app.py

# -*- coding: utf-8 -*-
# ==================================================
# app.py - Gradio App for Hugging Face Spaces
# Campus Placement Prediction
# ==================================================

import gradio as gr
import pandas as pd
import joblib
import numpy as np
import matplotlib.pyplot as plt # Only needed for figure type hint potentially
import seaborn as sns # Not directly used if images are pre-generated
import os
import warnings

warnings.filterwarnings('ignore')

# --- Configuration: Relative Paths for HF Spaces ---
# Ensure these files are uploaded to your HF Space repository
MODEL_FILENAME = 'placement_model_pipeline.joblib'
LABEL_ENCODER_FILENAME = 'placement_label_encoder.joblib'
FEATURES_FILENAME = 'placement_model_features.joblib'
DATA_FILE = 'Campus_Selection.csv' # Original data file
PLOT_DIR = 'plots' # Subdirectory for plots
FEATURE_IMPORTANCE_PLOT = os.path.join(PLOT_DIR, 'feature_importance.png')
PLACEMENT_PIE_CHART = os.path.join(PLOT_DIR, 'placement_distribution.png')
CORRELATION_HEATMAP = os.path.join(PLOT_DIR, 'correlation_heatmap.png')

# --- Global Variables to Hold Loaded Objects ---
pipeline = None
label_encoder = None
feature_names = None
df_original = None
df_head = pd.DataFrame() # Default empty dataframe
dataset_stats = "Dataset information not available."

# --- Load Model and Preprocessing Objects ---
print("Attempting to load model artifacts...")
try:
    if os.path.exists(MODEL_FILENAME):
        pipeline = joblib.load(MODEL_FILENAME)
        print(f"- Loaded: {MODEL_FILENAME}")
    else:
        print(f"Error: Model file not found at {MODEL_FILENAME}")
        # gr.Error(f"Model file '{MODEL_FILENAME}' not found. Cannot make predictions.") # Use if you want error banner on load

    if os.path.exists(LABEL_ENCODER_FILENAME):
        label_encoder = joblib.load(LABEL_ENCODER_FILENAME)
        print(f"- Loaded: {LABEL_ENCODER_FILENAME}")
    else:
        print(f"Error: Label encoder file not found at {LABEL_ENCODER_FILENAME}")
        # gr.Error(f"Label encoder file '{LABEL_ENCODER_FILENAME}' not found.")

    if os.path.exists(FEATURES_FILENAME):
        feature_names = joblib.load(FEATURES_FILENAME)
        print(f"- Loaded: {FEATURES_FILENAME}")
    else:
        print(f"Error: Feature names file not found at {FEATURES_FILENAME}")
        # gr.Error(f"Feature names file '{FEATURES_FILENAME}' not found.")

    if pipeline and label_encoder and feature_names:
        print("All essential model artifacts loaded successfully.")
    else:
        print("Warning: One or more essential model artifacts failed to load. Prediction functionality may be limited.")

except Exception as e:
    print(f"Error loading model artifacts: {e}")
    # Optionally raise a Gradio error to be visible in the UI on load
    # gr.Error(f"Failed to load model artifacts: {e}")


# --- Load Original Data for Overview Tab ---
print("Attempting to load original dataset...")
try:
    if os.path.exists(DATA_FILE):
        df_original = pd.read_csv(DATA_FILE)
        df_head = df_original.head(10)
        dataset_stats = f"**Number of Records:** {len(df_original)}\n\n**Columns:** {len(df_original.columns)}"
        print(f"- Loaded: {DATA_FILE}")
    else:
        print(f"Warning: Original data file '{DATA_FILE}' not found for overview tab.")
        dataset_stats = f"Original dataset file '{DATA_FILE}' not found."

except Exception as e:
    print(f"Error loading original dataset: {e}")
    dataset_stats = f"Error loading original dataset: {e}"

# --- Check if Plot Files Exist (for warnings in UI) ---
plots_exist = {
    "feature_importance": os.path.exists(FEATURE_IMPORTANCE_PLOT),
    "pie_chart": os.path.exists(PLACEMENT_PIE_CHART),
    "heatmap": os.path.exists(CORRELATION_HEATMAP)
}
print(f"Plot file existence check: {plots_exist}")


# --- Define Prediction Function ---
def predict_placement(*args):
    """

    Predicts placement status based on input features.

