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| import gradio as gr | |
| from openai import OpenAI | |
| from PyPDF2 import PdfReader | |
| import os | |
| import numpy as np | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| # Set up OpenAI API key | |
| api_key = os.getenv("OPENAI_API_KEY") # Make sure to have your OpenAI API key in .env | |
| client = OpenAI(api_key=api_key) | |
| # Set OpenAI API key | |
| # Function to extract text from PDF | |
| def extract_pdf_text(pdf_file): | |
| reader = PdfReader(pdf_file) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| return text | |
| # Function to split text into chunks | |
| def split_text(text, chunk_size=500, overlap=50): | |
| chunks = [] | |
| for i in range(0, len(text), chunk_size - overlap): | |
| chunks.append(text[i:i + chunk_size]) | |
| return chunks | |
| # Function to generate embeddings | |
| def get_embeddings(text): | |
| response = client.embeddings.create( | |
| input=text, | |
| model="text-embedding-3-small" | |
| ) | |
| return np.array(response.data[0].embedding) | |
| # Function to retrieve relevant chunks | |
| def retrieve_relevant_chunks(query, chunks, chunk_embeddings): | |
| query_embedding = get_embeddings(query) | |
| similarities = cosine_similarity([query_embedding], chunk_embeddings)[0] | |
| top_indices = np.argsort(similarities)[::-1][:3] # Get top 3 relevant chunks | |
| return [chunks[i] for i in top_indices] #we use the chunks list to retrieve the actual text of those relevant chunks. These text chunks will form the context. | |
| # Function to generate a response | |
| def generate_response(context, query): | |
| messages = [ | |
| {"role": "system", "content": "You are an assistant that answers questions based on the provided context in 30 words."}, | |
| {"role": "user", "content": f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"} | |
| ] | |
| response = client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=messages, | |
| max_tokens=300 | |
| ) | |
| return response.choices[0].message.content | |
| # Global variables to store chunks and embeddings | |
| chunks = [] | |
| chunk_embeddings = [] | |
| # Gradio interface functions | |
| def process_pdf(pdf_file): | |
| global chunks, chunk_embeddings | |
| text = extract_pdf_text(pdf_file) | |
| chunks = split_text(text) | |
| chunk_embeddings = [get_embeddings(chunk) for chunk in chunks] | |
| return "PDF processed successfully! You can now chat with it." | |
| def chat_with_pdf(query): | |
| global chunks, chunk_embeddings | |
| if not chunks or not chunk_embeddings: | |
| return "Please upload and process a PDF first." | |
| relevant_chunks = retrieve_relevant_chunks(query, chunks, chunk_embeddings) | |
| context = "\n".join(relevant_chunks) | |
| return generate_response(context, query) | |
| # Gradio app | |
| with gr.Blocks() as app: | |
| gr.Markdown("# Chat with Your PDF 📄🤖") | |
| pdf_file = gr.File(label="Upload PDF", file_types=[".pdf"]) | |
| process_button = gr.Button("Process PDF") | |
| process_status = gr.Textbox(label="Status", interactive=False) | |
| query = gr.Textbox(label="Ask a Question") | |
| chat_button = gr.Button("Chat with PDF") | |
| response = gr.Textbox(label="Response", interactive=False) | |
| process_button.click(process_pdf, inputs=pdf_file, outputs=process_status) | |
| chat_button.click(chat_with_pdf, inputs=query, outputs=response) | |
| app.launch() | |