Moved Dockerfile to root directory

app.py

@@ -0,0 +1,75 @@
+import os
+import json
+import joblib
+import pandas as pd
+from pathlib import Path
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# Model directory in HF Space (root/models/)
+MODEL_PATH = Path("models") / "best_model.joblib"
+
+if MODEL_PATH.exists():
+    PIPELINE = joblib.load(MODEL_PATH)
+else:
+    PIPELINE = None
+
+
+@app.get("/")
+def root():
+    return jsonify({
+        "status": "ok",
+        "message": "ExtraaLearn Lead Conversion API",
+        "model_loaded": PIPELINE is not None
+    })
+
+
+@app.post("/predict")
+def predict_single():
+    """Predict for a single JSON input"""
+    if PIPELINE is None:
+        return jsonify({"error": "Model not loaded"}), 503
+
+    payload = request.get_json(force=True)
+    X = pd.DataFrame([payload])
+
+    proba = float(PIPELINE.predict_proba(X)[:, 1][0])
+    pred = int(proba >= 0.5)
+
+    return jsonify({
+        "probability": proba,
+        "prediction": pred
+    })
+
+
+@app.post("/predict-batch")
+def predict_batch():
+    """Predict for multiple JSON rows"""
+    if PIPELINE is None:
+        return jsonify({"error": "Model not loaded"}), 503
+
+    payload = request.get_json(force=True)
+
+    if isinstance(payload, dict) and "records" in payload:
+        records = payload["records"]
+    elif isinstance(payload, list):
+        records = payload
+    else:
+        return jsonify({"error": "Invalid payload format"}), 400
+
+    df = pd.DataFrame(records)
+    probas = PIPELINE.predict_proba(df)[:, 1]
+    preds = (probas >= 0.5).astype(int)
+
+    df["conversion_proba"] = probas
+    df["prediction"] = preds
+
+    return df.to_json(orient="records")
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port)