Create app.py

app.py

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+import os
+import subprocess
+import numpy as np
+import pandas as pd
+import gradio as gr
+import zipfile
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestRegressor
+
+# Ensure required packages are installed
+try:
+    import gradio as gr
+except ImportError:
+    subprocess.run(["pip", "install", "gradio"], check=True)
+    import gradio as gr
+
+# Define file paths
+zip_file_path = "AI-powered Weather Forecasting.zip"
+extract_folder = "weather_forecasting_dataset"
+
+# Extract the ZIP file
+with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+    zip_ref.extractall(extract_folder)
+
+# Locate CSV file
+csv_file_path = os.path.join(extract_folder, "weatherHistory.csv")
+
+# Load the dataset
+df = pd.read_csv(csv_file_path)
+
+# Convert 'Formatted Date' to datetime format
+df['Formatted Date'] = pd.to_datetime(df['Formatted Date'], utc=True)
+df.set_index('Formatted Date', inplace=True)
+
+# ✅ Drop unnecessary columns
+df.drop(columns=['Summary', 'Daily Summary', 'Apparent Temperature (C)'], inplace=True)
+
+# ✅ Fill missing values in 'Precip Type'
+df['Precip Type'].fillna("rain", inplace=True)
+
+# ✅ Encode categorical variable 'Precip Type'
+le = LabelEncoder()
+df['Precip Type'] = le.fit_transform(df['Precip Type'])
+
+# ✅ Feature engineering: Extract time-based features
+df['Year'] = df.index.year
+df['Month'] = df.index.month
+df['Day'] = df.index.day
+df['Hour'] = df.index.hour
+
+# ✅ Print final feature names before training
+print("✅ Final Training Features:", df.columns.tolist())
+
+# Define target variable (temperature prediction)
+X = df.drop(columns=['Temperature (C)'])
+y = df['Temperature (C)']
+
+# ✅ Store feature names
+feature_names = X.columns.tolist()
+num_features = len(feature_names)
+
+# ✅ Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# ✅ Train RandomForestRegressor
+model = RandomForestRegressor(n_estimators=500, random_state=42)
+model.fit(X_train, y_train)
+
+# ✅ Debug: Print feature importance
+feature_importance = model.feature_importances_
+print("📊 Feature Importance:", dict(zip(feature_names, feature_importance)))
+
+# ✅ Define Prediction Function
+def predict_temperature(precip_type, humidity, wind_speed, wind_bearing, visibility, pressure, loud_cover, year, month, day, hour):
+    try:
+        # ✅ Encode categorical variable
+        precip_type_encoded = le.transform([precip_type])[0]
+
+        # ✅ Create correct sample data (Ensures 12 Features)
+        sample_data = np.array([[precip_type_encoded, humidity, wind_speed, wind_bearing, visibility, pressure, loud_cover, year, month, day, hour]])
+
+        # ✅ Debug: Print input features before prediction
+        print("🔹 Prediction Input Features:", feature_names)
+        print("🔹 Prediction Input Sample:", sample_data)
+
+        # ✅ Fix feature mismatch by adding missing feature if needed
+        if sample_data.shape[1] < num_features:
+            missing_features = num_features - sample_data.shape[1]
+            sample_data = np.hstack((sample_data, np.zeros((1, missing_features))))
+            print(f"⚠️ Added {missing_features} missing features to match model training!")
+
+        # ✅ Debug: Print adjusted sample data
+        print("🔹 Adjusted Sample Data:", sample_data)
+
+        # ✅ Predict temperature
+        prediction = model.predict(sample_data)[0]
+
+        # ✅ Debug: Print final prediction value
+        print("🔥 Final Prediction:", prediction)
+
+        return f"Predicted Temperature: {prediction:.2f}°C"
+    except Exception as e:
+        return f"Error: {e}"
+
+# ✅ Gradio UI
+inputs = [
+    gr.Radio(["rain", "snow"], label="Precip Type"),
+    gr.Number(label="Humidity"),
+    gr.Number(label="Wind Speed (km/h)"),
+    gr.Number(label="Wind Bearing (degrees)"),
+    gr.Number(label="Visibility (km)"),
+    gr.Number(label="Pressure (millibars)"),
+    gr.Number(label="Loud Cover", value=0.0),  # Default to 0 if always 0
+    gr.Number(label="Year"),
+    gr.Number(label="Month"),
+    gr.Number(label="Day"),
+    gr.Number(label="Hour"),
+]
+
+demo = gr.Interface(fn=predict_temperature, inputs=inputs, outputs="text", title="AI-Powered Weather Forecasting")
+
+demo.launch()