app.py

import gradio as gr
#from langchain.document_loaders import UnstructuredPDFLoader
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
#from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
import pinecone
import requests
import sys
#from langchain.prompts.chat import (ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate)
from langchain.chains.question_answering import load_qa_chain
#from langchain.chains import RetrievalQA
from langchain import PromptTemplate
from langchain import HuggingFaceHub
from PyPDF2 import PdfReader
#from langchain.document_loaders import TextLoader
#from sentence_transformers.util import semantic_search
from pathlib import Path
from time import sleep
#import pandas as pd
#import torch
import os
import random
import string
from dotenv import load_dotenv
load_dotenv()

def generate_random_string(length):
    letters = string.ascii_lowercase
    return ''.join(random.choice(letters) for i in range(length))      
random_string = generate_random_string(10)

file_path = os.path.join(os.getcwd(), "valuation.pdf")
#loader = PyPDFLoader("60LEADERSONAI.pdf")
#loader = PyPDFLoader(file_path)
#data = loader.load()
#text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
#db_texts = text_splitter.split_documents(data)

data = PdfReader(file_path)
raw_text = ''
db_texts=''
for i, page in enumerate(data.pages):
    text = page.extract_text()
    if text:
        raw_text += text
        text_splitter = RecursiveCharacterTextSplitter(        
#            separator = "\n",
            chunk_size = 1000,
            chunk_overlap  = 100, #striding over the text
            length_function = len,
        )
        db_texts = text_splitter.split_text(raw_text)

class HFEmbeddings:
    def __init__(self, api_url, headers):
        self.api_url = api_url
        self.headers = headers

    def get_embeddings(self, texts):
        response = requests.post(self.api_url, headers=self.headers, json={"inputs": texts, "options": {"wait_for_model": True}})
        embeddings = response.json()
        return embeddings

    def embed_documents(self, texts):
        embeddings = self.get_embeddings(texts)
        return embeddings

    def __call__(self, texts):
        return self.embed_documents(texts)

HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
model_id = os.getenv('model_id')
hf_token = os.getenv('hf_token')
repo_id = os.getenv('repo_id')

api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
headers = {"Authorization": f"Bearer {hf_token}"}

hf_embeddings = HFEmbeddings(api_url, headers)

PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
PINECONE_ENVIRONMENT = os.getenv('PINECONE_ENVIRONMENT')
PINECONE_INDEX_NAME = os.getenv('PINECONE_INDEX_NAME')
print(PINECONE_INDEX_NAME)

pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
index_name = PINECONE_INDEX_NAME
#index_name = pinecone.Index(index_name)
print(index_name)
namespace = random_string
print(namespace)

vector_db = Pinecone.from_texts(db_texts, hf_embeddings, index_name=index_name, namespace=namespace)
#vector_db = Pinecone.from_texts([t.page_content for t in db_texts], hf_embeddings, index_name=index_name, namespace=namespace)
#docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name, namespace=namespace)
print("***********************************")
print("Pinecone Vector/Embedding DB Ready.")

index_name_extracted=pinecone.list_indexes()
print(index_name_extracted)
index_current = pinecone.Index(index_name=index_name_extracted)
index_status=index_current.describe_index_stats(index_current) 
print(index_status)
print(namespace)

llm = HuggingFaceHub(repo_id=repo_id,
                     model_kwargs={"min_length":100,
                                   "max_new_tokens":1024, "do_sample":True,
                                   "temperature":0.1,
                                   "top_k":50,
                                   "top_p":0.95, "eos_token_id":49155})

#prompt_template = """You are a very helpful AI assistant. Please ONLY use {context} to answer the user's input question. If you don't know the answer, just say that you don't know. DON'T try to make up an answer and do NOT go beyond the given context without the user's explicitly asking you to do so!
#Question: {question}
#Helpful AI Repsonse:
#"""

prompt_template = """You are a very helpful AI assistant. Please ONLY use the givens context to answer the user's input question. If you don't know the answer, just say that you don't know.
Context: {context}
Question: {question}
Helpful AI Repsonse:
"""

PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])

chain = load_qa_chain(llm=llm, chain_type="stuff", prompt=PROMPT)

#chain = load_qa_chain(llm=llm, chain_type="stuff")

def run_chain(user_query):
    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
    index_name_extracted=pinecone.list_indexes()
    index_current = pinecone.Index(index_name=index_name_extracted)
    index_status=index_current.describe_index_stats(index_current) 
    if user_query !="" and not user_query.strip().isspace() and not user_query.isspace():
      print("Your query:\n"+user_query)
      vector_db_from_index = Pinecone.from_existing_index(index_name, hf_embeddings, namespace=namespace)
      ss_results = vector_db_from_index.similarity_search(query=user_query, namespace=namespace, k=5)
      initial_ai_response = chain.run(input_documents=ss_results, question=user_query, return_only_outputs=True)        
      #initial_ai_response=chain({"input_documents": ss_results, "question": user_query}, return_only_outputs=True)            
      temp_ai_response = initial_ai_response.partition('<|end|>')[0]
      final_ai_response = temp_ai_response.replace('\n', '')
      print(final_ai_response)
      print(index_name_extracted)
      print(index_status)
      print(namespace) 
      print("****************")
      return final_ai_response
    else:
      print("Invalid inputs.")  

def delete_index_namespace():
    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)    
    index_name_extracted=pinecone.list_indexes()
    index_current = pinecone.Index(index_name=index_name_extracted)
    index_status=index_current.describe_index_stats(index_current)     
    index_namespace_to_delete = pinecone.Index(index_name=index_name_extracted)
    index_namespace_to_delete.delete(delete_all=True, namespace=namespace)
    print("Pinecone Index Namespace: "+namespace+" has been deleted!")
        
with gr.Blocks() as demo:
    gr.Markdown("Enter your question below & click Get AI Response. Remember to clear data before exiting program.")
    with gr.Row():
        user_query = gr.Textbox(label="User query input box", placeholder="Enter your query here.")
        ai_response = gr.Textbox(label="AI Response display area", placeholder="AI Response to be displayed here.")
    query_btn = gr.Button("Get AI Response")
    ai_res_btn = gr.Button("Clear Data & Exit")
    query_btn.click(fn=run_chain, inputs=user_query, outputs=ai_response)
    ai_res_btn.click(fn=delete_index_namespace)

demo.launch()