mkdir chat-with-pdf cd chat-with-pdf python3 -m venv venv source venv/bin/activate pip install streamlit openai requests PyPDF2 

PINATA_API_KEY=<Your Pinata API Key> PINATA_SECRET_API_KEY=<Your Pinata Secret Key> OPENAI_API_KEY=<Your OpenAI API Key> 

OPENAI_API_KEY=<Your OpenAI API Key> PINATA_API_KEY=dfc05775d0c8a1743247 PINATA_SECRET_API_KEY=a54a70cd227a85e68615a5682500d73e9a12cd211dfbf5e25179830dc8278efc  

import os  # Import the os module to interact with the operating system import requests  # Import the requests library to make HTTP requests from dotenv import load_dotenv  # Import load_dotenv to load environment variables from a .env file  # Load environment variables from the .env file load_dotenv()  # Define the Pinata API URL for pinning files to IPFS PINATA_API_URL = "https://api.pinata.cloud/pinning/pinFileToIPFS"  # Retrieve Pinata API keys from environment variables PINATA_API_KEY = os.getenv("PINATA_API_KEY") PINATA_SECRET_API_KEY = os.getenv("PINATA_SECRET_API_KEY")  def upload_pdf_to_pinata(file_path):     """     Uploads a PDF file to Pinata's IPFS service.      Args:         file_path (str): The path to the PDF file to be uploaded.      Returns:         str: The IPFS hash of the uploaded file if successful, None otherwise.     """     # Prepare headers for the API request with the Pinata API keys     headers = {         "pinata_api_key": PINATA_API_KEY,         "pinata_secret_api_key": PINATA_SECRET_API_KEY     }      # Open the file in binary read mode     with open(file_path, 'rb') as file:         # Send a POST request to Pinata API to upload the file         response = requests.post(PINATA_API_URL, files={'file': file}, headers=headers)          # Check if the request was successful (status code 200)         if response.status_code == 200:             print("File uploaded successfully")  # Print success message             # Return the IPFS hash from the response JSON             return response.json()['IpfsHash']         else:             # Print an error message if the upload failed             print(f"Error: {response.text}")             return None  # Return None to indicate failure  

import os from openai import OpenAI from dotenv import load_dotenv  # Load environment variables from .env file load_dotenv()  # Initialize OpenAI client with the API key OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") client = OpenAI(api_key=OPENAI_API_KEY)  def get_openai_response(text, pdf_text):     try:         # Create the chat completion request         print("User Input:", text)         print("PDF Content:", pdf_text)  # Optional: for debugging          # Combine the user's input and PDF content for context         messages = [             {"role": "system", "content": "You are a helpful assistant for answering questions about the PDF."},             {"role": "user", "content": pdf_text},  # Providing the PDF content             {"role": "user", "content": text}  # Providing the user question or request         ]          response = client.chat.completions.create(             model="gpt-4",  # Use "gpt-4" or "gpt-4o mini" based on your access             messages=messages,             max_tokens=100,  # Adjust as necessary             temperature=0.7  # Adjust to control response creativity         )          # Extract the content of the response         return response.choices[0].message.content  # Corrected access method     except Exception as e:         return f"Error: {str(e)}"  

import streamlit as st import os import time from pinata_helper import upload_pdf_to_pinata from openai_helper import get_openai_response from PyPDF2 import PdfReader from dotenv import load_dotenv  # Load environment variables load_dotenv()  st.set_page_config(page_title="Chat with PDFs", layout="centered")  st.title("Chat with PDFs using OpenAI and Pinata")  uploaded_file = st.file_uploader("Upload your PDF", type="pdf")  # Initialize session state for chat history and loading state if "chat_history" not in st.session_state:     st.session_state.chat_history = [] if "loading" not in st.session_state:     st.session_state.loading = False  if uploaded_file is not None:     # Save the uploaded file temporarily     file_path = os.path.join("temp", uploaded_file.name)     with open(file_path, "wb") as f:         f.write(uploaded_file.getbuffer())      # Upload PDF to Pinata     st.write("Uploading PDF to Pinata...")     pdf_cid = upload_pdf_to_pinata(file_path)      if pdf_cid:         st.write(f"File uploaded to IPFS with CID: {pdf_cid}")          # Extract PDF content         reader = PdfReader(file_path)         pdf_text = ""         for page in reader.pages:             pdf_text += page.extract_text()          if pdf_text:             st.text_area("PDF Content", pdf_text, height=200)              # Allow user to ask questions about the PDF             user_input = st.text_input("Ask something about the PDF:", disabled=st.session_state.loading)              if st.button("Send", disabled=st.session_state.loading):                 if user_input:                     # Set loading state to True                     st.session_state.loading = True                      # Display loading indicator                     with st.spinner("AI is thinking..."):                         # Simulate loading with sleep (remove in production)                         time.sleep(1)  # Simulate network delay                         # Get AI response                         response = get_openai_response(user_input, pdf_text)                      # Update chat history                     st.session_state.chat_history.append({"user": user_input, "ai": response})                      # Clear the input box after sending                     st.session_state.input_text = ""                      # Reset loading state                     st.session_state.loading = False              # Display chat history             if st.session_state.chat_history:                 for chat in st.session_state.chat_history:                     st.write(f"**You:** {chat['user']}")                     st.write(f"**AI:** {chat['ai']}")                  # Auto-scroll to the bottom of the chat                 st.write("<style>div.stChat {overflow-y: auto;}</style>", unsafe_allow_html=True)                  # Add three dots as a loading indicator if still waiting for response                 if st.session_state.loading:                     st.write("**AI is typing** ...")          else:             st.error("Could not extract text from the PDF.")     else:         st.error("Failed to upload PDF to Pinata.")  

streamlit run app.py