Upload 5 files

app.py

@@ -0,0 +1,132 @@
+import pandas as pd
+import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
+import joblib
+import numpy as np
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.preprocessing import PolynomialFeatures, StandardScaler
+
+
+# Load the trained models & transformers
+linear_model = joblib.load("linear_model.pkl")
+poly_model = joblib.load("poly_model.pkl")
+poly_features = joblib.load("poly_features.pkl")
+scaler = joblib.load("scaler.pkl")  # Load the saved StandardScaler
+
+# Function to load and preview CSV data
+def load_data(file):
+    df = pd.read_csv(file)
+    print("DEBUG: CSV Data in Gradio:\n", df.head())  # Print first 5 rows
+    print("DEBUG: Data Types in Gradio:\n", df.dtypes)  # Check column types
+    return df.head()  # Show first 5 rows
+
+# Function to visualize population trends
+def plot_population_trend(file, model_choice):
+    df = pd.read_csv(file)
+
+    plt.figure(figsize=(8,5))
+    sns.scatterplot(x=df.iloc[:, 0], y=df.iloc[:, 1])
+    plt.xlabel("Years")
+    plt.ylabel("Population")
+    plt.title("Population Growth Trend")
+    plt.grid()
+
+    plt.figure(figsize=(8,5))
+    plt.scatter(df["Year"], df["Total_Population"], label="Actual Population", color="blue", alpha=0.6)
+
+    X = df["Year"].values.reshape(-1, 1)  # Extract Year column
+
+    if model_choice == "Linear Regression":
+        X_scaled = scaler.transform(X) 
+        predictions = linear_model.predict(X_scaled)
+        plt.plot(df["Year"], predictions, label="Linear Regression", color="red", linestyle="dashed")
+    else:  # Polynomial Regression
+        X_scaled = scaler.transform(X)  # Apply scaling
+        X_poly = poly_features.transform(X_scaled)  # Transform for Polynomial Regression
+        predictions = poly_model.predict(X_poly)
+        plt.plot(df["Year"], predictions, label="Polynomial Regression", color="green")
+
+    plt.xlabel("Year")
+    plt.ylabel("Population")
+    plt.title(f"Population Growth Prediction ({model_choice})")
+    plt.legend()
+    plt.grid()
+
+    #plt.savefig("population_trend.png")
+    #return "population_trend.png"
+
+    plt.savefig("population_trend.png")
+    return "population_trend.png"
+
+# Function to predict population using the selected model
+def predict_population(file, model_choice):
+    df = pd.read_csv(file)
+
+    # Ensure correct column format
+    if df.shape[1] < 2:
+        return None, "ERROR: CSV must contain two columns (Year, Population).", None
+    
+    df.columns = ["Year", "Population"]
+    df = df.astype({"Year": int, "Population": float})  # Convert data types
+
+    X = df["Year"].values.reshape(-1, 1)  # Extract Year column
+
+    if model_choice == "Linear Regression":
+        # Do NOT scale X for Linear Regression
+        X_scaled = scaler.transform(X) 
+        predictions = linear_model.predict(X_scaled)  
+    else:  # Polynomial Regression
+        X_scaled = scaler.transform(X)  # Apply the same scaling as training
+        X_poly = poly_features.transform(X_scaled)  # Transform for Polynomial Regression
+        predictions = poly_model.predict(X_poly)
+
+
+
+    df["Predicted Population"] = predictions  # Append predictions to DataFrame
+
+    # Extract the test set (2016-2020) before computing MSE & R²
+    test_mask = df["Year"].between(2016, 2020)  # Select only test years
+    X_test = df.loc[test_mask, "Year"].values.reshape(-1, 1)
+    y_test = df.loc[test_mask, "Population"].values
+    y_pred_test = df.loc[test_mask, "Predicted Population"].values
+
+    # Compute metrics only on the test set
+    mse = mean_squared_error(y_test, y_pred_test)
+    r2 = r2_score(y_test, y_pred_test)
+
+    print("DEBUG: Model Choice =", model_choice)
+    print("DEBUG: X Values for Prediction:\n", X[:5])  # Print first 5 inputs
+    print("DEBUG: Predictions:\n", predictions[:5])  # Print first 5 predictions
+
+    return df, "population_trend.png", f"{model_choice} Results: MSE = {mse:.2f}, R² Score = {r2:.2f}"
+
+
+
+# Wrapper function for Gradio
+def gradio_interface(file, model_choice):
+    preview = load_data(file)
+    trend_image = plot_population_trend(file, model_choice)
+    predictions, _, performance = predict_population(file, model_choice)
+    return preview, trend_image, predictions, performance
+
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=gradio_interface,  # Use the single wrapper function
+    inputs=[
+        gr.File(label="Upload CSV File"),
+        gr.Radio(["Linear Regression", "Polynomial Regression"], label="Choose Model")
+    ],
+    outputs=[
+        gr.Dataframe(label="Preview Data"),
+        gr.Image(label="Population Trend"),
+        gr.Dataframe(label="Predictions"),
+        gr.Textbox(label="Model Performance")
+    ],
+    title="Population Prediction Tool",
+    description="Upload a CSV file with Year and Population data. Choose a model (Linear or Polynomial Regression) to predict future population trends."
+)
+
+# Launch the Gradio App
+interface.launch()

linear_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e7b8ab6a96218c4a23bf8050255dae8cf468bb005574b1ecaffe2de4c3d70d4
+size 561

poly_features.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55a6ff11301e34be39169450c78b5f545a4737236f6a226d315d378f47e7fa87
+size 255

poly_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4195848735a33af474b374c688a41aad5ed9b29839c2c95858a3cdfd7f12ac0f
+size 617

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e664c813a5425937bb9a229017322def3636acfd71d341f8ce0a06ad528c2fd
+size 623