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# pdf_airavata_qa.py
import gradio as gr
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import requests
import os
# ----------------------------
# 1. Load and split PDF
# ----------------------------
def load_and_chunk(pdf_path):
loader = PyPDFLoader(pdf_path)
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)
texts = [c.page_content for c in chunks]
return texts
# ----------------------------
# 2. Build embedding index
# ----------------------------
def build_index(texts):
embed_model = SentenceTransformer('all-mpnet-base-v2') # You can choose another model
embeddings = embed_model.encode(texts, convert_to_numpy=True)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)
return index, embeddings
# ----------------------------
# 3. Airavata API call
# ----------------------------
def call_airavata(prompt):
# Example: Using HuggingFace Inference API (replace with your key or local endpoint)
API_URL = "https://api-inference.huggingface.co/models/ai4bharat/airavata" # Check actual endpoint
API_TOKEN = os.environ.get("HF_API_TOKEN") # Set your token in environment
headers = {"Authorization": f"Bearer {API_TOKEN}"}
payload = {"inputs": prompt}
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 200:
result = response.json()
return result[0]['generated_text']
else:
return f"Error: {response.status_code} - {response.text}"
# ----------------------------
# 4. PDF Q&A function
# ----------------------------
texts, index = [], None
def qa(pdf_file, question):
global texts, index
if pdf_file is not None:
# Load PDF and build index
texts = load_and_chunk(pdf_file.name)
index, _ = build_index(texts)
if not texts:
return "Please upload a PDF first."
# Embed the question
embed_model = SentenceTransformer('all-mpnet-base-v2')
q_emb = embed_model.encode([question], convert_to_numpy=True)
# Retrieve top 5 relevant chunks
D, I = index.search(q_emb, k=5)
context = "\n\n".join([texts[i] for i in I[0]])
# Build prompt for Airavata
prompt = f"""
You are an AI assistant. Use the following document context to answer the question.
Context:
{context}
Question: {question}
Answer:
"""
# Get answer from Airavata
answer = call_airavata(prompt)
return answer
# ----------------------------
# 5. Gradio Interface
# ----------------------------
demo = gr.Interface(
fn=qa,
inputs=[
gr.File(label="Upload PDF"),
gr.Textbox(label="Ask your question", placeholder="Type a question about the PDF...")
],
outputs=gr.Textbox(label="Answer"),
title="PDF Q&A with Airavata",
description="Upload a PDF and ask questions. Airavata will answer based on the document."
)
demo.launch()
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