Upload app.py

app.py

@@ -1,38 +1,36 @@
+#!/usr/bin/env python
+# coding: utf-8
+
 import gradio as gr
 import numpy as np
-# import requests
+import requests
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline, pipeline
 from langdetect import detect
 from matplotlib import pyplot as plt
 import imageio
 
-"""
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-"""
 # Load the model
-model = AutoModelForSequenceClassification.from_pretrained("saved_model")
+model = AutoModelForSequenceClassification.from_pretrained("saved_model") 
 tokenizer = AutoTokenizer.from_pretrained("saved_model")
 pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
 
-
 # Function called by the UI
 def attribution(text):
+    
     # Clean the plot
     plt.clf()
-
+    
     # Detect the language
     language = detect(text)
-
+     
     # Translate the input in german if necessary
     if language == 'fr':
         translator = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-de")
-        translatedText = translator(text[0:511])
+        translatedText = translator(text[0:1000])
         text = translatedText[0]["translation_text"]
-
+    elif language != 'de':
+        return "The language is not recognized, it must be either in German or in French.", None
+    
     # Set the bars of the bar chart
     bars = ""
     if language == 'fr':
@@ -40,17 +38,17 @@ def attribution(text):
     else:
         bars = ("VBS", "EDI", "AB-BA", "EJPD", "WBF", "UVEK", "EDA", "Parl", "BK", "EFD", "BV", "BGer")
 
-    # Make the prediction with the 512 first characters
-    results = pipe(text[0:511], return_all_scores=True)
+    # Make the prediction with the 1000 first characters 
+    results = pipe(text[0:1000], return_all_scores=True)
     rates = [row["score"] for row in results[0]]
-
+    
     # Bar chart
     y_pos = np.arange(len(bars))
     plt.barh(y_pos, rates)
     plt.yticks(y_pos, bars)
-
+    
     # Set the output text
-    name = ""
+    name = ""    
     maxRate = np.max(rates)
     maxIndex = np.argmax(rates)
 
@@ -72,18 +70,17 @@ def attribution(text):
                 i = 1
     # ML model pretty sure, show only one department
     else:
-        name = str(maxRate)[2:4] + "%" + "\t\t\t\t\t\t" + bars[maxIndex]
-
+        name =  str(maxRate)[2:4] + "%" + "\t\t\t\t\t\t" + bars[maxIndex]
+    
     # Save the bar chart as png and load it (enables better display)
     plt.savefig('rates.png')
     im = imageio.imread('rates.png')
 
     return name, im
-
+ 
 
 # display the UI
 interface = gr.Interface(fn=attribution, layout="vertical",
-                         inputs=[gr.inputs.Textbox(lines=20,
-                                                   placeholder="Geben Sie bitte den Titel und den Motionstext des Vorstosses ein.\nVeuillez entrer le titre et le texte dépoé de la requête.")],
-                         outputs=['text', 'image'])
+                         inputs=[gr.inputs.Textbox(lines=20, placeholder="Geben Sie bitte den Titel und den Sumbmitted Text des Vorstoss ein.\nVeuillez entrer le titre et le Submitted Text de la requête.")],
+                          outputs=['text', 'image'])
 interface.launch()