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Update app.py
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app.py
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@@ -3,19 +3,19 @@
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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 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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class Query(BaseModel):
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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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dict={ }
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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 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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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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