rishi1985 commited on
Commit
8017bfd
1 Parent(s): f861be6

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +12 -8
app.py CHANGED
@@ -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
@@ -28,7 +28,7 @@ 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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@@ -36,10 +36,11 @@ 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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-
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  class Query(BaseModel):
@@ -91,7 +92,10 @@ async def startup_event():
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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={ }