app.py

import os
import openai
openai.api_key= os.environ.get('API_OPENAI')
AIRTABLE_API_KEY = os.environ.get('API_AIRTABLE')

import gradio as gr
import os
import csv
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
import openai
import pinecone
import datetime
from tqdm.autonotebook import tqdm
import requests

embeddings = OpenAIEmbeddings(openai_api_key=openai.api_key)

AIRTABLE_ENDPOINT = "https://api.airtable.com/v0/appv2hF1PzrseVdeW/data_m"
AIRTABLE_ENDPOINT_LOG = "https://api.airtable.com/v0/appv2hF1PzrseVdeW/log"
HEADERS = {
    "Authorization": f"Bearer {AIRTABLE_API_KEY}",
    "Content-Type": "application/json"
}

def get_text_by_name(name):
    params = {
        "filterByFormula": f"{{name}} = '{name}'"
    }
    response = requests.get(AIRTABLE_ENDPOINT, headers=HEADERS, params=params)

    if response.status_code != 200:
        print(f"Error fetching data. Status code: {response.status_code}. Content: {response.content}")
        return None

    records = response.json().get("records")

    if not records:
        print(f"No record found with name: {name}")
        return None

    # Assuming that names are unique, take the first record.
    return records[0]["fields"].get("text")

def upload_to_airtable_log(date, question, answer, rating, comment):
    data = {
        "records": [{
            "fields": {
                 "date": date,
                 "question": question,
                 "answer": answer,
                 "rating": rating,
                 "comment": comment
            }
        }]
    }
    response = requests.post(AIRTABLE_ENDPOINT_LOG, headers=HEADERS, json=data)

    if response.status_code != 200:
        print(f"Error uploading airtable (log ) Status code: {response.status_code}. Content: {response.content}")
    else:
        print(f"Successfully uploaded airtable log")

def query_gpt_3_5(prompt, context):
    prompt = "Instruction: Твоя роль - кваліфікований співробітник саппорту у системи YouControl. Потрібно відповісти на питання від користувача з огляду на контекст. Контекст ми беремо з бази знань, але вона може бути не повна.  Якщо контекст не коректний, то відповідай на свій розсуд або передай запит сапорту, про контекс нічого не пишемо у відповіді."+"""
    """+ "question:" + prompt + """
    context:""" + context + """
    answer:"""
    completion = openai.ChatCompletion.create(
    model="gpt-4-0613",
    messages=[
      {"role": "user", "content": prompt}
              ]
    )
    return completion.choices[0].message.content

def print_docs(docs):
    for doc in docs:
        print(f"Chunk: {doc.page_content}")
        print(f"Content: {doc.metadata['source']}\n")

# initialize pinecone
pinecone.init(
    api_key="078ea261-eaa8-4920-8af6-0870f7f8a096",  # find at app.pinecone.io
    environment="eu-west4-gcp"  # next to api key in console
)

index_name = "yc-faq"
vectorstore = Pinecone.from_existing_index(index_name, embeddings)

def ask_yc_bot(question):
  if (len(question)<3 ): return "Не зрозумів питання"
  docs = vectorstore.similarity_search(question)
  print_docs(docs)
  source_name = docs[0].metadata['source']
  file_name  = source_name.replace("/content/drive/MyDrive/yc-bot/faq/", "")
  result_text = get_text_by_name(file_name)
  context = ""
  if result_text:
      print(f"Text for '{file_name}' is: {result_text}")
      context = context +  result_text



  #print (len(context))
  result = query_gpt_3_5(question, context)
  result = result + """
--------------------------------------------------------
[CONTEXT]
""" + context
  return result


def comment_bot(slider_value, comment_text, question_text, answer_text):
    date_d  = datetime.datetime.now().date()
    date_string = date_d.isoformat()
    upload_to_airtable_log(date_string, question_text, answer_text, slider_value, comment_text)

    return " "  # Если функция должна что-то возвращать, замените это на нужный вывод

import gradio as gr

description = """<h1>YC - FAQ_BOT</h1>
"""

demo = gr.Blocks()

with demo:
    gr.HTML(description)
    with gr.Row():
      with gr.Column(scale=1):
        text = gr.Textbox(lines=7, label = "Вопрос?")
        b1 = gr.Button("Спросить")
      with gr.Column(scale=2):
        answer = gr.Textbox(lines=10, label = "Ответ:")

    with gr.Row():
        radio = gr.Radio(label="Рейтинг ответа", choices=["Нет", "1", "2", "3", "4", "5"], value="Нет")
        comment = gr.Textbox(lines=2, label = "Комментарий")
    with gr.Row():
        b2 = gr.Button("Прокомментировать ответ ")

    inp_2 = [radio, comment, text, answer]
    b1.click(ask_yc_bot, inputs=text, outputs=answer)
    b2.click(comment_bot, inputs=inp_2, outputs=comment)


demo.launch(share=False, debug=True)