    Returns:

        - Profile Summary (Markdown)

        - Prediction Result (Markdown)

        - Probability Plot (Matplotlib Figure or None)

    """
    # Check if essential objects are loaded
    if pipeline is None or label_encoder is None or feature_names is None:
         message = "⚠️ **Error:** Model artifacts not loaded correctly. Cannot perform prediction."
         print(message)
         return (message, "", None) # Return error message and no plot

    # Create a DataFrame from the inputs with correct column names
    try:
        input_data = pd.DataFrame([args], columns=feature_names)
    except ValueError as e:
         message = f"⚠️ **Error:** Input data mismatch with expected features. Details: {e}"
         print(message)
         return (message, "", None)

    # Prepare Profile Summary String
    profile_md = "### 🧑‍🎓 Student Profile Summary\n" + "-"*25 + "\n"
    for i, feature in enumerate(feature_names):
        label = feature.replace('_p', ' %').replace('_b', ' Board').replace('_s', ' Stream').replace('_t', ' Type').replace('workex', 'Work Experience').replace('etest', 'Employability Test').replace('ssc', 'SSC').replace('hsc', 'HSC').replace('mba', 'MBA').replace('degree','Degree').replace('specialisation','Specialisation').replace('gender','Gender').replace('_',' ').title()
        profile_md += f"**{label}:** {args[i]}\n"

    # Convert numerical inputs (sliders/numbers) to numeric types
    numerical_cols_in_features = [
        'ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'
    ]
    try:
        for col in numerical_cols_in_features:
            if col in input_data.columns:
               input_data[col] = pd.to_numeric(input_data[col])
    except ValueError as e:
        error_msg = f"Error: Invalid numeric value provided. Details: {e}"
        print(error_msg)
        return (profile_md, f"⚠️ **Prediction Error:**\n{error_msg}", None)

    # Make prediction probability
    try:
        pred_proba = pipeline.predict_proba(input_data)[0]
        predicted_class_index = np.argmax(pred_proba)
        predicted_status = label_encoder.inverse_transform([predicted_class_index])[0]
        confidence = pred_proba[predicted_class_index]

        # Format prediction result
        if predicted_status == 'Placed':
            result_md = f"## ✅ Prediction: PLACED\n**Confidence:** {confidence:.2%}"
        else:
            result_md = f"## ❌ Prediction: NOT PLACED\n**Confidence:** {confidence:.2%}"

        # Create probability bar chart
        fig, ax = plt.subplots(figsize=(5, 3)) # Smaller plot for UI
        statuses = label_encoder.classes_
        probabilities = pred_proba
        colors = ['#ff9999', '#66b3ff'] # Ensure colors match labels if needed
        # Ensure correct color mapping if classes aren't always ['Not Placed', 'Placed']
        status_color_map = {label_encoder.classes_[0]: colors[0], label_encoder.classes_[1]: colors[1]}
        bar_colors = [status_color_map[status] for status in statuses]

        bars = ax.bar(statuses, probabilities, color=bar_colors)
        ax.set_ylim(0, 1)
        ax.set_ylabel('Probability')
        ax.set_title('Placement Probability')
        for bar in bars:
            height = bar.get_height()
            ax.text(bar.get_x() + bar.get_width()/2., height, f'{height:.2%}',
                    ha='center', va='bottom', fontsize=9)
        plt.tight_layout()

        # IMPORTANT: Close the plot to prevent it from displaying in logs or consuming memory
        # We return the figure object for Gradio to render
        # plt.close(fig) # DO NOT CLOSE HERE - Gradio needs the figure object

        return profile_md, result_md, fig # Return figure object

    except Exception as e:
        error_msg = f"An error occurred during prediction: {e}"
        print(f"Error during prediction: {e}")
        print(f"Input data:\n{input_data.to_string()}")
        print(f"Input data types:\n{input_data.dtypes}")
        # Ensure plot is closed if an error occurs before returning
        try: plt.close(fig)
        except NameError: pass # fig might not be defined if error happened early
        return (profile_md, f"⚠️ **Prediction Error:**\n{error_msg}", None)


# --- Build Gradio Interface using Blocks ---
app_title = "🎓 Campus Placement Predictor"
app_description = """

Predict student placement based on academic performance, background, work experience, and MBA specialization.

Input the details below and click 'Predict'. Explore other tabs for insights.

