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Update app.py
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app.py
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# Expose the port
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EXPOSE 7860
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# Keeps Python from generating .pyc files in the container
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ENV PYTHONDONTWRITEBYTECODE=1
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# Turns off buffering for easier container logging
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ENV PYTHONUNBUFFERED=1
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#
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# Set the NGROK_PATH environment variable to a writable location
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ENV NGROK_PATH /tmp/ngrok
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COPY requirements.txt .
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# Upgrade pip and install the required packages
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RUN pip install --upgrade pip && \
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pip install -r requirements.txt
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apt-get install -y sudo && \
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mkdir -p /code/image
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RUN adduser -u 5678 --disabled-password --gecos "" appuser && \
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usermod -aG sudo appuser && \
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usermod -aG root appuser && \
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chown -R appuser:appuser /code
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# Create the pyngrok bin directory and set the ownership and permissions for appuser
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RUN mkdir -p /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
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chown -R appuser:appuser /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
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chmod -R 777 /usr/local/lib/python3.9/site-packages/pyngrok/bin
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RUN apt-get update && \
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apt-get install -y curl
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#
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WORKDIR /code
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# Set the ownership and permissions for the /code directory and its contents
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RUN chown -R appuser:appuser /code && \
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chmod -R 777 /code
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# RUN chown -R appuser:appuser /code/data.csv && \
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# chmod -R 777 /code/data.csv
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COPY pyngrok.yml /tmp/pyngrok.yml
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USER appuser
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#
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#
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#
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# Start the Uvicorn server
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# ENTRYPOINT ["python", "main.py"]
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# CMD ["sh", "-c", "python main.py & sleep infinity"]
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860","--workers","2"]
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# import firebase_admin
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# from firebase_admin import credentials
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# from firebase_admin import firestore
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import io
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from fastapi import FastAPI, File, UploadFile
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# from werkzeug.utils import secure_filename
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# import speech_recognition as sr
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import subprocess
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import os
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import requests
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import random
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import pandas as pd
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# from pydub import AudioSegment
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from datetime import datetime
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from datetime import date
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# import numpy as np
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# from sklearn.ensemble import RandomForestRegressor
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# import shutil
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import json
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# from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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from pydantic import BaseModel
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from typing import Annotated
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# from transformers import BertTokenizerFast, EncoderDecoderModel
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import torch
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import re
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# from transformers import AutoTokenizer, T5ForConditionalGeneration
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from fastapi import Form
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# from transformers import AutoModelForSequenceClassification
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# from transformers import TFAutoModelForSequenceClassification
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# from transformers import AutoTokenizer, AutoConfig
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# import numpy as np
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# from scipy.special import softmax
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from sentence_transformers import SentenceTransformer
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# model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L6')
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# model = SentenceTransformer("sentence-transformers/all-roberta-large-v1")
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# model =SentenceTransformer("intfloat/multilingual-e5-large")
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model = SentenceTransformer('intfloat/multilingual-e5-large')
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class Query(BaseModel):
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text: str
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from fastapi import FastAPI, Request, Depends, UploadFile, File
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from fastapi.exceptions import HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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# now = datetime.now()
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# UPLOAD_FOLDER = '/files'
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# ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png',
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# 'jpg', 'jpeg', 'gif', 'ogg', 'mp3', 'wav'}
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=['*'],
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allow_credentials=True,
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allow_methods=['*'],
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allow_headers=['*'],
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)
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# cred = credentials.Certificate('key.json')
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# app1 = firebase_admin.initialize_app(cred)
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# db = firestore.client()
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# data_frame = pd.read_csv('data.csv')
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@app.on_event("startup")
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async def startup_event():
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print("on startup")
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@app.post("/")
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async def get_answer(q: Query ):
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text = q.text
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# text_e = model.encode(text)
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input_texts = [text]
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embeddings = model.encode(input_texts)
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text_e = embeddings[0]
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dict={ }
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c=0
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text_e= text_e.tolist()
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for num in text_e:
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dict[c]= num
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c= c+1
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return dict
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return "hello"
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