Create app.py

app.py

@@ -0,0 +1,91 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+import os
+import pickle
+import streamlit as st
+from scan_pdf_parser import get_text_from_scanned_pdf
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.llms import GooglePalm
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from langchain.vectorstores import FAISS
+from langchain.docstore.document import Document
+
+llm = GooglePalm(temperature=0.9)
+
+st.title("Query PDF Tool")
+
+uploaded_file = st.file_uploader("Choose a PDF file")
+main_placeholder = st.empty()
+second_placeholder = st.empty()
+
+
+if uploaded_file:
+    if not os.path.exists(uploaded_file.name):
+        main_placeholder.text("Data Loading...Started...⌛⌛⌛")
+        with open(f'{uploaded_file.name}', 'wb') as f:
+            f.write(uploaded_file.getbuffer())
+
+        pdf_loader = PyPDFLoader(uploaded_file.name)
+        documents = pdf_loader.load()
+
+        raw_text = ''
+        for doc in documents:
+            raw_text += doc.page_content
+
+        if len(raw_text) < 10:
+            main_placeholder.text("It looks like Scanned PDF, No worries converting it...⌛⌛⌛")
+            raw_text = get_text_from_scanned_pdf(uploaded_file.name)
+
+        main_placeholder.text("Text Splitter...Started...✅✅✅")
+        text_splitter = RecursiveCharacterTextSplitter(
+            separators=['\n\n', '\n', '.', ','],
+            chunk_size=2000
+        )
+
+        texts = text_splitter.split_text(raw_text)
+        docs = [Document(page_content=t) for t in texts]
+
+        embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base")
+        main_placeholder.text("Embedding Vector Started Building...✅✅✅")
+        vectorstore = FAISS.from_documents(docs, embeddings)
+
+        # Save the FAISS index to a pickle file
+        with open(f'vector_store_{uploaded_file.name}.pkl', "wb") as f:
+            pickle.dump(vectorstore, f)
+
+    main_placeholder.text("Data Loading...Completed...✅✅✅")
+
+
+query = second_placeholder.text_input("Question:")
+if query:
+    if os.path.exists(f'vector_store_{uploaded_file.name}.pkl'):
+        with open(f'vector_store_{uploaded_file.name}.pkl', "rb") as f:
+            vector_store = pickle.load(f)
+
+        prompt_template = """
+            <context>
+            {context}
+            </context>
+            Question: {question}
+            Assistant:"""
+        prompt = PromptTemplate(
+            template=prompt_template, input_variables=["context", "question"]
+        )
+
+        chain = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 1}),
+            return_source_documents=True,
+            chain_type_kwargs={"prompt": prompt}
+        )
+
+        with st.spinner("Searching for the answer..."):
+            result = chain({"query": query})
+        st.header("Answer")
+        st.write(result["result"])
+