Update app.py

app.py

@@ -1,42 +1,4 @@
-import pip
+pip show pickle torch numpy
 
-# Install dependencies from requirements.txt
-with open("requirements.txt", "r") as f:
-    requirements = f.readlines()
-pip.main(["install"] + requirements)
-
-import gradio as gr
-import pickle
-
-def read_pickle(path, saved_model_name: str):
-    """Loads a pickled model from the specified path."""
-    with open(path + saved_model_name + '.pickle', 'rb') as to_read:
-        model = pickle.load(to_read)
-    return model
-
-def automatidata(VendorID, passenger_count, Distance, Duration, rush_hour):
-    """Predicts the taxi fare using the loaded model."""
-    inputs = [[VendorID, passenger_count, Distance, Duration, rush_hour]]
-    prediction = model.predict(inputs)
-    prediction_value = prediction[0][0]
-    return f"Fare amount(approx.) = ${round(prediction_value, 2)}"
-
-if __name__ == "__main__":
-    path = 'F:/Case study/Interview preparation/01.Project/1. Automatidata/Final/'
-    model = read_pickle(path, 'Automatidata_gui')
-
-    automatidata_ga = gr.Interface(
-        fn=automatidata,
-        inputs=[
-            gr.Number(1, 2, label="VendorID - [1, 2]"),
-            gr.Number(0, 6, label="Passenger Count"),
-            gr.Number(label="Distance"),
-            gr.Number(label="Duration"),
-            gr.Number(0, 1, label="Rush Hour")
-        ],
-        outputs="text",
-        title="Taxi Fares Estimater",
-        description="Predicting Taxi Fare Amount Using Machine Learning.",
-    )
-
-    automatidata_ga.launch(auth=('user', 'auto'), share=True)
+# Install if not found
+pip install pickle torch numpy