Add application file

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+
+model = joblib.load("nb_model.pickle")
+
+def return_output(text):
+    """Alternative function to output predictions as simple text"""
+    output = predict(text)
+    output_string = f"Device: {output[0]}\n\nProbability: {output[1]}"
+    return output_string
+
+
+def predict(text):
+
+    data = pd.DataFrame({"text": [text]})
+    #prediction_class = model.predict(data)[0]
+    #prediction_prob = round(model.predict_proba(data).max(), 3)
+    #return prediction_class, prediction_prob
+    pred = model.predict_proba(data)[0]
+    return {'Android': pred[0], 'iPhone': pred[1]}
+
+
+description = "According to the dataset used for this model, Trump  mainly uses two devices for tweeting" \
+              " - an Android and an iPhone device.\nIt seems likely that members of his staff are tweeting on his" \
+              " behalf using iPhone.\nTry and see if you can write an 'iPhone' and an 'Android' tweet."
+
+iface = gr.Interface(fn=predict,
+                     #fn=return_output,
+                     inputs="text",
+                     #outputs="text",
+                     outputs="label",
+                     allow_flagging=False, title="iPhone or Android?",
+                     interpretation="default", description=description)
+
+iface.launch()

requirements.txt

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+pandas
+sklearn
+joblib