Upload app.py

app.py

@@ -17,25 +17,20 @@ model_dict = {
     "Offensive Language Detection": "offensive"
 }
 
-def process_url(url, model_key):
-    model_name = model_dict[model_key]
+def process_url(url, model_name):
+    model_name = model_dict[model_name]
     processed_text = data_prep.process_data(url)
     final_scores = model_predict.predict_text(processed_text, model_name)
-    
-    print(f"Final Scores: {final_scores}")  # Debug statement
-
     if model_name == 'outcome':
         highest_prob_item = max(final_scores, key=lambda x: x['score'])
         highest_prob_label = highest_prob_item['outcome']
         highest_prob = highest_prob_item['score']
         progress_bars = {item['outcome']: item['score'] for item in final_scores}
-    else:
-        highest_prob_item = max(final_scores, key=lambda x: x['score'])
-        highest_prob_label = highest_prob_item[list(highest_prob_item.keys())[1]]
-        highest_prob = highest_prob_item['score']
-        progress_bars = {item[list(item.keys())[1]]: item['score'] for item in final_scores}
+        
+        return processed_text, highest_prob_label, highest_prob, progress_bars
     
-    return processed_text, highest_prob_label, highest_prob, progress_bars
+    else:
+        return processed_text, "", "", final_scores
 
 title = 'Wikipedia Deletion Discussion Analysis Suite'
 desc = """ This demo is about classifying deletion discussions from Wikipedia about Wikipedia articles. Wikipedia community engages in discussions related to an article’s quality, and map potential issues to existing templates, or Wikipedia policies, which cover diverse areas, from low notability of sources to content implausibility or vandalism.
@@ -55,16 +50,14 @@ The input to the classifier is a URL of a Wikipedia deletion discussion page, an
 The input to the classifier is a URL of a Wikipedia deletion discussion page, and the output is the predicted label of the discussion, along with the probability of the predicted label, and the probabilities of all the labels.
 """
 
-
 url_input = gr.Textbox(label="URL")
 model_name_input = gr.Dropdown(label="Choose the Task", choices=list(model_dict.keys()), value=list(model_dict.keys())[0])
 outputs = [
     gr.Textbox(label="Processed Text"),
-    gr.Textbox(label="Label with Highest Probability"),
-    gr.Textbox(label="Probability"),
-    gr.JSON(label="All Labels and Probabilities"),
-    #gr.HTML(label="Processed Text")
+    gr.Textbox(label="Label with Highest Probability"),  # This will only be used for the outcome task
+    gr.Textbox(label="Probability"),  # This will only be used for the outcome task
+    gr.JSON(label="All Labels and Probabilities")  # This will be used for all tasks
 ]
 
 demo = gr.Interface(fn=process_url, inputs=[url_input, model_name_input], outputs=outputs, title=title, description=desc)
-demo.launch() #share=True)
+demo.launch()  # share=True