"""

css = """

.gradio-container { font-family: 'IBM Plex Sans', sans-serif; max-width: 1200px; margin: auto; }

.gr-button { color: white; border-color: #007bff; background: #007bff; }

footer { visibility: hidden; }

.gr-label { font-weight: bold; }

h1 { text-align: center; }

"""

# Define default values (can be adjusted)
default_ssc_p = 70.0
default_hsc_p = 70.0
default_degree_p = 70.0
default_etest_p = 70.0
default_mba_p = 65.0

# Start Gradio Blocks UI Definition
app_ui = gr.Blocks(theme=gr.themes.Soft(primary_hue=gr.themes.colors.blue, secondary_hue=gr.themes.colors.sky), title=app_title, css=css)

with app_ui:
    gr.Markdown(f"<h1>{app_title}</h1>")
    gr.Markdown(app_description)

    # Define Input Components (organized)
    input_components_map = {}
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("**Personal & Secondary**")
            input_components_map['gender'] = gr.Radio(label="Gender", choices=['M', 'F'], value='M')
            input_components_map['ssc_p'] = gr.Slider(label="SSC Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_ssc_p)
            input_components_map['ssc_b'] = gr.Dropdown(label="SSC Board", choices=['Central', 'Others'], value='Central')
        with gr.Column(scale=1):
            gr.Markdown("**Higher Secondary**")
            input_components_map['hsc_p'] = gr.Slider(label="HSC Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_hsc_p)
            input_components_map['hsc_b'] = gr.Dropdown(label="HSC Board", choices=['Central', 'Others'], value='Central')
            input_components_map['hsc_s'] = gr.Dropdown(label="HSC Stream", choices=['Commerce', 'Science', 'Arts'], value='Commerce')
        with gr.Column(scale=1):
            gr.Markdown("**Degree & Experience**")
            input_components_map['degree_p'] = gr.Slider(label="Degree Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_degree_p)
            input_components_map['degree_t'] = gr.Dropdown(label="Degree Type", choices=['Comm&Mgmt', 'Sci&Tech', 'Others'], value='Comm&Mgmt')
            input_components_map['workex'] = gr.Radio(label="Work Experience", choices=['No', 'Yes'], value='No')
        with gr.Column(scale=1):
            gr.Markdown("**Employability & MBA**")
            input_components_map['etest_p'] = gr.Slider(label="Employability Test %", minimum=0.0, maximum=100.0, step=0.1, value=default_etest_p)
            input_components_map['specialisation'] = gr.Dropdown(label="MBA Specialization", choices=['Mkt&Fin', 'Mkt&HR'], value='Mkt&Fin')
            input_components_map['mba_p'] = gr.Slider(label="MBA Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_mba_p)

    # --- Order Input Components based on loaded feature_names ---
    ordered_input_components = []
    if feature_names:
        missing_features = []
        for name in feature_names:
            component = input_components_map.get(name)
            if component:
                ordered_input_components.append(component)
            else:
                missing_features.append(name)
                print(f"Warning: UI component for feature '{name}' not defined in input_components_map.")
        if missing_features:
             gr.Warning(f"Missing UI components for features: {', '.join(missing_features)}. Predictions might fail.")
        elif len(ordered_input_components) != len(feature_names):
             gr.Warning("Mismatch between number of UI components and expected features.")
    else:
        # Fallback if feature_names couldn't load - order might be wrong!
        ordered_input_components = list(input_components_map.values())
        gr.Warning("Feature names file not loaded. Input order may be incorrect, predictions might fail.")


    predict_button = gr.Button("🚀 Predict Placement Status")

    # Define Output Components within Tabs
    with gr.Tabs():
        with gr.TabItem("📊 Prediction Results"):
            with gr.Row():
                out_profile = gr.Markdown(label="Input Summary")
                with gr.Column():
                     out_prediction = gr.Markdown(label="Prediction")
                     out_plot = gr.Plot(label="Probability Distribution") # Displays the matplotlib fig

        with gr.TabItem("💡 Feature Importance"):
            gr.Markdown("## Feature Importance Analysis")
            gr.Markdown("Shows which factors most influence the placement prediction (based on the trained model). Higher values indicate greater influence.")
            if plots_exist["feature_importance"]:
                gr.Image(FEATURE_IMPORTANCE_PLOT, label="Feature Importance Plot", show_label=False)
            else:
                gr.Warning(f"Feature importance plot not found at '{FEATURE_IMPORTANCE_PLOT}'. Please ensure it was generated and uploaded.")
            gr.Markdown("""

            *Insights based on typical results for this type of problem:*

            - **Academic Performance:** SSC %, HSC %, and Degree % are often strong predictors.

