Update app.py

app.py

@@ -24,42 +24,45 @@ model = joblib.load("./traffic_model.pkl")
 def infer(input_dataframe):
   return pd.DataFrame(model.predict(input_dataframe))
 
-response_tomtom = requests.get(
-                'https://api.tomtom.com/traffic/services/4/flowSegmentData/absolute/10/json?key=azGiX8jKKGxCxdsF1OzvbbWGPDuInWez&point=59.39575,17.98343')
-json_response_tomtom = json.loads(response_tomtom.text)  # get json response
-
-currentSpeed = json_response_tomtom["flowSegmentData"]["currentSpeed"]
-freeFlowSpeed = json_response_tomtom["flowSegmentData"]["freeFlowSpeed"]
-congestionLevel = currentSpeed/freeFlowSpeed
-
-confidence = json_response_tomtom["flowSegmentData"]["confidence"] # Reliability of the traffic data, by percentage
-
-
-# Get weather data from SMHI, updated hourly
-
-response_smhi = requests.get(
-            'https://opendata-download-metanalys.smhi.se/api/category/mesan1g/version/2/geotype/point/lon/17.983/lat/59.3957/data.json')
-json_response_smhi = json.loads(response_smhi.text) 
-
-# weather data manual https://opendata.smhi.se/apidocs/metanalys/parameters.html#parameter-wsymb
-referenceTime = dp.parse(json_response_smhi["referenceTime"]).timestamp()
-
-t             = json_response_smhi["timeSeries"][0]["parameters"][0]["values"][0] # Temperature
-ws            = json_response_smhi["timeSeries"][0]["parameters"][4]["values"][0] # Wind Speed
-prec1h        = json_response_smhi["timeSeries"][0]["parameters"][6]["values"][0] # Precipation last hour
-fesn1h        = json_response_smhi["timeSeries"][0]["parameters"][8]["values"][0] # Snow precipation last hour
-vis           = json_response_smhi["timeSeries"][0]["parameters"][9]["values"][0] # Visibility
-
-
-row           ={"referenceTime": referenceTime, 
-                    "t": t, 
-                    "ws": ws, 
-                    "prec1h": prec1h, 
-                    "fesn1h": fesn1h, 
-                    "vis": vis, 
-                    "confidence": confidence}
-
-row = pd.DataFrame([row], columns=row.keys())
-print(row)
-
-gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=title, description=description, examples=[row]).launch()
+def get_row():
+    response_tomtom = requests.get(
+                    'https://api.tomtom.com/traffic/services/4/flowSegmentData/absolute/10/json?key=azGiX8jKKGxCxdsF1OzvbbWGPDuInWez&point=59.39575,17.98343')
+    json_response_tomtom = json.loads(response_tomtom.text)  # get json response
+    
+    currentSpeed = json_response_tomtom["flowSegmentData"]["currentSpeed"]
+    freeFlowSpeed = json_response_tomtom["flowSegmentData"]["freeFlowSpeed"]
+    congestionLevel = currentSpeed/freeFlowSpeed
+    
+    confidence = json_response_tomtom["flowSegmentData"]["confidence"] # Reliability of the traffic data, by percentage
+    
+    
+    # Get weather data from SMHI, updated hourly
+    
+    response_smhi = requests.get(
+                'https://opendata-download-metanalys.smhi.se/api/category/mesan1g/version/2/geotype/point/lon/17.983/lat/59.3957/data.json')
+    json_response_smhi = json.loads(response_smhi.text) 
+    
+    # weather data manual https://opendata.smhi.se/apidocs/metanalys/parameters.html#parameter-wsymb
+    referenceTime = dp.parse(json_response_smhi["referenceTime"]).timestamp()
+    
+    t             = json_response_smhi["timeSeries"][0]["parameters"][0]["values"][0] # Temperature
+    ws            = json_response_smhi["timeSeries"][0]["parameters"][4]["values"][0] # Wind Speed
+    prec1h        = json_response_smhi["timeSeries"][0]["parameters"][6]["values"][0] # Precipation last hour
+    fesn1h        = json_response_smhi["timeSeries"][0]["parameters"][8]["values"][0] # Snow precipation last hour
+    vis           = json_response_smhi["timeSeries"][0]["parameters"][9]["values"][0] # Visibility
+    
+    
+    row           ={"referenceTime": referenceTime, 
+                        "t": t, 
+                        "ws": ws, 
+                        "prec1h": prec1h, 
+                        "fesn1h": fesn1h, 
+                        "vis": vis, 
+                        "confidence": confidence}
+    
+    row = pd.DataFrame([row], columns=row.keys())
+    print(row)
+
+    return row
+
+gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=title, description=description, examples=[get_row()]).launch()