update

app.py

@@ -2,6 +2,17 @@ import os
 import joblib
 from huggingface_hub import hf_hub_download
 
+import streamlit as st
+import pandas as pd
+import os
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Hugging Face Model Repo
+MODEL_REPO = "chagu13/is_click_predictor"
+MODEL_DIR = "models"
+os.makedirs(MODEL_DIR, exist_ok=True)  # Ensure directory exists
+
 # Hugging Face Model Repo
 MODEL_REPO = "chagu13/is_click_predictor"
 MODEL_DIR = "models"
@@ -48,3 +59,65 @@ def load_models():
     except Exception as e:
         print(f"❌ Error loading models: {e}")
         return None, None, None
+
+
+# Load models at startup
+st.title("Is_Click Predictor - ML Model Inference")
+st.info("Upload a CSV file, and the trained models will predict click probability.")
+
+catboost, xgb, rf = load_models()
+
+# Upload File
+uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+if uploaded_file:
+    input_df = pd.read_csv(uploaded_file)
+    st.success("File uploaded successfully!")
+
+    # Make Predictions
+    st.subheader("Predictions in Progress...")
+
+    catboost_preds = catboost.predict(input_df)
+    xgb_preds = xgb.predict(input_df)
+    rf_preds = rf.predict(input_df)
+
+    catboost_probs = catboost.predict_proba(input_df)[:, 1]
+    xgb_probs = xgb.predict_proba(input_df)[:, 1]
+    rf_probs = rf.predict_proba(input_df)[:, 1]
+
+    # Combine results
+    predictions_df = pd.DataFrame({
+        "CatBoost": catboost_preds,
+        "XGBoost": xgb_preds,
+        "RandomForest": rf_preds
+    })
+
+    # Apply "at least one model predicts 1" rule
+    predictions_df["is_click_predicted"] = predictions_df.max(axis=1)
+
+    # Generate probability file
+    probabilities_df = pd.DataFrame({
+        "CatBoost_Prob": catboost_probs,
+        "XGBoost_Prob": xgb_probs,
+        "RandomForest_Prob": rf_probs
+    })
+
+    # Save results
+    binary_predictions_path = "binary_predictions.csv"
+    filtered_predictions_path = "filtered_predictions.csv"
+    probabilities_path = "model_probabilities.csv"
+
+    predictions_df.to_csv(binary_predictions_path, index=False)
+    predictions_df[predictions_df["is_click_predicted"] == 1].to_csv(filtered_predictions_path, index=False)
+    probabilities_df.to_csv(probabilities_path, index=False)
+
+    st.success("Predictions completed! Download results below.")
+
+    # Download Buttons
+    with open(binary_predictions_path, "rb") as f:
+        st.download_button("Download Binary Predictions (0/1)", f, file_name="binary_predictions.csv")
+
+    with open(filtered_predictions_path, "rb") as f:
+        st.download_button("Download Clicked Predictions (Only 1s)", f, file_name="filtered_predictions.csv")
+
+    with open(probabilities_path, "rb") as f:
+        st.download_button("Download Probability Predictions", f, file_name="model_probabilities.csv")