app.py

import streamlit as st

#from transformers import pipeline
from pinecone import Pinecone, ServerlessSpec
from sentence_transformers import SentenceTransformer, util
from openai import OpenAI
from datetime import datetime
import pandas as pd
import os
import time
import json

from huggingface_hub import HfFileSystem
token = os.getenv('FILE_TOKEN')
fs = HfFileSystem(token=token)




api_key=os.environ["OPENAI_API_KEY"]


bi_encoder = SentenceTransformer('msmarco-distilbert-base-v4')
bi_encoder.max_seq_length = 256     # Truncate long documents to 256 tokens

# Store the index as a variable
INDEX_NAME = 'cl-search-idx'

pc_api_key= os.environ["clpine"]  #AWS
pc = Pinecone(api_key=pc_api_key)
index = pc.Index(name=INDEX_NAME)

system_instructions_text='''
          Your task is to extract the answer to a question from a body of text provided to you.
          The body of text will be enclosed within the delimiter tags  <text> and </text>

          For example,
          <text> General Preparation Tips for VARC Section:

          You need to develop an incessant habit of speed reading.
          Start with reading newspapers, editorials, fiction and nonfiction novels and simple passages.
          The more you read, the faster you read. Learn the basic grammar concepts like parts of speech, articles,verbs, adjectives, tenses, auxiliary verbs, modifiers, modals etc.
          Revise at least 50 new words every day
          </text>

          Question: What are some tips for preparing for VARC?
          Here are some tips for preparing for the VARC section:
          1. develop an incessant habit of speed reading
          2. Start reading newspapers, editorials, fiction and nonfiction novels
          3. Learn basic grammar concepts\n
          4. Revise at least 50 new words a day

          Question: How many new words are to be learnt in a day?
          It is advised that 50 new words are learn every day

          Your response should be based on the information contained in the provided text and should not included any other sources.
          If you are unable to answer the question from the text provided, please respond " Sorry. I do not have enough information to answer this"
           Do repeat the question. Do not make a pointed reference to the text provided. Directly answer the question
          '''
json_instructions='''
          Your task is to extract the answer to a question from a body of text provided to you in a json array.
          The json will contain two pieces of content in this format:
          [
              {"id":1 , "content": " first content"},
              {"id":2 , "content": " second content"}
          ]
          You need to check which content is most appropriate to answer the question and prepare
          an answer based on the content

          For example,
          [
          { "id":1 , "content" : "General Preparation Tips for Verbal Section:\n
          You need to develop an incessant habit of speed reading.
          Start with reading newspapers, editorials, fiction and nonfiction novels and simple passages.
          The more you read, the faster you read. Learn the basic grammar concepts like parts of speech, articles,verbs, adjectives, tenses, auxiliary verbs, modifiers, modals etc.
          Revise at least 50 new words every day"},
          { "id":2 , "content" : "General Preparation Tips for Quantitative Section:\n
          You need to develop an speed in solving math problems.
          Start with reading funda books,  math text books.
          Learn the basic  concepts like arithmetic, geometry, numbers, probability,  etc.
          Solve at least 50 new problems every day"}
          ]

          Question: What are some tips for preparing for Verbal exam?
          Here are some tips for preparing for the VARC section:
          1. develop an incessant habit of speed reading
          2. Start reading newspapers, editorials, fiction and nonfiction novels
          3. Learn basic grammar concepts\n
          4. Revise at least 50 new words a day


          Your response should be based on the information contained in the provided content in the json and should not included any other sources.
          If you are unable to answer the question from the content provided, please respond " Sorry. I do not have enough information to answer this"
           Do repeat the question. Do not make a pointed reference to the content provided. Directly answer the question
          '''

def get_meta_score(url,question_embedding):
    qry = index.fetch(ids=[url], namespace="meta")
    try:
        emb=qry['vectors'][resp['id']]['values']
        vector1 = np.array(emb).reshape(1, -1)  # Reshape to ensure compatibility with sklearn
        vector2 = question_embedding.numpy().reshape(1, -1)
        
        # Calculate cosine similarity
        cosine_scores = util.cos_sim(question_embedding, emb)
        return  cosine_scores.item())
    except:
        return 0

def query_from_pinecone(index,namespace, question_embedding, top_k=3):
    # get embedding from THE SAME embedder as the documents

    return index.query(
      vector=question_embedding,
      top_k=top_k,
      namespace=namespace,
      include_metadata=True   # gets the metadata (dates, text, etc)
    ).get('matches')

def response_generator(response):

    for word in response.split():
        yield word + " "
        time.sleep(0.05)

def write_log(query,url, score, ans, ts):
    # Construct new row
    score = str(score)
    ans = ans.replace('"', '""')
    new_row = f'\n"{query}","{url}",{score},"{ans}","{ts}"'

    with fs.open("datasets/sujitb/data/querylog.csv", "r") as f:
        buffer = f.read()

