Update app.py

app.py

@@ -1,81 +1,81 @@
-import streamlit as st
-from multiprocessing import Process
-
-from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification
-
-import torch
-import pandas as pd
-import json
-import requests
-
-import time
-import os
-
-model_name_or_directory = "MKaan/multilingual-cpv-sector-classifier" 
-tokenizer = AutoTokenizer.from_pretrained("bert-base-multilingual-cased")
-
-config = AutoConfig.from_pretrained(model_name_or_directory)
-model = AutoModelForSequenceClassification.from_pretrained(model_name_or_directory, config=config)
-
-device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-idx2cpv = pd.read_csv("idx2cpv.csv")
-idx2cpv = dict(zip(idx2cpv.indexes, idx2cpv.sectors))
-
-def get_result(input):
-  input_ids = tokenizer(input, return_tensors="pt").input_ids
-  output = model(input_ids)
-  pred = output.logits.argmax(dim=-1)
-  pred = pred.cpu().detach().numpy()[0]
-  return idx2cpv[pred]
-  
-if __name__ == "__main__":
-    st.title('Multilingual Sector Classifier 📄') #📊💼
-    st.subheader('Finds the correct sector for the given contract description')
-    st.markdown("Built by Mustafa Kaan Görgün, [Linkedin](https://www.linkedin.com/in/mustafa-kaan-görgün-a2461288/), [Model Card](https://huggingface.co/MKaan/multilingual-cpv-sector-classifier) ", unsafe_allow_html=True)
-    
-    examples = pd.read_csv("examples.csv")
-    lang2example = dict(zip(examples.lang, examples.descr))
-    
-    st.markdown(f'##### Try it now:')
-    
-    #st.markdown(f'Choose a language in any of 22 languages')
-    input_lang = st.selectbox(
-        label="Choose a language from the list of 22 languages",
-        options=examples.lang,
-        index=5
-    )
-    
-    input_text_1 = st.text_area(
-        label="Example description in choosen language",
-        value=lang2example[input_lang],
-        height=150,
-        max_chars=500
-    )
-    
-    button1 = st.button('Run the example')
-    
-    st.write("or")
-    
-    #st.markdown('Write your own contract description in any of 104 languages that MBERT supports.')
-    input_text_2 = st.text_area(
-        label="Write your own contract description in any of 104 languages that MBERT supports.",
-        value="Your description comes here..",
-        height=100,
-        max_chars=500
-    )
-   
-    button2 = st.button('Run your own')
-    
-    st.markdown(f'##### Classified Sector: ')
-    if button1:
-        with st.spinner('In progress.......'):
-            sector_class = get_result(input_text_1)
-            #sector_class = input_text_1
-        st.success(sector_class)
-        
-    if button2:
-        with st.spinner('In progress.......'):
-            sector_class = get_result(input_text_2)
-            #sector_class = input_text_2
+import streamlit as st
+from multiprocessing import Process
+
+from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification
+
+import torch
+import pandas as pd
+import json
+import requests
+
+import time
+import os
+
+model_name_or_directory = "MKaan/multilingual-cpv-sector-classifier" 
+tokenizer = AutoTokenizer.from_pretrained("bert-base-multilingual-cased")
+
+config = AutoConfig.from_pretrained(model_name_or_directory)
+model = AutoModelForSequenceClassification.from_pretrained(model_name_or_directory, config=config)
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+idx2cpv = pd.read_csv("idx2cpv.csv")
+idx2cpv = dict(zip(idx2cpv.indexes, idx2cpv.sectors))
+
+def get_result(input):
+  input_ids = tokenizer(input, return_tensors="pt").input_ids
+  output = model(input_ids)
+  pred = output.logits.argmax(dim=-1)
+  pred = pred.cpu().detach().numpy()[0]
+  return idx2cpv[pred]
+  
+if __name__ == "__main__":
+    st.title('Multilingual Sector Classifier 📄') #📊💼
+    st.subheader('Finds the correct sector for the given contract description')
+    st.markdown("Built by Mustafa Kaan Görgün, [Linkedin](https://www.linkedin.com/in/mustafa-kaan-görgün-a2461288/), [Model Card](https://huggingface.co/MKaan/multilingual-cpv-sector-classifier) ", unsafe_allow_html=True)
+    
+    examples = pd.read_csv("examples.csv")
+    lang2example = dict(zip(examples.lang, examples.descr))
+    
+    st.markdown(f'##### Try it now:')
+    
+    #st.markdown(f'Choose a language in any of 22 languages')
+    input_lang = st.selectbox(
+        label="Choose a language from the list of 22 languages",
+        options=examples.lang,
+        index=5
+    )
+    
+    input_text_1 = st.text_area(
+        label="Example description in choosen language",
+        value=lang2example[input_lang],
+        height=150,
+        max_chars=500
+    )
+    
+    button1 = st.button('Run the example')
+    
+    st.write("or")
+    
+    #st.markdown('Write your own contract description in any of 104 languages that MBERT supports.')
+    input_text_2 = st.text_area(
+        label="Write your own contract description in any of 104 languages that MBERT supports.",
+        value="Your description comes here..",
+        height=100,
+        max_chars=500
+    )
+   
+    button2 = st.button('Run your own')
+    
+    st.markdown(f'##### Classified Sector: ')
+    if button1:
+        with st.spinner('In progress.......'):
+            sector_class = get_result(input_text_1)
+            #sector_class = input_text_1
+        st.success(sector_class)
+        
+    if button2:
+        with st.spinner('In progress.......'):
+            sector_class = get_result(input_text_2)
+            #sector_class = input_text_2
         st.success(sector_class)