Create app.py

app.py

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+import fitz  # PyMuPDF
+from transformers import DPRQuestionEncoderTokenizer, DPRQuestionEncoder
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import json
+import faiss
+import numpy as np
+import streamlit as st
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    document = fitz.open(pdf_path)
+    text = ""
+    for page_num in range(document.page_count):
+        page = document.load_page(page_num)
+        text += page.get_text("text")
+    return text
+
+# Function to chunk text into smaller segments
+def chunk_text(text, chunk_size=1000):
+    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+
+# Initialize models
+retriever_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
+retriever = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
+generator_tokenizer = T5Tokenizer.from_pretrained('t5-base')
+generator = T5ForConditionalGeneration.from_pretrained('t5-base')
+
+# Index chunks using FAISS
+def index_chunks(chunks):
+    index = faiss.IndexFlatL2(768)  # Assuming 768-dimensional embeddings
+    chunk_embeddings = []
+    for chunk in chunks:
+        inputs = retriever_tokenizer(chunk, return_tensors='pt', padding=True, truncation=True)
+        chunk_embedding = retriever(**inputs).pooler_output.detach().numpy()
+        chunk_embeddings.append(chunk_embedding)
+    chunk_embeddings = np.vstack(chunk_embeddings)
+    index.add(chunk_embeddings)
+    return index, chunk_embeddings
+
+# Function to get answer to a query
+def get_answer(query, chunks, index, chunk_embeddings, max_length=50):
+    # Encode query using retriever
+    inputs = retriever_tokenizer(query, return_tensors='pt')
+    question_embedding = retriever(**inputs).pooler_output.detach().numpy()
+    
+    # Search for the most relevant chunk
+    distances, indices = index.search(question_embedding, 1)
+    retrieved_chunk = chunks[indices[0][0]]
+    
+    # Generate answer using generator
+    input_ids = generator_tokenizer(retrieved_chunk, return_tensors='pt').input_ids
+    output_ids = generator.generate(input_ids, max_length=max_length)
+    answer = generator_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    return answer
+
+# Load and process PDF
+pdf_text = extract_text_from_pdf('policy-booklet-0923.pdf')
+chunks = chunk_text(pdf_text)
+index, chunk_embeddings = index_chunks(chunks)
+
+# Streamlit front-end
+st.title("RAG-Powered PDF Chatbot")
+
+user_query = st.text_input("Enter your question:")
+if user_query:
+    answer = get_answer(user_query, chunks, index, chunk_embeddings, max_length=100)
+    st.write("Answer:", answer)