Update app.py

app.py

@@ -1,7 +1,6 @@
 # ==============================
 # 🚖 Uber Driver Recommendation System
-# Single File - Hugging Face Ready
-# ==============================
+# # ==============================
 
 import numpy as np
 import pandas as pd
@@ -14,14 +13,13 @@ from sklearn.ensemble import RandomForestRegressor
 # ------------------------------
 def generate_data(n=1000):
     np.random.seed(42)
-    data = pd.DataFrame({
+    return pd.DataFrame({
         "pickup_distance": np.random.uniform(0.5, 10, n),
         "trip_distance": np.random.uniform(1, 20, n),
         "fare": np.random.uniform(50, 500, n),
         "surge": np.random.choice([1, 1.5, 2], n),
         "rating": np.random.uniform(3, 5, n)
     })
-    return data
 
 # ------------------------------
 # 2. Feature Engineering
@@ -29,11 +27,13 @@ def generate_data(n=1000):
 def feature_engineering(df):
     df = df.copy()
     df["earning_per_km"] = df["fare"] / (df["trip_distance"] + 1)
-    df["efficiency"] = (df["fare"] * df["surge"]) / (df["pickup_distance"] + df["trip_distance"])
+    df["efficiency"] = (df["fare"] * df["surge"]) / (
+        df["pickup_distance"] + df["trip_distance"]
+    )
     return df
 
 # ------------------------------
-# 3. Model Training
+# 3. Train Model
 # ------------------------------
 def train_model():
     data = generate_data()
@@ -44,79 +44,84 @@ def train_model():
     X = data.drop("reward", axis=1)
     y = data["reward"]
 
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
 
-    model = RandomForestRegressor(n_estimators=50)
+    model = RandomForestRegressor(n_estimators=50, random_state=42)
     model.fit(X_train, y_train)
 
-    return model
+    return model, X.columns.tolist()
 
-model = train_model()
+model, feature_columns = train_model()
 
 # ------------------------------
-# 4. Ride Generator (Dynamic UI)
+# 4. Generate Ride Options (FIXED)
 # ------------------------------
-def generate_rides(base_pickup, base_trip, base_fare, base_surge):
+def generate_rides(pickup, trip, fare, surge):
     rides = []
     for _ in range(5):
         rides.append({
-            "pickup_distance": base_pickup + np.random.uniform(-1, 1),
-            "trip_distance": base_trip + np.random.uniform(-2, 2),
-            "fare": base_fare + np.random.uniform(-50, 50),
-            "surge": max(1, base_surge + np.random.choice([0, 0.5]))
+            "pickup_distance": max(0.5, pickup + np.random.uniform(-1, 1)),
+            "trip_distance": max(1, trip + np.random.uniform(-2, 2)),
+            "fare": max(50, fare + np.random.uniform(-50, 50)),
+            "surge": min(2, max(1, surge + np.random.choice([0, 0.5]))),
+            "rating": np.random.uniform(3, 5)  # ✅ FIX
         })
     return pd.DataFrame(rides)
 
 # ------------------------------
-# 5. Explainability Logic
+# 5. Explanation Logic
 # ------------------------------
 def explain(row):
     reasons = []
+
     if row["fare"] > 300:
-        reasons.append("High Fare 💰")
+        reasons.append("High Fare")
     if row["pickup_distance"] < 3:
-        reasons.append("Close Pickup 📍")
+        reasons.append("Close Pickup")
     if row["surge"] > 1:
-        reasons.append("Surge Pricing ⚡")
+        reasons.append("Surge Benefit")
     if row["trip_distance"] > 10:
-        reasons.append("Long Trip 🚗")
+        reasons.append("Long Trip")
 
     return ", ".join(reasons) if reasons else "Balanced Ride"
 
 # ------------------------------
-# 6. Recommendation Engine
+# 6. Recommendation Engine (FIXED)
 # ------------------------------
 def recommend(pickup, trip, fare, surge):
     rides = generate_rides(pickup, trip, fare, surge)
     rides = feature_engineering(rides)
 
+    # ✅ Ensure feature consistency
+    rides = rides[feature_columns]
+
     scores = model.predict(rides)
     rides["score"] = scores
 
     rides = rides.sort_values(by="score", ascending=False).head(3)
 
-    # Build dashboard output
+    # ✅ Clean UI Output
     output = ""
-    for i, row in rides.iterrows():
-        explanation = explain(row)
-        output += f"""
-🚖 Ride Option
-Score: {round(row['score'],2)}
-Fare: ₹{round(row['fare'],2)}
-Pickup Distance: {round(row['pickup_distance'],2)} km
-Trip Distance: {round(row['trip_distance'],2)} km
-Surge: {row['surge']}
-Why: {explanation}
--------------------------
-"""
+    for idx, row in rides.iterrows():
+        output += (
+            f"🚖 Ride Option\n"
+            f"Score: {round(row['score'], 2)}\n"
+            f"Fare: ₹{round(row['fare'], 2)}\n"
+            f"Pickup: {round(row['pickup_distance'], 2)} km\n"
+            f"Trip: {round(row['trip_distance'], 2)} km\n"
+            f"Surge: {row['surge']}\n"
+            f"Why: {explain(row)}\n"
+            f"-----------------------------\n"
+        )
+
     return output
 
 # ------------------------------
-# 7. Gradio UI
+# 7. Gradio UI (STABLE)
 # ------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("## 🚖 Uber Driver Recommendation System")
-    gr.Markdown("Get best rides based on smart AI scoring")
+    gr.Markdown("AI-based smart ride selection")
 
     with gr.Row():
         pickup = gr.Slider(0.5, 10, value=2, label="Pickup Distance (km)")
@@ -128,12 +133,19 @@ with gr.Blocks() as demo:
 
     btn = gr.Button("Get Recommendation")
 
-    output = gr.Textbox(label="Top Ride Recommendations")
+    output = gr.Textbox(
+        label="Top Ride Recommendations",
+        lines=15
+    )
 
-    btn.click(recommend, inputs=[pickup, trip, fare, surge], outputs=output)
+    btn.click(
+        fn=recommend,
+        inputs=[pickup, trip, fare, surge],
+        outputs=output
+    )
 
 # ------------------------------
-# 8. Launch App
+# 8. Launch
 # ------------------------------
 if __name__ == "__main__":
     demo.launch()