Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+from sentence_transformers import SentenceTransformer
+import fitz  # PyMuPDF
+import os
+
+# Load the models
+summarization_model_name = 'facebook/bart-large-cnn'
+tokenizer = AutoTokenizer.from_pretrained(summarization_model_name)
+summarization_model = AutoModelForSeq2SeqLM.from_pretrained(summarization_model_name)
+
+qa_model_name = 'distilbert-base-uncased-distilled-squad'
+qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
+qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
+qa_pipeline = pipeline('question-answering', model=qa_model, tokenizer=qa_tokenizer)
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(file):
+    doc = fitz.open(file)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Function to summarize document
+def summarize_document(document):
+    inputs = tokenizer(document, return_tensors='pt', max_length=1024, truncation=True)
+    summary_ids = summarization_model.generate(inputs['input_ids'], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Streamlit app
+st.title("PDF Summarizer and Q&A")
+st.write("Upload a PDF file to get a summary and ask questions about the content.")
+
+uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+if uploaded_file is not None:
+    # Save the uploaded file to a temporary location
+    with open("temp.pdf", "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    # Extract text from the PDF
+    document_text = extract_text_from_pdf("temp.pdf")
+    
+    # Display the extracted text
+    st.write("Extracted Text:")
+    st.write(document_text)
+    
+    if st.button("Summarize"):
+        with st.spinner('Summarizing...'):
+            summary = summarize_document(document_text)
+            st.write("**Summary:**")
+            st.write(summary)
+    
+    question = st.text_input("Ask a question about the document")
+    
+    if st.button("Get Answer"):
+        if question:
+            with st.spinner('Generating answer...'):
+                answer = qa_pipeline({'question': question, 'context': document_text})
+                st.write("**Answer:**")
+                st.write(answer['answer'])
+        else:
+            st.write("Please enter a question.")
+
+# Remove temporary file after use
+if os.path.exists("temp.pdf"):
+    os.remove("temp.pdf")