Create app.py

app.py

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+import torch
+import gradio
+from transformers import TextClassificationPipeline, DistilBertTokenizer, DistilBertForSequenceClassification
+
+# model path in hugginface
+model_path = "yabramuvdi/distilbert-wfh"
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+model = DistilBertForSequenceClassification.from_pretrained(model_path)
+
+# create a pipeline for predictions
+classifier = TextClassificationPipeline(model=model,
+                                        tokenizer=tokenizer,
+                                        return_all_scores=True)
+                                        
+# basic elements of page
+title = "Work From Home Predictor"
+description = "Demo application that predicts the pressence of work from home in any given sequence of text."
+article = ""    # text at the end of the app
+examples = [
+    ["This is a work from home position", 0.998],
+    ["This position does not allow working from home", 0.998],
+    ]
+
+#%%
+
+def predict_wfh(input_text, input_slider):
+    
+    # get scores from model
+    predictions = classifier(input_text)[0]
+
+    # use selected threshold to classify as WFH
+    prob_wfh = predictions[1]["score"]
+    if prob_wfh > input_slider:
+        wfh = 1
+        no_wfh = 0
+    else:
+        wfh = 0
+        no_wfh = 1
+
+    return({"Not work from home": no_wfh, "Work from home": wfh}, f"Probability of WFH: {np.round(prob_wfh, 3)}")
+
+
+label = gr.outputs.Label(num_top_classes=1, type="confidences", label="Binary classification")
+text_output = gr.outputs.Textbox(type="auto", label="Predicted probability")
+
+app = gr.Interface(fn=[predict_wfh],
+                  inputs=[gr.inputs.Textbox(lines=10, label="Input text"), gr.inputs.Slider(0, 1, 0.001, label="Classification threshold")],
+                  outputs=[label, text_output],
+                  theme="huggingface",
+                  title=title,
+                  description=description,
+                  article=article,
+                  examples=examples,
+                  allow_flagging="manual",
+                  flagging_options=["mistake", "borderline"]
+                  )
+
+app.launch()