,4 @@
 import streamlit as st
-from streamlit.state.session_state import SessionState
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_groq import ChatGroq
@@ -11,14 +10,6 @@ import tempfile
 from gtts import gTTS
 import os
 
-def text_to_speech(text):
-    tts = gTTS(text=text, lang='en')
-    audio_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
-    temp_filename = audio_file.name
-    tts.save(temp_filename)
-    st.audio(temp_filename, format='audio/mp3')
-    os.remove(temp_filename)
-
 def get_pdf_text(pdf_docs):
     text = ""
     for pdf in pdf_docs:
@@ -49,6 +40,14 @@ def get_conversational_chain():
     prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
     return chain
+    
+def text_to_speech(text):
+    tts = gTTS(text=text, lang='en')
+    audio_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
+    temp_filename = audio_file.name
+    tts.save(temp_filename)
+    st.audio(temp_filename, format='audio/mp3')
+    os.remove(temp_filename)
 
 def user_input(user_question, api_key):
     embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=api_key, model_name="sentence-transformers/all-MiniLM-l6-v2")
@@ -72,43 +71,42 @@ def main():
     st.markdown("<h1 style='font-size:20px;'>ChatBot by Muhammad Huzaifa</h1>", unsafe_allow_html=True)
     api_key = st.secrets["inference_api_key"]
 
-    session_state = SessionState.get(pdf_docs=None, raw_text=None, processing_complete=False)
-
     # Sidebar column for file upload
     with st.sidebar:
         st.header("Chat with PDF")
-        session_state.pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True, type=["pdf"])
+        pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True, type=["pdf"])
 
     # Main column for displaying extracted text and user interaction
     col1, col2 = st.columns([1, 2])
-
-    if session_state.pdf_docs:
+    raw_text = None
+    if pdf_docs:
         with col1:
             if st.button("Submit"):
                 with st.spinner("Processing..."):
-                    session_state.raw_text = get_pdf_text(session_state.pdf_docs)
-                    text_chunks = get_text_chunks(session_state.raw_text)
+                    raw_text = get_pdf_text(pdf_docs)
+                    text_chunks = get_text_chunks(raw_text)
                     get_vector_store(text_chunks, api_key)
                     st.success("Processing Complete")
-                    session_state.processing_complete = True
- 
+    
     # Check if PDF documents are uploaded and processing is complete
-    if session_state.pdf_docs and session_state.raw_text and session_state.processing_complete:
+    if pdf_docs is not None and raw_text is not None:
         with col1:
             user_question = st.text_input("Ask a question from the Docs")
             if user_question:
                 user_input(user_question, api_key)
-
+                raw_text = get_pdf_text(pdf_docs)
+    # Show message if no PDF documents are uploaded
+    if pdf_docs is None:
+        with col1:
+            st.write("Please upload a document first to proceed.")
+            
     # Display extracted text if available
-    if session_state.raw_text is not None:
+    if raw_text is not None:
         with col2:
             st.subheader("Extracted Text from PDF:")
-            st.text(session_state.raw_text)
+            st.text(raw_text)
+
 
-    # Show message if no PDF documents are uploaded
-    if not session_state.pdf_docs:
-        with col1:
-            st.write("Please upload a document first to proceed.")
 
 if __name__ == "__main__":
     main()