Updated app at mån 8 jan 2024 18:02:37 CET

app.py

@@ -0,0 +1,71 @@
+import hopsworks
+import joblib
+import gradio as gr
+from datetime import datetime, timedelta
+
+
+def predict_bridge_opening(input_date, input_time):
+    """
+    Predicts when the Södertälje Mälarbro will open.
+    :param input_date: The date to predict for.
+    :param input_time: The time to predict for.
+    :return: A dictionary of predictions for different time spans.
+    """
+    try:
+        # Combine the date and time inputs
+        input_date_time = f"{input_date} {input_time}"
+        date_time = datetime.strptime(input_date_time, '%Y-%m-%d %H:%M')
+
+        # Define time spans
+        half_hour_span = (date_time - timedelta(minutes=30), date_time + timedelta(minutes=30))
+        one_hour_span = (date_time - timedelta(hours=1), date_time + timedelta(hours=1))
+        two_hour_span = (date_time - timedelta(hours=2), date_time + timedelta(hours=2))
+        today_span = (date_time.replace(hour=0, minute=0, second=0, microsecond=0),
+                      date_time.replace(hour=23, minute=59, second=59, microsecond=999999))
+
+        # Dummy predictions (just user interface testing)
+        predictions = [False, False, True, True]
+
+        # predictions = model.predict(date_time)
+
+        # Return formatted predictions
+        return {
+            "30 Minute Span": f"{half_hour_span[0]} to {half_hour_span[1]}: {predictions[0]}",
+            "1 Hour Span": f"{one_hour_span[0]} to {one_hour_span[1]}: {predictions[1]}",
+            "2 Hour Span": f"{two_hour_span[0]} to {two_hour_span[1]}: {predictions[2]}",
+            "Today": f"{today_span[0]} to {today_span[1]}: {predictions[3]}"
+        }
+    except Exception as e:
+        return str(e)
+
+
+# TODO: Enable when model is ready
+# print("Logging in to Hopsworks...")
+# project = hopsworks.login()
+#
+# print("Getting feature store...")
+# fs = project.get_feature_store()
+#
+# print("Getting model registry...")
+# mr = project.get_model_registry()
+#
+# print("Getting model: ...")
+# model = mr.get_model("iris_model", version=1)
+#
+# print("Downloading model...")
+# model_dir = model.download()
+#
+# print("Initializing model locally...")
+# model = joblib.load(model_dir + "/bridge_model.pkl")
+
+print("Configuring gradio interface...")
+iface = gr.Interface(
+    fn=predict_bridge_opening,
+    inputs=[gr.inputs.Date(label="Select Date"), gr.inputs.Time(label="Select Time")],
+    outputs=gr.outputs.JSON(label="Bridge Opening Predictions"),
+    title="Södertälje Mälarbro Bridge Opening Predictor",
+    description="Predict when the Södertälje Mälarbro will open. Select a date and time to see dummy predictions."
+)
+
+print("Launching gradio...")
+iface.launch()

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1