Create app.py

app.py

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+#importing the necessary libraries
+import gradio as gr
+import numpy as np
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+#Defining the labels of the models
+labels = ['Politics', 'Tech', 'Entertainment', 'Business', 'World', 'Sport']
+
+#Defining the models and tokenuzer
+model_name = 'valurank/finetuned-distilbert-news-article-categorization'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+#Reading in the text file
+def read_in_text(url):
+  with open(url, 'r') as file:
+    article = file.read()
+    return article
+ 
+#Defining a function to get the category of the news article   
+def get_category(file):
+  text = read_in_text(file.name)
+
+  input_tensor = tokenizer.encode(text, return_tensors='pt', truncation=True)
+  logits = model(input_tensor).logits
+
+  softmax = torch.nn.Softmax(dim=1)
+  probs = softmax(logits)[0]
+  probs = probs.cpu().detach().numpy()
+  max_index = np.argmax(probs)
+  emotion = labels[max_index]
+  return emotion
+  
+#Creating the interface for the radio app
+demo = gr.Interface(get_category, inputs=gr.inputs.File(label='Upload your .txt file here'),
+                    outputs = 'text',
+                    title='News Article Categorization')
+                    
+                    
+#Launching the radio app
+if __name__ == '__main__':
+  demo.launch(debug=True)