Fixing Routing problem

Inference.py

@@ -8,7 +8,7 @@
 ############################################################################################
 
 
-model_path = "./ASR_2_1_220.pth"
+model_path = "./ASR_2_1_300.pth"
 
 
 ############################################################################################

main.py

@@ -1,6 +1,7 @@
-import sys
 import io
+import sys
 
+import gdown
 from fastapi import (
     FastAPI,
     File,
@@ -11,13 +12,20 @@
 from pydantic import BaseModel
 
 from Inference import predict
-
 import tempfile
 import os
 from utils.Translation import *
 
 app = FastAPI()
 
+# Function to get the model and tokenizer from Google Drive instead of putting it in the repo
+def download_file_from_google_drive(file_id, output_path):
+    url = f'https://drive.google.com/uc?id={file_id}'
+    gdown.download(url, output_path, quiet=False)
+
+
+download_file_from_google_drive('1wYF0uHMHWdWb6G2XOB6dLQj3LWyz8u5X', './ASR_2_1_300.pth')
+
 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
 
 # Add CORS middleware
@@ -40,16 +48,14 @@ class TranslationRequest(BaseModel):
 
 
 @app.post("/translate/auto")
-async def translate(request: TranslationRequest):
+async def translateOpenL(request: TranslationRequest):
     response = translate_openl(request.text)
     return {"translation": response}
 
 
 @app.post("/translate/en")
-async def translate(request: TranslationRequest):
-    print(request.text)
-    response = translate(request.text)
-    print(response)
+async def translate_endpoint(request: TranslationRequest):
+    response = await translate(request.text)
     return {"translation": response}
 
 
@@ -64,7 +70,7 @@ async def upload_audio(file: UploadFile = File(...)):
 
     # Create a temporary file in the current working directory
     with tempfile.NamedTemporaryFile(
-        dir=current_dir, delete=False, suffix=".wav"
+            dir=current_dir, delete=False, suffix=".wav"
     ) as tmp_file:
         tmp_file.write(contents)
         tmp_file_path = tmp_file.name  # Get the path of the temp file

requirements.txt

@@ -1,10 +1,11 @@
 fastapi~=0.115.0
-openai~=1.50.2
-python-dotenv~=1.0.1
 numpy~=2.0.2
 torch~=2.4.1
 uvicorn~=0.31.0
 python-multipart~=0.0.12
 pydantic~=2.8.2
 librosa~=0.10.2.post1
-requests~=2.32.3
+requests~=2.32.3
+gdown~=5.2.0
+tokenizers~=0.20.1
+pandas~=2.2.3

utils/Translation.py

@@ -1,32 +1,20 @@
 import requests
-import sys
-import io
-import os
 import re
-import numpy as np
-
-from pathlib import Path
 
 import pandas as pd
 import torch
 
-# import evaluate
-
 from torch import nn
-from torch.utils.data import DataLoader, Dataset, Subset
-
-from tqdm.notebook import tqdm
+from torch.utils.data import Dataset
 
 from tokenizers import Tokenizer
 from tokenizers import normalizers
 from tokenizers import pre_tokenizers
-from tokenizers.models import WordLevel, WordPiece
+from tokenizers.models import WordLevel
 from tokenizers.processors import TemplateProcessing
 from tokenizers.normalizers import NFC, StripAccents, Strip, Lowercase
 from tokenizers.pre_tokenizers import Whitespace
-from tokenizers.trainers import WordLevelTrainer, WordPieceTrainer
-
-from sklearn.model_selection import train_test_split
+from tokenizers.trainers import WordLevelTrainer
 
 
 def translate_openl(txt):
@@ -204,13 +192,13 @@ def forward(self, src, src_mask):
 
 class Encoder(nn.Module):
     def __init__(
-        self,
-        vocab_size: int,
-        latent_dim: int,
-        n_layers: int,
-        n_heads: int,
-        pf_dim: int,
-        dropout: float,
+            self,
+            vocab_size: int,
+            latent_dim: int,
+            n_layers: int,
+            n_heads: int,
+            pf_dim: int,
+            dropout: float,
     ):
         super().__init__()
 
@@ -309,14 +297,14 @@ def forward(self, trg, enc_out, trg_mask, src_mask):
 
 class Decoder(nn.Module):
     def __init__(
-        self,
-        vocab_size: int,
-        latent_dim: int,
-        n_layers: int,
-        n_heads: int,
-        pf_dim: int,
-        dropout: float,
-        max_len: int = 100,
+            self,
+            vocab_size: int,
+            latent_dim: int,
+            n_layers: int,
+            n_heads: int,
+            pf_dim: int,
+            dropout: float,
+            max_len: int = 100,
     ):
         super().__init__()
         self.latent_dim = latent_dim
@@ -373,9 +361,9 @@ def forward(self, trg, enc_out, trg_mask, src_mask):
 
 class Seq2Seq(nn.Module):
     def __init__(
-        self,
-        encoder: nn.Module,
-        decoder: nn.Module,
+            self,
+            encoder: nn.Module,
+            decoder: nn.Module,
     ):
         super().__init__()
         self.encoder = encoder
@@ -416,14 +404,13 @@ def forward(self, src, trg, src_mask, trg_mask):
 
 
 def translate_sentence(
-    sentence: str,
-    src_tokenizer: Tokenizer,
-    trg_tokenizer: Tokenizer,
-    model: nn.Module,
-    device: torch.device,
-    max_len: int = 50,
+        sentence: str,
+        src_tokenizer: Tokenizer,
+        trg_tokenizer: Tokenizer,
+        model: nn.Module,
+        device: torch.device,
+        max_len: int = 50,
 ):
-
     model.eval()
 
     # encode sentence
@@ -545,5 +532,4 @@ def translate(sentence):
     # print(translation)
     return translation
 
-
 # print(translate("سوف نذهب الي الحديقة الاحد المقبل"))