first commit

app.py

@@ -1,12 +1,14 @@
 import datetime
 import gradio as gr
-from langdetect import DetectorFactory
+from huggingface_hub import hf_hub_download
 import fasttext, torch, clip
 from sentence_transformers import SentenceTransformer, util
  
 model_en, _ = clip.load("ViT-B/32")
 model_multi = SentenceTransformer("sentence-transformers/clip-ViT-B-32-multilingual-v1")
 
+fasttext_model = fasttext.load_model(hf_hub_download("julien-c/fasttext-language-id", "lid.176.bin"))
+
 def prep_examples():
     example_text1 = "Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most \
     people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment. \
@@ -78,7 +80,7 @@ def detect_lang(text):
         seq_lang = fasttext_model.predict(text, k=1)[0][0].split("__label__")[1]
     except:
         print("Language detection failed!",
-              "Date:{}, Sequence:{}".format(
+              "Date:{}, Sequence: {}".format(
 			  str(datetime.datetime.now()),
 			  text))
 
@@ -88,10 +90,10 @@ def sequence_to_classify(text, labels):
 	lang = detect_lang(text)
 	if lang == 'en':
 		model = model_en
-		hypothesis_template = "This example is {}."
+		hypothesis_template = "{}"
 	else:
 		model = model_multi
-		hypothesis_template = "{}."
+		hypothesis_template = "{}"
 	
 	labels = [hypothesis_template.format(label) for label in labels.split(";;")]
 	

requirements.txt

@@ -1,5 +1,5 @@
 torch
 sentence-transformers
 git+https://github.com/openai/CLIP.git
-langdetect
+huggingface_hub
 fasttext