Chat with your PDF using Pinata,OpenAI and Streamlit

In this tutorial, we’ll build a simple chat interface that allows users to upload a PDF, retrieve its content using OpenAI’s API, and display the responses in a chat-like interface using Streamlit. We will also leverage @pinata to upload and store the PDF files.

Let's have a little glance at what we are building before moving forward:

Prerequisites :

• Basic knowledge of Python
• Pinata API key (for uploading PDFs)
• OpenAI API key (for generating responses)
• Streamlit installed (for building the UI)

Step 1: Project Setup

Start by creating a new Python project directory:

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

Now, create a .env file in the root of your project and add the following environment variables:

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

One have to manage OPENAI_API_KEY by own as it's paid.But let's go through the process of creating api keys in Pinita.

So, before proceeding further let us know what Pinata is why we are using it.

Pinata is a service that provides a platform for storing and managing files on IPFS (InterPlanetary File System), a decentralized and distributed file storage system.

• Decentralized Storage: Pinata helps you store files on IPFS, a decentralized network.
• Easy to Use: It provides user-friendly tools and APIs for file management.
• File Availability: Pinata keeps your files accessible by "pinning" them on IPFS.
• NFT Support: It's great for storing metadata for NFTs and Web3 apps.
• Cost-Effective: Pinata can be a cheaper alternative to traditional cloud storage.

Let's create required tokens by signin:

Next step is to verify your registered email :

After verifying signin to generate api keys :

After that go to API Key Section and Create New API Keys:

Finally, keys are successfully generated.Please copy that keys and save it in your code editor.

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

Step 2: PDF Upload using Pinata

We’ll use Pinata’s API to upload PDFs and get a hash (CID) for each file. Create a file named pinata_helper.py to handle the PDF upload.

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

Step 3: Setting up OpenAI
Next, we’ll create a function that uses the OpenAI API to interact with the text extracted from the PDF. We’ll leverage OpenAI’s gpt-4o or gpt-4o-mini model for chat responses.

Create a new file openai_helper.py:

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)}"

Step 4: Building the Streamlit Interface

Now that we have our helper functions ready, it’s time to build the Streamlit app that will upload PDFs, fetch responses from OpenAI, and display the chat.

Create a file named app.py:

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.")

Step 5: Running the App

To run the app locally, use the following command:

streamlit run app.py

Our file is successfully uploaded in Pinata Platform :

Step 6: Explaining the Code

Pinata Upload

• The user uploads a PDF file, which is temporarily saved locally and uploaded to Pinata using the upload_pdf_to_pinata function. Pinata returns a hash (CID), which represents the file stored on IPFS.

PDF Extraction

• Once the file is uploaded, the content of the PDF is extracted using PyPDF2. This text is then displayed in a text area.

OpenAI Interaction

• The user can ask questions about the PDF content using the text input. The get_openai_response function sends the user’s query along with the PDF content to OpenAI, which returns a relevant response.

Final code is available in this github repo :
https://github.com/Jagroop2001/chat-with-pdf

That's all for this blog! Stay tuned for more updates and keep building amazing apps! 💻✨
Happy coding! 😊

Comments

[john]

@jagroop2001 ,
Wow, I didn't know we could create such an AI project so easily! Integrating Pinata, OpenAI, and Streamlit opens up so many possibilities for building interactive applications.
I would try with image to text generation. Can you suggest is it feasible ?

[Jagroop Singh]

@john12 ,
Creating an image-to-text generation application using OpenAI's models is definitely possible! The advanced features of models like GPT-4 and DALL-E make this a realistic and exciting project.

[john]

@jagroop2001 ,Thank you so much! 🙌✨ I'm really excited about this project! 😊

[Jagroop Singh]

@john12 , let me know if you need any help in your project.

[john]

@jagroop2001 , sure thanks

[Web]

@jagroop2001
Whoa! Fantastic Project using OpenAI and Pinata. I've tried this and it works well.
Your API keys aren't functioning, by the way. I attempted to utilize this

[Jagroop Singh]

@works ,
yes because I have shown this for demo purpose after that I delete the @pinata keys and regenerated new ones.

[Web]

@jagroop2001 , got it.
Can you guide me that how I would build a platform like that where code file uploaded and OpenAI generate code review of it and also provide optimized code correction.

Is this possible with OPEN AI

[Jagroop Singh]

@works ,
Yes, it's possible to build a platform that allows code file uploads, with OpenAI generating code reviews and offering optimised corrections. You can achieve this by integrating OpenAI's API or Gemini API ( which is free) or Open Source Model for code analysis and Pinata for secure file storage, all within a React-based front-end.

I'm already working on this exact problem statement and plan to publish the project within a few days, using Pinata, OpenAI, React, and other technologies.

[Web]

Wow , I will be waiting for this as this would really help me to learn with your code refence. @jagroop2001

[Martin Baun]

I highly recommend you take a look at LangChain :)

[Jagroop Singh]

Sure @martinbaun , any resources ??

[Jagroop Singh]

@martinbaun , are you pointing to built this project using Langchain using RAG's ?

[Dark Coder]

there is a project called repochat that is for the same purpose. still great read!

[Jagroop Singh]

@dark_coder_vibes ,
Thank you for the heads-up! I appreciate the mention of repochat—it’s always interesting to see different approaches to similar goals. Glad you enjoyed the read!

[caga]

Sounds interesting @jagroop2001 ,
Why Pinata when we can store that in any 3rd party bucket or even in backend public folder ?

[Jagroop Singh]

@paxnw ,
@pinata is great because it leverages IPFS, giving files a decentralized home that's secure, accessible.
Unlike a typical backend folder or cloud storage, IPFS ensures that files are immutable and distributed, reducing dependency on any single server.
This can boost performance, especially for apps that need reliable file access across multiple locations. Plus, Pinata’s API makes integration and file management a breeze!