Update app.py

app.py

@@ -2,30 +2,31 @@ import gradio as gr
 import numpy as np
 from tensorflow import keras
 
-# Load the pre-trained model
-model = keras.models.load_model('omni_rnn_0.h5')
+# Load the pre-trained models
+speed_model = keras.models.load_model('omni_rnn_0.h5')
+power_model = keras.models.load_model('power_model.h5')  # Load the second model for power prediction
 
-def predict(year, proton_density, temperature):
+def predict_speed(year, proton_density, temperature):
+    # Your existing speed prediction code here
+
+def predict_power(speed):
     try:
         # Ensure the input data has the correct types and ranges
-        year = int(year)
-        proton_density = float(proton_density)
-        temperature = float(temperature)
+        speed = float(speed)
 
         # Ensure the input data is within valid ranges
-        if year < 0 or proton_density < 0 or temperature < 0:
-            return {"error": "Input values should be non-negative"}
+        if speed < 0:
+            return {"error": "Speed should be non-negative"}
 
         # Convert input data to a NumPy array
-        input_data = np.array([year, proton_density, temperature]).reshape(1, -1)
+        input_data = np.array([speed]).reshape(1, -1)
 
-        # Make predictions using the loaded model
-        predictions = model.predict(input_data)
+        # Make predictions using the loaded power model
+        power_prediction = power_model.predict(input_data)
 
         # Format the predictions as a dictionary
         result = {
-            "predicted_speed": float(predictions[0, 0]),
-            "predicted_field_magnitude": float(predictions[0, 1])
+            "predicted_power": float(power_prediction[0])
         }
 
         return result
@@ -33,11 +34,21 @@ def predict(year, proton_density, temperature):
     except Exception as e:
         return {"error": str(e)}
 
-iface = gr.Interface(
-    fn=predict,
+# Create the Gradio interfaces
+speed_interface = gr.Interface(
+    fn=predict_speed,
     inputs=["text", "text", "text"],  # Year, Proton Density, Temperature
     outputs="json",
-    title="ReconXploration" 
+    title="Solar Wind Speed Prediction"
+)
+
+power_interface = gr.Interface(
+    fn=predict_power,
+    inputs="text",  # Speed prediction from the previous step
+    outputs="json",
+    title="Solar Wind Power Generation Prediction"
 )
 
-iface.launch()
+# Launch the interfaces
+speed_interface.launch()
+power_interface.launch()