Creat app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+import io
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+from langchain import HuggingFaceHub
+import boto3
+from botocore.config import Config
+from st_files_connection import FilesConnection
+load_dotenv()
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+bucket_name = "chatbot-resume"
+
+def get_pdf_text_from_s3(bucket_name, pdf_keys):
+    s3 = boto3.client('s3', config=Config(signature_version='s3v4'))
+    text = ""
+    for pdf_key in pdf_keys:
+        response = s3.get_object(Bucket=bucket_name, Key=pdf_key)
+        pdf_data = response['Body'].read()
+        pdf_file = io.BytesIO(pdf_data)
+        pdf_reader = PdfReader(pdf_file)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+    provided context just say, "answer is not available in the context", don't provide the wrong answer, try to convince that skillset is amazing and promising as this is intended for recruiters, also the number format mm/yyyy - mm/yyyy
+    is for start and end date of university or work, BS refers to Bachelors degree.\n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+    chain = get_conversational_chain()
+    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+    st.write("Reply: ", response["output_text"])
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Chat with Narek's Resume(Google Gemini)")
+    conn = st.connection('s3', type=FilesConnection)
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        pdf_keys = []  # Initialize an empty list to store PDF file keys
+        s3 = boto3.client('s3')
+        paginator = s3.get_paginator('list_objects_v2')
+        for result in paginator.paginate(Bucket=bucket_name):
+            if 'Contents' in result:
+                for item in result['Contents']:
+                    if item['Key'].endswith('.pdf'):  # Check if the object is a PDF file
+                        pdf_keys.append(item['Key'])  # Add the PDF file key to the list
+        raw_text = get_pdf_text_from_s3(bucket_name, pdf_keys)
+        text_chunks = get_text_chunks(raw_text)
+        get_vector_store(text_chunks)
+        user_input(user_question)
+
+    with st.sidebar:
+        st.title("Menu:")
+        st.write("Please wait while PDF files are fetched from S3...")
+
+if __name__ == "__main__":
+    main()