Create app.py

app.py

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+import streamlit as st
+import pdfplumber
+import openai
+from dotenv import load_dotenv
+import os
+
+load_dotenv()  # Load environment variables from .env file
+
+# Safe retrieval of API key from environment variables
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Streamlit UI setup for the application
+st.title("Advanced PDF-Based Application")
+st.markdown("Select the functionality you want to use from the sidebar.")
+
+# Sidebar for mode selection and file uploading
+with st.sidebar:
+    mode = st.radio("Choose a mode:", ["PDF Summarizer", "Question Answering"])
+    uploaded_files = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=['pdf'], on_change=lambda: st.experimental_rerun())
+
+# Initializing documents list
+documents = []
+
+# Progress bar for file processing
+if uploaded_files:
+    with st.spinner('Processing PDF files...'):
+        progress_bar = st.progress(0)
+        total_files = len(uploaded_files)
+        for i, uploaded_file in enumerate(uploaded_files):
+            with pdfplumber.open(uploaded_file) as pdf:
+                full_text = ""
+                for page in pdf.pages[:50]:  # Process each page up to a limit of 50 pages
+                    full_text += page.extract_text() or ""
+                documents.append(full_text)
+            progress_bar.progress((i + 1) / total_files)
+        st.success("PDFs processed successfully. Proceed based on the selected mode.")
+        progress_bar.empty()
+
+# Using tabs to separate features
+tab1, tab2 = st.tabs(["Question Answering", "PDF Summarizer"])
+
+with tab1:
+    if mode == "Question Answering":
+        question = st.text_input("Enter your question here:")
+        if question and documents:
+            combined_text = "\n".join(documents[:3])
+            messages = [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": question},
+                {"role": "system", "content": combined_text}
+            ]
+            response = openai.ChatCompletion.create(
+                model="gpt-4",
+                messages=messages,
+                max_tokens=500
+            )
+            st.write("Answer:", response.choices[0].message['content'])
+
+with tab2:
+    if mode == "PDF Summarizer" and documents:
+        summaries = []
+        for doc in documents[:3]:
+            messages = [
+                {"role": "system", "content": "You are a helpful assistant tasked to summarize documents."},
+                {"role": "user", "content": "Summarize the following text brifly:\n" + doc}
+            ]
+            response = openai.ChatCompletion.create(
+                model="gpt-4",
+                messages=messages,
+                max_tokens=1024
+            )
+            summaries.append(response.choices[0].message['content'].strip())
+        
+        for idx, summary in enumerate(summaries):
+            st.write(f"Summary {idx+1}:", summary)