Update app.py

app.py

@@ -1,11 +1,8 @@
 import streamlit as st
 import os
-import base64
-import time
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from transformers import pipeline
 import torch
-import textwrap
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import Chroma
 from langchain.llms import HuggingFacePipeline
@@ -27,10 +24,7 @@ base_model = AutoModelForSeq2SeqLM.from_pretrained(
 persist_directory = "db"
 
 @st.cache_resource
-def data_ingestion(uploaded_file):
-    with open(uploaded_file.name, "rb") as pdf_file:
-        pdf_content = pdf_file.read()
-
+def data_ingestion(pdf_content):
     # Process the PDF content here and generate a brief summary.
     # You can use libraries like PyPDF2, pdfminer, or other PDF processing tools.
 
@@ -76,58 +70,29 @@ def process_answer(instruction):
     answer = generated_text['result']
     return answer
 
-def get_file_size(file):
-    file.seek(0, os.SEEK_END)
-    file_size = file.tell()
-    file.seek(0)
-    return file_size
-
-@st.cache_data
-# Function to display the PDF of a given file
-def displayPDF(file):
-    # Opening file from file path
-    with open(file, "rb") as f:
-        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+def main():
+    st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 🦜📄 </h1>", unsafe_allow_html=True)
+    st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/manas95826'>The Manas with ❤️ </a></h3>", unsafe_allow_html=True)
 
-    # Embedding PDF in HTML
-    pdf_display = F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+    st.markdown("<h2 style='text-align: center; color:red;'>Ask Questions about your PDF 👇</h2>", unsafe_allow_html=True)
 
-    # Displaying File
-    st.markdown(pdf_display, unsafe_allow_html=True)
+    user_input = st.text_input("Ask a question:", key="input")
 
-# Display conversation history using Streamlit messages
-def display_conversation(history):
-    for i in range(len(history["generated"])):
-        message(history["past"][i], is_user=True, key=str(i) + "_user")
-        message(history["generated"][i], key=str(i))
+    # Initialize session state for generated responses
+    if "generated" not in st.session_state:
+        st.session_state["generated"] = []
 
-def main():
-    st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 🦜📄 </h1>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/manas95826'>The Manas with ❤️ </a></h3>", unsafe_allow_html=True)
+    # Search the database for a response based on user input and update session state
+    if user_input:
+        answer = process_answer({'query': user_input})
+        st.session_state["generated"].append(answer)
 
-    st.markdown("<h2 style='text-align: center; color:red;'>Upload your PDF 👇</h2>", unsafe_allow_html=True)
-
-    uploaded_file = st.file_uploader("", type=["pdf"])
-
-    if uploaded_file is not None:
-        file_details = {
-            "Filename": uploaded_file.name,
-            "File size": get_file_size(uploaded_file)
-        }
-
-        col1, col2 = st.columns([1, 2])
-        with col1:
-            st.markdown("<h4 style color:black;'>File details</h4>", unsafe_allow_html=True)
-            st.json(file_details)
-            st.markdown("<h4 style color:black;'>File preview</h4>", unsafe_allow_html=True)
-            pdf_view = displayPDF(uploaded_file)
-
-        with col2:
-            with st.spinner('Processing the uploaded PDF...'):
-                pdf_summary = data_ingestion(uploaded_file)
-            st.success('PDF processing is complete!')
-            st.markdown("<h4 style color:black;'>PDF Summary</h4>", unsafe_allow_html=True)
-            st.write(pdf_summary)
+    # Display conversation history using Streamlit messages
+    if st.session_state["generated"]:
+        st.markdown("<h4>Conversation History:</h4>", unsafe_allow_html=True)
+        for i, response in enumerate(st.session_state["generated"]):
+            st.text(f"Q{i+1}: {user_input}")
+            st.text(f"A{i+1}: {response}")
 
 if __name__ == "__main__":
     main()