Upload app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier, IsolationForest
+from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve
+
+# -------------------------
+# Load Dataset (auto-download from GitHub mirror if not present)
+# -------------------------
+import os, requests, zipfile
+
+DATA_URL = "https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv"
+DATA_PATH = "creditcard.csv"
+
+if not os.path.exists(DATA_PATH):
+    print("Downloading dataset...")
+    r = requests.get(DATA_URL)
+    with open(DATA_PATH, "wb") as f:
+        f.write(r.content)
+
+df = pd.read_csv(DATA_PATH)
+
+# -------------------------
+# Preprocess
+# -------------------------
+X = df.drop("Class", axis=1)
+y = df["Class"]
+
+scaler = StandardScaler()
+X["Amount"] = scaler.fit_transform(X["Amount"].values.reshape(-1, 1))
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42, stratify=y
+)
+
+# -------------------------
+# Train Models
+# -------------------------
+log_reg = LogisticRegression(max_iter=5000, class_weight="balanced", random_state=42)
+log_reg.fit(X_train, y_train)
+
+rf = RandomForestClassifier(n_estimators=200, class_weight="balanced", random_state=42)
+rf.fit(X_train, y_train)
+
+iso_forest = IsolationForest(
+    n_estimators=200, contamination=0.0017, random_state=42
+)
+iso_forest.fit(X_train)
+
+# -------------------------
+# Evaluation
+# -------------------------
+def evaluate_models():
+    results = {}
+
+    # Logistic Regression
+    y_pred_lr = log_reg.predict(X_test)
+    y_prob_lr = log_reg.predict_proba(X_test)[:, 1]
+    results["Logistic Regression"] = classification_report(y_test, y_pred_lr, digits=4)
+
+    # Random Forest
+    y_pred_rf = rf.predict(X_test)
+    y_prob_rf = rf.predict_proba(X_test)[:, 1]
+    results["Random Forest"] = classification_report(y_test, y_pred_rf, digits=4)
+
+    # Isolation Forest
+    y_pred_if = iso_forest.predict(X_test)
+    y_pred_if = np.where(y_pred_if == -1, 1, 0)
+    results["Isolation Forest"] = classification_report(y_test, y_pred_if, digits=4)
+
+    return results
+
+
+# -------------------------
+# Fraud Prediction Function
+# -------------------------
+def predict_transaction(amount, time, v_features):
+    # Build feature vector
+    data = np.array([time] + v_features + [amount]).reshape(1, -1)
+    data[:, -1] = scaler.transform(data[:, -1].reshape(-1, 1))  # scale amount
+
+    pred_lr = log_reg.predict(data)[0]
+    prob_lr = log_reg.predict_proba(data)[0][1]
+
+    pred_rf = rf.predict(data)[0]
+    prob_rf = rf.predict_proba(data)[0][1]
+
+    pred_if = iso_forest.predict(data)[0]
+    pred_if = 1 if pred_if == -1 else 0
+
+    return {
+        "Logistic Regression": f"Fraud={pred_lr} (Prob={prob_lr:.3f})",
+        "Random Forest": f"Fraud={pred_rf} (Prob={prob_rf:.3f})",
+        "Isolation Forest": f"Fraud={pred_if}",
+    }
+
+
+# -------------------------
+# Gradio UI
+# -------------------------
+def ui_transaction(amount, time, *v_features):
+    v_features = list(v_features)
+    return predict_transaction(amount, time, v_features)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 💳 Credit Card Fraud Detection\nCompare Logistic Regression, Random Forest & Isolation Forest")
+
+    with gr.Tab("Evaluate Models"):
+        btn = gr.Button("Run Evaluation")
+        out = gr.Textbox(lines=15, label="Results")
+
+        def run_eval():
+            res = evaluate_models()
+            return "\n\n".join([f"{k}:\n{v}" for k, v in res.items()])
+
+        btn.click(run_eval, outputs=out)
+
+    with gr.Tab("Predict a Transaction"):
+        amount = gr.Number(label="Transaction Amount")
+        time = gr.Number(label="Time (seconds since first transaction)")
+        v_inputs = [gr.Number(label=f"V{i}") for i in range(1, 29)]
+
+        btn_pred = gr.Button("Predict Fraud")
+        out_pred = gr.JSON(label="Predictions")
+
+        btn_pred.click(ui_transaction, inputs=[amount, time] + v_inputs, outputs=out_pred)
+
+demo.launch()