app.py

import gradio as gr
from langchain.tools import Tool
from langchain.utilities import GoogleSearchAPIWrapper
from rank_bm25 import BM25Okapi
import sys
import nltk
nltk.download('punkt')
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from langchain.callbacks import get_openai_callback
import openai
import time
import pandas as pd
import random
import os
import csv
from langchain.tools import Tool
from langchain.utilities import GoogleSearchAPIWrapper
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
import os 
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
import tempfile
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
import os

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
import os
import time
# Import Azure OpenAI
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
from langchain.callbacks import get_openai_callback
import os
import openai
# Import Azure OpenAI
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
from langchain.callbacks import get_openai_callback
import sys
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from langchain.callbacks import get_openai_callback
import openai
import pandas as pd
import random
import os
import csv
import numpy as np
import pickle
from rank_bm25 import BM25Okapi
from openai import OpenAI
from nltk.tokenize import word_tokenize
loaded_texts = np.load('texts.npy', allow_pickle=True)
loaded_texts= [str(text) if not isinstance(text, str) else text for text in loaded_texts]
with open('bm25_model.pkl', 'rb') as file:
  bm25 = pickle.load(file)
# france credentials
BASE_URL = "https://cnerg-gpt-france.openai.azure.com/"
DEPLOYMENT_NAME = "GPT-4-France"
API_KEY = os.environ['API_KEY']
model = AzureChatOpenAI(
    openai_api_base=BASE_URL,
    openai_api_version="2023-05-15",
    deployment_name=DEPLOYMENT_NAME,
    openai_api_key=API_KEY,
    openai_api_type="azure",
)
search = GoogleSearchAPIWrapper()
import os
def top10_results(query):
    return search.results(query, 10)

tool = Tool(
    name="Google Search",
    description="Search Google for recent results.",
    func=top10_results,
)

def search_results(input_text):
  raw_text_list = tool.run(input_text)
  return raw_text_list
def Bm25(raw_text_list,input_text,n) :
  corpus = [item['snippet'] for item in raw_text_list]
  tokenized_corpus = [doc.split(" ") for doc in corpus]
  bm25 = BM25Okapi(tokenized_corpus)
  query = input_text
  tokenized_query = query.split(" ")
  doc_scores = bm25.get_scores(tokenized_query)
  top_5_results = bm25.get_top_n(tokenized_query, corpus, n=n)
  results = '\n'.join(top_5_results)
  combined_input = "query = " + input_text + "\n\n For the above query these are some results from a search engine: \n ".join(results)  + "\n\n Give detailed and brief answer for the query write in 500-1000 words. Give detailed and well informative answer(include calculations if needed, using tables and other styles of structuring is optional for better answering ) "
  return combined_input,results

def llm_route(llm):
    openai_api_key = "EMPTY"
    openai_api_base = ""
    if llm=="llama-2-7b":
        openai_api_base= os.environ['llama7_api']
    if llm=="llama-2-13b":
        openai_api_base= os.environ['llama13_api']
    if llm=="Vicuna-13b":
        openai_api_base= os.environ['vicuna13_api']
    client = OpenAI(
        api_key=openai_api_key,
        base_url=openai_api_base,
    )
    models = client.models.list()
    model = models.data[0].id
    return model,client
        
    
    
    
# Define your functions here
def function2(input_text,one_shot_example,llm):
    if llm=="GPT-4":
      model = AzureChatOpenAI(
          openai_api_base=BASE_URL,
          openai_api_version="2023-05-15",
          deployment_name=DEPLOYMENT_NAME,
          openai_api_key=API_KEY,
          openai_api_type="azure",
        )
      if len(one_shot_example)==0:
        combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data , question : {input_text}"
        generated_answer = model(
              [
                  HumanMessage(
                      content=combined_input
                  )
              ]
          )
        return generated_answer.content
      else:
        combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data ,Below is a example question-answer pair for reference\n\n {one_shot_example} \n\n  Now answer this question \n\n question :{input_text}"
        generated_answer = model(
              [
                  HumanMessage(
                      content=combined_input
                  )
              ]
          )
        return generated_answer.content
    else :
        model,client=llm_route(llm)
        if len(one_shot_example)==0:
           
            combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data , question : {input_text}"
            completion = client.completions.create(
                model=model,
                prompt=combined_input,
                max_tokens=1024,  # Adjust the number of tokens as needed
                n=1,  # Number of completions to generate
                stop=None,  # Optional: specify a stop sequence
                temperature=0.7  # Adjust the creativity of the response
            )
            return completion.choices[0].text.strip()
        else:
            combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data ,Below is a example question-answer pair for reference\n\n {one_shot_example} \n\n  Now answer this question \n\n question :{input_text}"
            completion = client.completions.create(
                model=model,
                prompt=combined_input,
                max_tokens=1024,  # Adjust the number of tokens as needed
                n=1,  # Number of completions to generate
                stop=None,  # Optional: specify a stop sequence
                temperature=0.7  # Adjust the creativity of the response
            )
            return completion.choices[0].text.strip()
          

