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surajintellact/
chat_neutrino

Name: chat_neutrino
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import requests from io import BytesIO from PyPDF2 import PdfReader from langchain.text_splitter import RecursiveCharacterTextSplitter import os 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 flask import Flask, request, jsonify from flask_cors import CORS

load_dotenv() os.getenv("GOOGLE_API_KEY") genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

app = Flask(name) CORS(app)

def get_pdf_text(pdf_docs): text="" for pdf in pdf_docs: pdf_reader= PdfReader(pdf) 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\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)
docs = new_db.similarity_search(user_question)

chain = get_conversational_chain()


response = chain(
    {"input_documents":docs, "question": user_question}
    , return_only_outputs=True)

print(response)
# st.write("Reply: ", response["output_text"])
return response["output_text"]

@app.route('/chat', methods=['POST']) def chat(): data = request.json user_question = data.get('message')

pdf_url = "https://unec.edu.az/application/uploads/2014/12/pdf-sample.pdf"

# Download the PDF file
response = requests.get(pdf_url)
if response.status_code != 200:
    return jsonify({"status": "error", "message": f"Failed to download PDF from URL: {pdf_url}"}), 404

# Read the downloaded PDF content
pdf_content = BytesIO(response.content)

# Process the PDF content
raw_text = get_pdf_text([pdf_content])
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)

# Get the response
response_text = user_input(user_question)

return jsonify({"response": response_text})

if name == "main": app.run(debug=True)

surajintellact/chat_neutrino

surajintellact/
chat_neutrino