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app.py

@@ -1,3 +1,7 @@
+# ==============================
+# 🚖 Uber Driver Recommendation System
+# # ==============================
+
 import numpy as np
 import pandas as pd
 import gradio as gr
@@ -23,7 +27,9 @@ 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
 
 # ------------------------------
@@ -32,6 +38,7 @@ def feature_engineering(df):
 def train_model():
     data = generate_data()
     data = feature_engineering(data)
+
     data["reward"] = data["efficiency"]
 
     X = data.drop("reward", axis=1)
@@ -41,12 +48,13 @@ def train_model():
 
     model = RandomForestRegressor(n_estimators=50, random_state=42)
     model.fit(X_train, y_train)
+
     return model, X.columns.tolist()
 
 model, feature_columns = train_model()
 
 # ------------------------------
-# 4. Generate Ride Options (For Single Input)
+# 4. Generate Ride Options (FIXED)
 # ------------------------------
 def generate_rides(pickup, trip, fare, surge):
     rides = []
@@ -56,109 +64,88 @@ def generate_rides(pickup, trip, fare, surge):
             "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)
+            "rating": np.random.uniform(3, 5)  # ✅ FIX
         })
     return pd.DataFrame(rides)
 
 # ------------------------------
-# 5. Explain Logic
+# 5. Explanation Logic
 # ------------------------------
 def explain(row):
     reasons = []
-    if row["fare"] > 300: reasons.append("High Fare")
-    if row["pickup_distance"] < 3: reasons.append("Close Pickup")
-    if row["surge"] > 1: reasons.append("Surge Benefit")
-    if row["trip_distance"] > 10: reasons.append("Long Trip")
+
+    if row["fare"] > 300:
+        reasons.append("High Fare")
+    if row["pickup_distance"] < 3:
+        reasons.append("Close Pickup")
+    if row["surge"] > 1:
+        reasons.append("Surge Benefit")
+    if row["trip_distance"] > 10:
+        reasons.append("Long Trip")
+
     return ", ".join(reasons) if reasons else "Balanced Ride"
 
 # ------------------------------
-# 6. Recommendation for Sliders
+# 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)
+
+    # ✅ Clean UI Output
     output = ""
     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"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. Recommendation for Excel Upload
-# ------------------------------
-def recommend_excel(file):
-    if file is None:
-        return "No file uploaded.", None
-    df = pd.read_excel(file)
-    
-    # Check required columns
-    required_cols = ["pickup_distance", "trip_distance", "fare", "surge"]
-    for col in required_cols:
-        if col not in df.columns:
-            return f"Missing column: {col}", None
-
-    df = feature_engineering(df)
-    df = df[feature_columns]
-    df["score"] = model.predict(df)
 
-    # Top 3 per row (if needed, we just sort overall)
-    df = df.sort_values(by="score", ascending=False)
-    
-    # Explanation column
-    df["reason"] = df.apply(explain, axis=1)
-
-    return df.head(10), df  # preview, full df for download
+    return output
 
 # ------------------------------
-# 8. Gradio UI
+# 7. Gradio UI (STABLE)
 # ------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 🚖 Uber Driver Recommendation System with Excel Upload")
-    gr.Markdown("You can either use sliders for single ride recommendation or upload Excel for batch processing.")
+    gr.Markdown("## 🚖 Uber Driver Recommendation System")
+    gr.Markdown("AI-based smart ride selection")
 
-    # ----- Sliders -----
     with gr.Row():
         pickup = gr.Slider(0.5, 10, value=2, label="Pickup Distance (km)")
         trip = gr.Slider(1, 20, value=5, label="Trip Distance (km)")
+
     with gr.Row():
         fare = gr.Slider(50, 500, value=200, label="Fare (₹)")
         surge = gr.Slider(1, 2, value=1, step=0.5, label="Surge")
-    btn_single = gr.Button("Get Recommendation")
-    output_single = gr.Textbox(label="Top Ride Recommendations", lines=15)
-    btn_single.click(fn=recommend, inputs=[pickup, trip, fare, surge], outputs=output_single)
-
-    gr.Markdown("---")
 
-    # ----- Excel Upload -----
-    gr.Markdown("### Upload Excel for Batch Ride Recommendation")
-    file_input = gr.File(label="Upload Excel file with columns: pickup_distance, trip_distance, fare, surge", file_types=[".xlsx"])
-    btn_excel = gr.Button("Generate Recommendations")
-    excel_preview = gr.Dataframe(label="Top Recommendations Preview")
-    download_btn = gr.File(label="Download Full Recommendations Excel")
+    btn = gr.Button("Get Recommendation")
 
-    def process_excel(file):
-        preview, full_df = recommend_excel(file)
-        if full_df is not None:
-            full_df.to_excel("ride_recommendations.xlsx", index=False)
-            return preview, "ride_recommendations.xlsx"
-        return preview, None
+    output = gr.Textbox(
+        label="Top Ride Recommendations",
+        lines=15
+    )
 
-    btn_excel.click(fn=process_excel, inputs=file_input, outputs=[excel_preview, download_btn])
+    btn.click(
+        fn=recommend,
+        inputs=[pickup, trip, fare, surge],
+        outputs=output
+    )
 
 # ------------------------------
-# 9. Launch
+# 8. Launch
 # ------------------------------
 if __name__ == "__main__":
     demo.launch()