            - **Employability Test:** Performance in standardized tests (etest_p) is usually critical.

            - **Work Experience:** Can provide a significant advantage.

            - **MBA Performance:** MBA % reinforces the importance of consistent academic achievement.

            """)

        with gr.TabItem("📈 Dataset Overview"):
            gr.Markdown("## Dataset Overview")
            gr.Markdown("A quick look at the data used to train the model.")
            with gr.Row():
                with gr.Column(scale=2): # Give more space to dataframe
                    gr.Markdown("**Data Sample**")
                    if df_original is not None:
                         gr.DataFrame(df_head, label="First 10 Rows", row_count=(10, "fixed"), wrap=True, interactive=False)
                    else:
                         gr.Warning(f"Original dataset '{DATA_FILE}' not found.")
                    gr.Markdown("**Basic Stats**")
                    gr.Markdown(dataset_stats)
                with gr.Column(scale=1):
                     gr.Markdown("**Placement Distribution**")
                     if plots_exist["pie_chart"]:
                         gr.Image(PLACEMENT_PIE_CHART, label="Placement Distribution", show_label=False)
                     else:
                         gr.Warning(f"Placement distribution plot not found at '{PLACEMENT_PIE_CHART}'.")
                     gr.Markdown("**Correlation Analysis**")
                     if plots_exist["heatmap"]:
                         gr.Image(CORRELATION_HEATMAP, label="Correlation Heatmap", show_label=False)
                     else:
                         gr.Warning(f"Correlation heatmap not found at '{CORRELATION_HEATMAP}'.")

    # --- Link Button Click to Function ---
    predict_button.click(
        fn=predict_placement,
        inputs=ordered_input_components, # Use the ordered list
        outputs=[out_profile, out_prediction, out_plot]
    )

    # --- Add Examples ---
    # Ensure example values match the order and type of ordered_input_components
    if feature_names: # Only add examples if we know the correct feature order
        example_list = [
            # M, ssc_p, ssc_b, hsc_p, hsc_b, hsc_s, degree_p, degree_t, workex, etest_p, specialisation, mba_p -> default order if no feature_names
            ['M', 67.0, 'Others', 91.0, 'Others', 'Commerce', 58.0, 'Sci&Tech', 'No', 55.0, 'Mkt&HR', 58.8], # Row 1 (Placed)
            ['M', 56.0, 'Central', 52.0, 'Central', 'Science', 52.0, 'Sci&Tech', 'No', 66.0, 'Mkt&HR', 59.43], # Row 4 (Not Placed)
            ['F', 77.0, 'Central', 87.0, 'Central', 'Commerce', 59.0, 'Comm&Mgmt', 'No', 68.0, 'Mkt&Fin', 68.63], # Row 14 (Placed)
            ['F', 52.0, 'Central', 64.0, 'Central', 'Commerce', 61.0, 'Comm&Mgmt', 'No', 55.0, 'Mkt&Fin', 62.93], # Row 187 (Not Placed)
            ['M', 84.0, 'Others', 90.9, 'Others', 'Science', 64.5, 'Sci&Tech', 'No', 86.04, 'Mkt&Fin', 59.42]  # Row 79 (Placed)
        ]
        # Remap examples based on actual feature_names order if necessary (though the default order matches here)
        # This step is complex if the order differs significantly. Assuming the order defined in UI matches feature_names for simplicity now.
        final_examples = example_list

        gr.Examples(
            examples=final_examples,
            inputs=ordered_input_components,
            outputs=[out_profile, out_prediction, out_plot],
            fn=predict_placement,
            cache_examples=False # Caching might be ok if function is pure
        )

# --- Launch the App ---
# This is the standard way to launch in HF Spaces (app variable must be defined)
# app_ui.launch() # No debug=True for production on Spaces

# If running locally for testing before pushing to HF:
if __name__ == "__main__":
    print("Launching Gradio app locally...")
    app_ui.launch(debug=True) # Use debug=True for local testing
    # app_ui.launch() # Use this for standard local deployment without debug prints