  

def function3(input_text,one_shot_example,n,llm):
  n=int(n)
  k=search_results(input_text)
  k,results=Bm25(k,input_text,n)
  if llm=="GPT-4":
      model = AzureChatOpenAI(
          openai_api_base=BASE_URL,
          openai_api_version="2023-05-15",
          deployment_name=DEPLOYMENT_NAME,
          openai_api_key=API_KEY,
          openai_api_type="azure",
        )
      if len(one_shot_example)==0:
        combined_input = k
        generated_answer = model(
              [
                  HumanMessage(
                      content=combined_input
                  )
              ]
          )
        return generated_answer.content,results
      else:
        combined_input = k+f"\n\n Here is a sample question answer pair for reference :\n\n {one_shot_example} "
        generated_answer = model(
              [
                  HumanMessage(
                      content=combined_input
                  )
              ]
          )
        return generated_answer.content,results
  else:
      model,client=llm_route(llm)
      if len(one_shot_example)==0:

        combined_input = k
        completion = client.completions.create(
                model=model,
                prompt=combined_input,
                max_tokens=1024,  # Adjust the number of tokens as needed
                n=1,  # Number of completions to generate
                stop=None,  # Optional: specify a stop sequence
                temperature=0.7  # Adjust the creativity of the response
            )
        return completion.choices[0].text.strip(),results
      else:
        combined_input = k+f"\n\n Here is a sample question answer pair for reference :\n\n {one_shot_example} "
        completion = client.completions.create(
                model=model,
                prompt=combined_input,
                max_tokens=1024,  # Adjust the number of tokens as needed
                n=1,  # Number of completions to generate
                stop=None,  # Optional: specify a stop sequence
                temperature=0.7  # Adjust the creativity of the response
            )
        return completion.choices[0].text.strip(),results
      
      


def function4(input_text, one_shot_example, n,llm):
    tokenized_query = word_tokenize(input_text.lower())
    doc_scores = bm25.get_scores(tokenized_query)
    sorted_docs = [doc for _, doc in sorted(zip(doc_scores, loaded_texts), reverse=True)]
    n=int(n)
    k=""
    for doc in sorted_docs[:n]:
        k+=doc
    results=k
    if llm=="GPT-4":
        model = AzureChatOpenAI(
          openai_api_base=BASE_URL,
          openai_api_version="2023-05-15",
          deployment_name=DEPLOYMENT_NAME,
          openai_api_key=API_KEY,
          openai_api_type="azure",
        )
        if len(one_shot_example)==0:
            combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data , context:{k} \n\n question : {input_text}"
            generated_answer = model(
                  [
                      HumanMessage(
                          content=combined_input
                      )
                  ]
              )
            return generated_answer.content,results
        else:
            
            combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data  \n\n context:{k} \n\n,Below is an example question-answer pair for reference\n\n {one_shot_example} \n\n  Now answer this question \n\n question :{input_text}"
            generated_answer = model(
                  [
                      HumanMessage(
                          content=combined_input
                      )
                  ]
              )
            return generated_answer.content,results
    else:
        model,client=llm_route(llm)
        if len(one_shot_example)==0:
            combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data , context:{k} \n\n question : {input_text}"
            completion = client.completions.create(
                model=model,
                prompt=combined_input,
                max_tokens=1024,  # Adjust the number of tokens as needed
                n=1,  # Number of completions to generate
                stop=None,  # Optional: specify a stop sequence
                temperature=0.7  # Adjust the creativity of the response
            )
            return completion.choices[0].text.strip(),results
        else:
            completion = client.completions.create(
                model=model,
                prompt=combined_input,
                max_tokens=1024,  # Adjust the number of tokens as needed
                n=1,  # Number of completions to generate
                stop=None,  # Optional: specify a stop sequence
                temperature=0.7  # Adjust the creativity of the response
            )
            return completion.choices[0].text.strip(),results
        
        


# Define the dropdown options
dropdown_options = ["1", "2", "3"]
dropdown_options_4 = ["1","2","3","4","5","6","7","8","9","10"]
llm_dropdown=["GPT-4","llama-2-7b","llama-2-13b"]
# ,"Vicuna-13b"

# Create individual interfaces for each function
# iface1 = gr.Interface(gpt4, inputs="text", outputs="text")
#iface2 = gr.Interface(gpt4, inputs=["text", "text"], outputs="text")
iface2 = gr.Interface(
    function2, 
    inputs=[
        gr.Textbox(label="Input Text"), 
        gr.Textbox(label="One Shot Example"), 
        gr.Dropdown(choices=llm_dropdown, label="LLM")
        
    ], 
    outputs="text"
)


iface3 = gr.Interface(
    function3, 
    inputs=[
        gr.Textbox(label="Input Text"), 
        gr.Textbox(label="One Shot Example"), 
        gr.Dropdown(choices=dropdown_options, label="Number of top search results"),
        gr.Dropdown(choices=llm_dropdown, label="LLM")
    ], 
    outputs=[
        gr.Textbox(label="LLM Answer"), 
        gr.Textbox(label="Google Search Result")
    ]
)


iface4 = gr.Interface(
    function4, 
    inputs=[
        gr.Textbox(label="Input Text"), 
        gr.Textbox(label="One Shot Example"), 
        gr.Dropdown(choices=dropdown_options_4, label="Number of top k documents"),
        gr.Dropdown(choices=llm_dropdown, label="LLM")
    ], 
        outputs=[
        gr.Textbox(label="LLM Answer"), 
        gr.Textbox(label="Abstract Search Result")
    ]
   
    
)


# Create a parallel interface that combines all individual interfaces
iface = gr.TabbedInterface([iface2, iface3, iface4], 
                           tab_names=["LLM Inference", "LLM with internet search", "LLM with Abstract Search"])



# Launch the interface
if __name__ == "__main__":
    iface.launch(share=True)
#a