Create app.py

app.py

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+import streamlit as st
+import os
+from tempfile import NamedTemporaryFile
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+# Function to save the uploaded PDF to a temporary file
+def save_uploaded_file(uploaded_file):
+    with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+        temp_file.write(uploaded_file.read())
+        return temp_file.name
+
+# Function to get answers from the PDF
+def get_answer(question, db, model, tokenizer):
+    doc = db.similarity_search(question, k=4)
+    context = doc[0].page_content + doc[1].page_content + doc[2].page_content + doc[3].page_content
+
+    # Load the model & tokenizer for question-answering
+    model_name = "deepset/roberta-base-squad2"
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # Create a question-answering pipeline
+    nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+    # Prepare the input
+    QA_input = {
+        "question": question,
+        "context": context,
+    }
+
+    # Get the answer
+    result = nlp(**QA_input)
+
+    return result["answer"]
+
+# Streamlit UI
+st.title("PDF Question Answering App")
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+if uploaded_file is not None:
+    # Save the uploaded file to a temporary location
+    temp_file_path = save_uploaded_file(uploaded_file)
+
+    # Load the PDF document using PyPDFLoader
+    loader = PyPDFLoader(temp_file_path)
+    pages = loader.load_and_split()
+
+    # Initialize embeddings and Chroma
+    embed = HuggingFaceEmbeddings()
+    db = Chroma.from_documents(pages, embed)
+
+    # Load the model & tokenizer for question-answering
+    model_name = "deepset/roberta-base-squad2"
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # Initializations
+    conversation = []
+    st.write("Ask your questions, and I'll provide answers:")
+    
+    # Continuous question-answering loop
+    while True:
+        question = st.text_input("Enter your question:")
+        if st.button("Get Answer"):
+            answer = get_answer(question, db, model, tokenizer)
+            st.write("Answer:")
+            st.write(answer)
+            conversation.append({"question": question, "answer": answer})
+        
+        # Add an option to end the conversation
+        if st.button("End Conversation"):
+            break
+
+    # Display the conversation history
+    st.write("Conversation History:")
+    for entry in conversation:
+        st.write(f"Q: {entry['question']}")
+        st.write(f"A: {entry['answer']}")
+
+    # Cleanup: Delete the temporary file
+    os.remove(temp_file_path)