    # Append the new row to buffer
    buffer += new_row

    # Write the buffer to the file in "W" mode
    with fs.open("datasets/sujitb/data/querylog.csv", "w",encoding="utf-8") as f:
        f.write(buffer)

    return
    
logfile='querylog.csv'
try:
    df_log=pd.read_csv(logfile, index_col=0)
except:
    df_log=pd.DataFrame(columns=['query','url','score','ans', 'ts'])



st.title('CLLM Answering Machine')

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])


QUESTION = st.chat_input('Ask a question -e.g How to prepare for Verbal section for CAT?')


#QUESTION=st.text_area('Ask a question -e.g How to prepare for Verbal section for CAT?')  ##' How to prepare for Verbal section ?'
score=0
testing=True


if QUESTION:

    with st.chat_message("user"):
        st.markdown(QUESTION)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": QUESTION})

    question_embedding = bi_encoder.encode(QUESTION, convert_to_tensor=True)

    ns='webpages'
    ns='full'
    resp= query_from_pinecone(index,ns, question_embedding.tolist(), 3)
    if len(resp)>0:
        #st.write("Got results...extracting an answer")
        out= resp[0]['metadata']['data']
        try:
            url= resp[0]['metadata']['url']
        except:
            url= resp[0]['id']
        url= resp[0]['id']
        
        title=resp[0]['metadata']['title']
        #+ '\n*************\n'+  resp[1]['metadata']['text'] + '\n*************\n'+ resp[2]['metadata']['text']
        d={}
        d['id']=1
        d['content']=resp[0]['metadata']['data']
        d['url']=url
        meta_score= get_meta_score(url,question_embedding)
        score=.5* resp[0]['score'] + .5*meta_score

        if len(resp)>1:
            d['id']=2
            d['content']=resp[1]['metadata']['data']
            d['url']=url
            meta_score= get_meta_score(url,question_embedding)
            score=.5* resp[0]['score'] + .5*meta_score


        # Get the elements with the top 2 highest values
        top_2 = sorted(d.items(), key=lambda x: x[3], reverse=True)[:2]
            
        # covert to array
        json_array = [{'id': key, 'content': value} for key, value in top_2.items()]
        json_data = json.dumps(json_array)

        st.write(json_data)
        
        if score>.5:

            mode = "two"  # two passages

            client = OpenAI()

            if mode=="one":
                instr=system_instructions_text
                content="""
                <text>
                {}
                </text>
                """.format(out)
                
            if mode=="two":
                instr=json_instructions
                content=json_data

            response = client.chat.completions.create(
              model="gpt-3.5-turbo",
              messages=[
                {"role": "system", "content":instr },
                {"role": "user", "content": content},
                {"role": "user", "content": "Question:"+QUESTION}
              ]
            )

            ans= response.choices[0].message.content
        else:
            ans='Weak match to your query. Please try reframing your question'

        #st.write("Matched URL:{}  Score:{}".format(url,score))
        testing = False
        if testing:
            if len(resp)>=1:
                st.write("2nd Matched URL:{}  Score:{}".format(resp[1]['id'],resp[1]['score']))
            if len(resp)>=2:
                st.write("3rd Matched URL:{}  Score:{}".format(resp[2]['id'],resp[2]['score']))

        with st.chat_message("assistant"):
            response = st.write_stream(response_generator(ans))
            # Add assistant response to chat history
            st.session_state.messages.append({"role": "assistant", "content": response})
        #st.write(ans)

        #st.write(' ----------------------')
        #st.write(out)

        now= str(datetime.utcnow())
        df_log.loc[len(df_log)]=[QUESTION,url,score,ans,now]
        write_log(QUESTION,url, score, ans, now)
        #df.to_csv("hf://datasets/sujitb/data/test.csv")
        #df_log.to_csv("hf://datasets/sujitb/data/"+logfile)

    else:
        #st.write("No matches for query")
        ans= "No matches for query"
        response = st.write_stream(response_generator(ans))
        # Add assistant response to chat history
        st.session_state.messages.append({"role": "assistant", "content": response})

        now= str(datetime.utcnow())
        df_log.loc[len(df_log)]=[QUESTION,'No match',0,'-',now]
        #df_log.to_csv("hf://datasets/sujitb/data/"+logfile)
        write_log(QUESTION,url, score, ans, now)