Upload app y requierements

app.py

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+import pickle
+import pandas as pd
+import numpy as np
+import gradio as gr
+import pathlib
+#plt = platform.system()
+#if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
+
+with open('lin_reg.bin', 'rb') as f_in:
+    (dv, model) = pickle.load(f_in)
+
+def prepare_features(ride):
+    features = {}
+    features['PU_DO'] = '%s_%s' % (ride['PULocationID'], ride['DOLocationID'])
+    features['trip_distance'] = ride['trip_distance']
+    return features
+    
+def predict(features):
+    X = dv.transform(features)
+    preds = model.predict(X)
+    return float(preds[0])
+
+def main(X1,X2):
+    """request input, preprocess it and make prediction"""
+    input_data = {
+    "PU_DO": X1,
+    "trip_distance": X2
+    }
+    features = prepare_features(input_data)
+    pred = predict(features)
+
+    result = {
+        'duration': pred
+    }
+
+    return result
+
+def classify_image(img):
+#create input and output objects
+#input
+input1 = gr.inputs.Number()
+input2 = gr.inputs.Number()
+
+#output object
+output = gr.outputs.Textbox() 
+
+intf = gr.Interface(title = "New York taxi duration prediction",
+                    description = "The objective of this project is to predict the duration of a taxi trip in the city of New York.",
+                    fn=main, 
+                    inputs=[input1,input2], 
+                    outputs=[output], 
+                    live=True,
+                    enable_queue=True
+                    )
+intf.launch()

requirements.txt

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+pandas==2.1.1
+scikit-learn==1.3.1