Update app.py

app.py

@@ -1,166 +1,53 @@
-# Import necessary libraries
 import streamlit as st
 from langchain_community.document_loaders import PyPDFLoader
 import openai
 from langchain.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from langchain.chat_models import ChatOpenAI
-from fpdf import FPDF
-import os
+import pandas as pd
 from datetime import datetime, timedelta
 
-# Set up Streamlit UI
 st.title('Educational Assistant')
 st.header('Summary, Quiz Generator, Q&A, and Topics to be Covered')
 st.sidebar.title('Drop your PDF here')
 
-# Input OpenAI API key from keyboard
 openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
-
 user_file_upload = st.sidebar.file_uploader(label='', type='pdf')
-
-# Sidebar option selection for Summary, Quiz, Q&A, or Topics to be Covered
 option = st.sidebar.radio("Choose an option", ('Generate Summary', 'Generate Quiz', 'Ask a Question', 'Topics to be Covered'))
 
-# Input for asking questions (only visible when "Ask a Question" is selected)
-question_input = None
-if option == 'Ask a Question':
-    question_input = st.text_input("Enter your question about the document:")
-
-# Function to generate a PDF and allow download
-def generate_pdf(response, filename="response.pdf"):
-    pdf = FPDF()
-    pdf.add_page()
-    
-    # Adding a Unicode-compatible font (like Arial Unicode MS or other compatible font)
-    pdf.add_font('ArialUnicode', '', 'arialuni.ttf', uni=True)  # Path to font, make sure this is correct for your system
-    pdf.set_font('ArialUnicode', '', 12)
-    
-    # Add the response text
-    pdf.multi_cell(0, 10, response)
-    
-    # Save to a temporary file
-    pdf.output(filename)
-    
-    # Return the file path
-    return filename
-
 if openai_api_key:
-    # Set OpenAI API key
     openai.api_key = openai_api_key
 
     if user_file_upload:
-        # Read the uploaded file
         pdf_data = user_file_upload.read()
-
-        # Save the uploaded file to a temporary location
         with open("temp_pdf_file.pdf", "wb") as f:
             f.write(pdf_data)
-
-        # Load the temporary PDF file
         loader = PyPDFLoader("temp_pdf_file.pdf")
         data = loader.load_and_split()
 
-        ## Prompt Template for Summary
-        prompt_1 = ChatPromptTemplate.from_messages(
-            [
-                ("system", "You are a smart assistant. Give a summary of the user's PDF. Be polite."),
-                ("user", "{data}")
-            ]
-        )
-
-        # Pass the OpenAI API key explicitly to the ChatOpenAI instance
-        llm_summary = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key)  # Pass the key here
-        output_parser = StrOutputParser()
-        chain_1 = prompt_1 | llm_summary | output_parser
-
-        ## Prompt Template for Quiz
-        prompt_2 = ChatPromptTemplate.from_messages(
-            [
-                ("system", "You are a smart assistant. Generate 10 multiple-choice quiz questions with 4 options each (including correct and incorrect options) from the user's PDF. Please also include the correct answer in your response. Be polite."),
-                ("user", "{data}")
-            ]
-        )
-
-        # Pass the OpenAI API key explicitly to the ChatOpenAI instance
-        llm_quiz = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key)  # Pass the key here
-        output_parser = StrOutputParser()
-        chain_2 = prompt_2 | llm_quiz | output_parser
-
-        ## Prompt Template for Question-Answering
-        prompt_3 = ChatPromptTemplate.from_messages(
-            [
-                ("system", "You are a smart assistant. Answer the user's question based on the content of the PDF. Be polite."),
-                ("user", "{data}\n\nUser's question: {question}")
-            ]
-        )
-
-        # Pass the OpenAI API key explicitly to the ChatOpenAI instance
-        llm_qa = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key)  # Pass the key here
-        output_parser = StrOutputParser()
-        chain_3 = prompt_3 | llm_qa | output_parser
-
-        ## Prompt Template for Topics to be Covered
         prompt_4 = ChatPromptTemplate.from_messages(
             [
-                ("system", "You are a smart assistant. Analyze the user's PDF and generate 7 topics based on the content for the next 7 days. Be polite."),
+                ("system", "You are a smart assistant. Analyze the user's PDF and generate 7 topics with detailed themes for the next 7 days. Output format: 'Day X: Task\tTheme'"),
                 ("user", "{data}")
             ]
         )
-
-        # Pass the OpenAI API key explicitly to the ChatOpenAI instance
-        llm_topics = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key)  # Pass the key here
+        llm_topics = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key)
         output_parser = StrOutputParser()
         chain_4 = prompt_4 | llm_topics | output_parser
 
-    if option == 'Generate Summary':
-        # Generate summary
-        summary_response = chain_1.invoke({'data': data})
-        st.write(summary_response)
-        
-        # Generate PDF for the summary and offer it as a download
-        pdf_filename = generate_pdf(summary_response, filename="summary_response.pdf")
-        st.download_button("Download Summary as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf")
-        
-    elif option == 'Generate Quiz':
-        # Generate quiz
-        quiz_response = chain_2.invoke({'data': data})
-        st.write(quiz_response)
-        
-        # Generate PDF for the quiz and offer it as a download
-        pdf_filename = generate_pdf(quiz_response, filename="quiz_response.pdf")
-        st.download_button("Download Quiz as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf")
-        
-    elif option == 'Ask a Question' and question_input:
-        # Add a "Generate Answer" button
-        generate_answer = st.button("Generate Answer")
-        
-        if generate_answer:
-            # Generate answer for the user's question
-            question_answer_response = chain_3.invoke({'data': data, 'question': question_input})
-            st.write(question_answer_response)
-            
-            # Generate PDF for the question answer and offer it as a download
-            pdf_filename = generate_pdf(question_answer_response, filename="question_answer_response.pdf")
-            st.download_button("Download Answer as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf")
-
-    elif option == 'Topics to be Covered':
-        # Generate topics for the next 7 days
-        topics_response = chain_4.invoke({'data': data})
-        topics = topics_response.split("\n")  # Split response into topics
-
-        # Get today's date and create a table for the topics for the next 7 days
-        start_date = datetime.today()
-        table_data = []
+        if option == 'Topics to be Covered':
+            topics_response = chain_4.invoke({'data': data})
+            topics_list = topics_response.split("\n")
+            start_date = datetime.today()
+            table_data = []
 
-        for i in range(7):
-            day_date = start_date + timedelta(days=i)
-            topic = topics[i] if i < len(topics) else "Topic not available"
-            table_data.append([day_date.strftime("%dth %b %Y"), topic])
+            for i in range(7):
+                day_date = start_date + timedelta(days=i)
+                topic_parts = topics_list[i].split('\t') if i < len(topics_list) else [f"Day {i+1}: you will figure out my llm program", "you will figure out my llm program"]
+                table_data.append([day_date.strftime("%d-%b-%y"), topic_parts[0], topic_parts[1]])
 
-        # Display the topics table
-        st.write("### Topics to be Covered in the Next 7 Days")
-        st.table(table_data)
-        
+            df = pd.DataFrame(table_data, columns=["Day", "Tasks", "Theme"])
+            st.write("### Topics to be Covered in the Next 7 Days")
+            st.table(df)
 else:
     st.sidebar.warning("Please enter your OpenAI API Key to proceed.")