Update app.py

app.py

@@ -1,3 +1,39 @@
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 import sklearn
 import gradio as gr
 import joblib
@@ -9,38 +45,67 @@ import dateutil.parser as dp
 import pandas as pd
 from huggingface_hub import hf_hub_url, cached_download
 import time
-import datetime
-
-title = "Stockholm Highway E4 Real Time Traffic Prediction"
-description = "Stockholm E4 (59°23'44.7"" N 17°59'00.4""E) highway real time traffic prediction, updated in every hour"
-
-inputs = [gr.Dataframe(row_count = (1, "fixed"), col_count=(7,"fixed"), label="Input Data", interactive=1)]
-
-outputs = [gr.Dataframe(row_count = (1, "fixed"), col_count=(1, "fixed"), label="Predictions", headers=["Congestion Level"])]
-
-model = joblib.load("./traffic_model.pkl")
 
-response_smhi = requests.get(
+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)
+    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
 
-def infer(input_dataframe):
-  return pd.DataFrame(model.predict(input_dataframe))
+    # Use current time
+    referenceTime = time.time()
+    
+    row           ={"referenceTime": referenceTime, 
+                        "temperature": t, 
+                        "wind speed": ws, 
+                        "precipation last hour": prec1h, 
+                        "snow precipation last hour": fesn1h, 
+                        "visibility": vis, 
+                        "confidence of data": confidence}
     
-referenceTime = dp.parse(json_response_smhi["referenceTime"]).timestamp()
+    row = pd.DataFrame([row], columns=row.keys())
+    print(row)
+    row.dropna(axis=0, inplace=True)
+    return row
 
+model = joblib.load(cached_download(
+    hf_hub_url("tilos/Traffic_Prediction", "traffic_model.pkl")
+))
 
-def get_time():
-    return datetime.datetime.now()
+def infer(input_dataframe):
+  return pd.DataFrame(model.predict(input_dataframe)).clip(0, 1)
+
+title = "Stoclholm Highway E4 Real Time Traffic Prediction"
+description = "Stockholm E4 (59°23'44.7"" N 17°59'00.4""E) highway real time traffic prediction"
 
-#with gr.Blocks() as demo:
-#    with gr.Row():
-#        with gr.Column():
-#            c_time2 = gr.Textbox(label="Current Time refreshed every second")           
+inputs = [gr.Dataframe(row_count = (1, "fixed"), col_count=(7,"fixed"), 
+                       headers=["referenceTime", "t", "ws", "prec1h", "fesn1h", "vis", "confidence"], 
+                       # datatype=["timestamp", "float", "float", "float", "float", "float"],
+                       label="Input Data", interactive=1)]
 
-#    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
+outputs = [gr.Dataframe(row_count = (1, "fixed"), col_count=(1, "fixed"), label="Predictions", headers=["Congestion Level"])]
 
-    
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
@@ -48,18 +113,19 @@ with gr.Blocks() as demo:
                        headers=["referenceTime", "t", "ws", "prec1h", "fesn1h", "vis", "confidence"], 
                        # datatype=["timestamp", "float", "float", "float", "float", "float"],
                        label="Input Data", interactive=1)
-        with gr.Column():
-            c_time2 = gr.Textbox(label="Current Time refreshed every second")
-    
-            
         with gr.Column():
             gr.Dataframe(row_count = (1, "fixed"), col_count=(1, "fixed"), label="Predictions", headers=["Congestion Level"])
-            
-    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
+    demo.load(get_row, every=10)
     
     with gr.Row():
         btn_sub = gr.Button(value="Submit")
     
     btn_sub.click(infer, inputs = inputs, outputs = outputs)
+        
+
+    
+# interface = gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=title, description=description, examples=[get_row()], cache_examples=False)
+# interface.launch()
 
-demo.queue().launch()
+if __name__ == "__main__":
+    demo.queue().launch()