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import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download
import os
from modelConnector import ModelConnector
# ===========================
# LOAD MODEL & DATASET
# ===========================
st.title("📊 Is Click Predictor")
# Download and load the trained model from Hugging Face
model_path = hf_hub_download(repo_id="chagu13/is_click", repo_type="space", filename="models/rf_model.pkl")
rf_model = joblib.load(model_path)
st.success("✅ Model Loaded Successfully!")
# ===========================
# LOAD DATA FROM HUGGING FACE
# ===========================
st.sidebar.header("Dataset Selection")
# # Download required dataset files
# X_test_path = hf_hub_download(repo_id="taimax13/is_click_data", filename="X_test_1st(1).csv")
# y_test_path = hf_hub_download(repo_id="taimax13/is_click_data", filename="y_test_1st.csv")
# train_data_path = hf_hub_download(repo_id="taimax13/is_click_data", filename="train_dataset_full - train_dataset_full (1).csv")
base_dir = os.path.dirname(os.path.abspath(__file__)) # Get the directory of app.py
X_test_path = os.path.join(base_dir, "data", "X_test_1st.csv")
y_test_path = os.path.join(base_dir, "data", "y_test_1st (1).csv")
train_data_path = os.path.join(base_dir, "data", "train_dataset_full - train_dataset_full.csv")
# Load datasets
X_test = pd.read_csv(X_test_path)
y_test = pd.read_csv(y_test_path, header=None) # Ensure labels match test dataset index
train_data = pd.read_csv(train_data_path)
st.info(f"✅ Loaded datasets: **Train: {len(train_data)} rows**, **Test: {len(X_test)} rows**")
# Initialize Model Connector
model_connector = ModelConnector()
st.title("📊 Is Click Predictor - Train, Retrain, and Predict")
# ===========================
# CHECK MODEL STATUS
# ===========================
if model_connector.model:
st.success("✅ Model Loaded Successfully!")
else:
st.warning("⚠ No model found. Please train one first.")
# ===========================
# TRAIN MODEL IF NOT FOUND
# ===========================
if st.button("🚀 Train Model"):
st.info("🔄 Training model...")
message = model_connector.train_model()
st.success(message)
# ===========================
# RETRAIN MODEL
# ===========================
if st.button("🔄 Retrain Model"):
st.info("🔄 Retraining model with latest data...")
message = model_connector.retrain_model()
st.success(message)
# ===========================
# SELECT A DATA SAMPLE
# ===========================
st.sidebar.header("Select a Test Sample for Prediction")
# Merge X_test with y_test for selection (without labels affecting prediction)
X_test["actual_click"] = y_test.values
# Allow user to pick a row
selected_index = st.sidebar.selectbox("Choose a test sample index", X_test.index)
selected_row = X_test.loc[selected_index].drop("actual_click") # Exclude actual label
# Display selected row
st.write("### Selected Data Sample:")
st.dataframe(selected_row.to_frame().T) # Display as a table
# ===========================
# MAKE PREDICTION & EXPORT CSV
# ===========================
if st.button("Predict Click"):
# Convert selected row to DataFrame for model input
input_data = selected_row.to_frame().T
# Make prediction
prediction = rf_model.predict(input_data)[0]
# Add prediction to DataFrame
input_data["is_click_predicted"] = prediction
# Save prediction as CSV
csv_filename = "prediction_result.csv"
input_data.to_csv(csv_filename, index=False)
# Display Prediction Result
st.subheader("Prediction Result")
if prediction == 1:
st.success("🟢 The model predicts: **User WILL CLICK on the ad!**")
else:
st.warning("🔴 The model predicts: **User WILL NOT CLICK on the ad.**")
# Provide download button for prediction result
st.download_button(
label="📥 Download Prediction Result",
data=input_data.to_csv(index=False).encode("utf-8"),
file_name="prediction_result.csv",
mime="text/csv",
)
st.markdown("---")
st.info("Select a test row from the **left panel**, click **'Predict Click'**, and download the prediction result as a CSV.")
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