Update app.py

app.py

@@ -1,69 +1,69 @@
-import streamlit as st
-import pdfplumber
-import faiss
-import torch
-import numpy as np
-from sentence_transformers import SentenceTransformer
-from transformers import pipeline
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-# Load embedding model
-embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
-qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
-
-# Function to extract text from PDF
-def extract_text_from_pdf(pdf_file):
-    text = ""
-    with pdfplumber.open(pdf_file) as pdf:
-        for page in pdf.pages:
-            text += page.extract_text() + "\n"
-    return text.strip()
-
-# Chunking text
-def chunk_text(text, chunk_size=500, overlap=100):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
-    return splitter.split_text(text)
-
-# Generate embeddings
-def generate_embeddings(text_chunks):
-    return embedding_model.encode(text_chunks, convert_to_numpy=True)
-
-# Create FAISS index
-def create_faiss_index(embeddings):
-    dimension = embeddings.shape[1]
-    index = faiss.IndexFlatL2(dimension)
-    index.add(embeddings)
-    return index
-
-# Retrieve relevant context
-def retrieve_context(query, index, text_chunks, top_k=3):
-    query_embedding = embedding_model.encode([query], convert_to_numpy=True)
-    distances, indices = index.search(query_embedding, top_k)
-    retrieved_text = "\n".join([text_chunks[i] for i in indices[0]])
-    return retrieved_text
-
-# Generate Answer
-def answer_question(query, faiss_index, book_chunks):
-    context = retrieve_context(query, faiss_index, book_chunks)
-    result = qa_pipeline(question=query, context=context)
-    return result["answer"]
-
-# Streamlit UI
-st.title("📖 Book-Based Question Answering System")
-st.write("Upload a book (PDF) and ask any question!")
-
-# File uploader
-uploaded_file = st.file_uploader("Upload a PDF book", type="pdf")
-
-if uploaded_file:
-    st.write("Processing book...")
-    book_text = extract_text_from_pdf(uploaded_file)
-    book_chunks = chunk_text(book_text)
-    chunk_embeddings = generate_embeddings(book_chunks)
-    faiss_index = create_faiss_index(chunk_embeddings)
-    st.success(f"Book processed successfully! ({len(book_chunks)} chunks)")
-
-    query = st.text_input("Ask a question based on the book:")
-    if query:
-        answer = answer_question(query, faiss_index, book_chunks)
-        st.write(f"**Answer:** {answer}")
+import streamlit as st
+import pdfplumber
+import faiss
+import torch
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+# Load embedding model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    with pdfplumber.open(pdf_file) as pdf:
+        for page in pdf.pages:
+            text += page.extract_text() + "\n"
+    return text.strip()
+
+# Chunking text
+def chunk_text(text, chunk_size=500, overlap=100):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
+    return splitter.split_text(text)
+
+# Generate embeddings
+def generate_embeddings(text_chunks):
+    return embedding_model.encode(text_chunks, convert_to_numpy=True)
+
+# Create FAISS index
+def create_faiss_index(embeddings):
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+    return index
+
+# Retrieve relevant context (Increased context size)
+def retrieve_context(query, index, text_chunks, top_k=7):
+    query_embedding = embedding_model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_embedding, top_k)
+    retrieved_text = "\n".join([text_chunks[i] for i in indices[0]])
+    return retrieved_text
+
+# Generate Answer (Allow longer answers)
+def answer_question(query, faiss_index, book_chunks):
+    context = retrieve_context(query, faiss_index, book_chunks)
+    result = qa_pipeline(question=query, context=context, max_answer_len=150)  
+    return result["answer"] + "\n\n**Additional Context:** " + context[:400] + "..."  
+
+# Streamlit UI
+st.title("📖 Book-Based Question Answering System")
+st.write("Upload a book (PDF) and ask any question!")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a PDF book", type="pdf")
+
+if uploaded_file:
+    st.write("Processing book...")
+    book_text = extract_text_from_pdf(uploaded_file)
+    book_chunks = chunk_text(book_text)
+    chunk_embeddings = generate_embeddings(book_chunks)
+    faiss_index = create_faiss_index(chunk_embeddings)
+    st.success(f"Book processed successfully! ({len(book_chunks)} chunks)")
+
+    query = st.text_input("Ask a question based on the book:")
+    if query:
+        answer = answer_question(query, faiss_index, book_chunks)
+        st.write(f"**Answer:** {answer}")