correct app

app.py

@@ -1,7 +1,67 @@
 import gradio as gr
+import joblib
+import numpy as np
+import sklearn
+import pandas as pd
 
-def greet(name):
-	return f"Hello, {name}"
+model = joblib.load("joblib/rf_classifier_CA.joblib")
+qod_encoder = joblib.load("joblib/qod_encoder_CA.joblib")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def predict(pressure, temperature, humidity, visibility, wind_speed, precipitation, amenity,
+            bump, crossing, give_way, junction, no_exit, railway, station,
+            stop, traffic_signal, quarter):
+	encoded_quarter = qod_encoder.transform(pd.DataFrame([[quarter]], columns=qod_encoder.feature_names_in_))[0][0]
+
+	# Ensure checkbox values default to False if None
+	checkbox_values = [amenity, bump, crossing, give_way, junction, no_exit,
+	                   railway, station, stop, traffic_signal]
+
+	checkbox_values = [bool(value) if value is not None else False for value in checkbox_values]
+
+	print("Inputs Received:")
+	print(
+		f"quarter: {quarter} - {encoded_quarter}, pressure: {pressure}, temperature: {temperature}, "
+		f"humidity: {humidity}, visibility: {visibility}, wind_speed: {wind_speed}, "
+		f"precipitation: {precipitation}, "
+		f"amenity: {amenity}, bump: {bump}, crossing: {crossing}, give_way: {give_way}, "
+		f"junction: {junction}, no_exit: {no_exit}, railway: {railway}, "
+		f"station: {station}, stop: {stop}, traffic_signal: {traffic_signal}")
+
+	# Convert features to the required format
+	input_data = np.array([pressure, temperature, humidity, visibility,
+	                       wind_speed, precipitation, amenity, bump, crossing,
+	                       give_way, junction, no_exit, railway, station, stop, traffic_signal,
+	                       encoded_quarter]).reshape(1, -1)
+	prediction = model.predict(input_data)
+	return prediction[0]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+
+        gr.Number(label="Pressure(in)", minimum=27.31, maximum=30.61, value=27.31),
+        gr.Number(label="Temperature(in)", minimum=15.8, maximum=117, value=15.8),
+        gr.Number(label="Humidity(%)", minimum=2, maximum=100, value=100),
+        gr.Number(label="Visibility(mi)", minimum=0, maximum=80, value=0),
+        gr.Number(label="Wind Speed(mph)", minimum=0, maximum=73.6, value=0),
+        gr.Number(label="Precipitation", minimum=0, maximum=1, value=0),
+
+        gr.Checkbox(label="Amenity"),
+        gr.Checkbox(label="Bump"),
+        gr.Checkbox(label="Crossing"),
+        gr.Checkbox(label="Give Way"),
+        gr.Checkbox(label="Junction"),
+        gr.Checkbox(label="No Exit"),
+        gr.Checkbox(label="Railway"),
+        gr.Checkbox(label="Station"),
+        gr.Checkbox(label="Stop"),
+        gr.Checkbox(label="Traffic_Signal"),
+        gr.Dropdown(choices=['Q1', 'Q2', 'Q3', 'Q4'], label="Quarter of Day"),
+
+    ],
+    outputs=gr.Textbox(label="Prediction")
+)
+
+# Launch the interface
+iface.launch()

joblib/qod_encoder_CA.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26ab27c167c2f498e33b9c3056d473dbf2c70d969da111902e4b6a6328a17c5f
+size 1066

joblib/rf_classifier_CA.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84a0e314f9812de5039228d1795264c6db93bb98c048a9c28ff1340103b85e04
+size 229375065