Update app.py

app.py

@@ -1,7 +1,49 @@
 import gradio as gr
+import pandas as pd
+from datasets import load_dataset
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 1️⃣ Load & prepare data (runs once at startup)
+ds = load_dataset("notadib/NASA-Power-Daily-Weather", split="train")
+df = pd.DataFrame(ds)[["RH2M", "PRECTOTCORR", "ALLSKY_SFC_SW_DWN", "T2M"]].dropna()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+X = df[["RH2M", "PRECTOTCORR", "ALLSKY_SFC_SW_DWN"]]
+y = df["T2M"]
+
+# use a small subset so startup stays fast
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+model = RandomForestRegressor(n_estimators=50, random_state=42)
+model.fit(X_train, y_train)
+
+# 2️⃣ Define your prediction function
+def predict_temperature(rh2m, prectotcorr, solar):
+    """Given humidity, precipitation, and solar radiation, predict temperature."""
+    val = model.predict([[rh2m, prectotcorr, solar]])[0]
+    return round(float(val), 2)
+
+# 3️⃣ Build the Gradio interface
+demo = gr.Interface(
+    fn=predict_temperature,
+    inputs=[
+        gr.Number(label="Relative Humidity (%)", value=50, precision=1),
+        gr.Number(label="Precipitation (mm)", value=1.0, precision=2),
+        gr.Number(label="Solar Radiation (W/m²)", value=200.0, precision=1),
+    ],
+    outputs=gr.Number(label="Predicted Temp (°C)"),
+    title="🌍 ClimatePredict: Daily Temperature Forecast",
+    description=(
+        "This demo uses a Random Forest model trained on NASA POWER daily weather data. "
+        "Adjust the inputs and click **Submit** to see the forecasted temperature."
+    ),
+    examples=[
+        [60, 0.5, 180],
+        [30, 2.0, 300],
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()