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rainfall_app.py

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+
+import numpy as np
+import joblib
+from tensorflow.keras.models import load_model
+import gradio as gr
+
+# Load the saved scaler and model
+scaler = joblib.load('scaler.joblib')
+model = load_model('rainfall_prediction_model.h5')
+
+def predict_rainfall(Dew_Point, Pressure, Gust_Speed, RH, Wind_Direction,
+                     Wind_Speed, Temperature, Rained, Water_Content, Solar_Radiation):
+    # Preprocess the input data
+    input_data = np.array([[Dew_Point, Pressure, Gust_Speed, RH, Wind_Direction,
+                            Wind_Speed, Temperature, Rained, Water_Content, Solar_Radiation]])
+    input_data_scaled = scaler.transform(input_data)
+    input_data_scaled = input_data_scaled.reshape((input_data_scaled.shape[0], 1, input_data_scaled.shape[1]))
+    
+    # Make a prediction
+    prediction = model.predict(input_data_scaled)
+    
+    # Output the prediction
+    return 'Rain' if prediction[0][0] > 0 else 'No Rain'
+
+# Gradio Interface
+inputs = [
+    gr.inputs.Number(label="Dew Point"),
+    gr.inputs.Number(label="Pressure"),
+    gr.inputs.Number(label="Gust Speed"),
+    gr.inputs.Number(label="Relative Humidity"),
+    gr.inputs.Number(label="Wind Direction"),
+    gr.inputs.Number(label="Wind Speed"),
+    gr.inputs.Number(label="Temperature"),
+    gr.inputs.Number(label="Rained"),
+    gr.inputs.Number(label="Water Content"),
+    gr.inputs.Number(label="Solar Radiation")
+]
+output = gr.outputs.Textbox(label="Prediction")
+
+gr.Interface(fn=predict_rainfall, inputs=inputs, outputs=output, title="Rainfall Prediction").launch()
+

rainfall_prediction_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cda417df87e791a82bc48abd93061cada7ddaea9cad5c7dda231431047234783
+size 506208

requirements.txt

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+
+numpy
+joblib
+tensorflow
+gradio
+

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dff6e925219a9a98856668c6c02016b7b68501ad95e891d408eb34cf261d1ffb
+size 1319