Update app.py

app.py

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+# ============================================
+# AI Apartment Rent Predictor for Dhaka
+# Single Codebase (Colab + Hugging Face Ready)
+# ============================================
+
+# Install required libraries
+
+import pandas as pd
+import numpy as np
+import gradio as gr
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestRegressor
+
+# -----------------------------
+# 1. CREATE SAMPLE DATASET
+# -----------------------------
+
+data = {
+    "area_sqft": [800, 1000, 1200, 1500, 1800, 900, 1100, 1400, 1600, 2000],
+    "bedrooms": [2, 3, 3, 4, 4, 2, 3, 3, 4, 5],
+    "bathrooms": [2, 2, 3, 3, 4, 2, 2, 3, 3, 4],
+    "location": [
+        "Dhanmondi", "Mirpur", "Gulshan", "Banani", "Uttara",
+        "Mohammadpur", "Mirpur", "Gulshan", "Banani", "Uttara"
+    ],
+    "furnished": ["No", "No", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes"],
+    "rent": [25000, 18000, 50000, 55000, 40000, 22000, 20000, 52000, 56000, 45000]
+}
+
+df = pd.DataFrame(data)
+
+# -----------------------------
+# 2. DATA PREPROCESSING
+# -----------------------------
+
+location_encoder = LabelEncoder()
+furnished_encoder = LabelEncoder()
+
+df["location"] = location_encoder.fit_transform(df["location"])
+df["furnished"] = furnished_encoder.fit_transform(df["furnished"])
+
+X = df.drop("rent", axis=1)
+y = df["rent"]
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# -----------------------------
+# 3. TRAIN AI MODEL
+# -----------------------------
+
+model = RandomForestRegressor(n_estimators=200, random_state=42)
+model.fit(X_train, y_train)
+
+# -----------------------------
+# 4. PREDICTION FUNCTION
+# -----------------------------
+
+def predict_rent(area_sqft, bedrooms, bathrooms, location, furnished):
+    location_encoded = location_encoder.transform([location])[0]
+    furnished_encoded = furnished_encoder.transform([furnished])[0]
+
+    input_data = np.array([
+        area_sqft, bedrooms, bathrooms, location_encoded, furnished_encoded
+    ]).reshape(1, -1)
+
+    prediction = model.predict(input_data)[0]
+    return f"Estimated Monthly Rent: ৳ {int(prediction)}"
+
+# -----------------------------
+# 5. GRADIO WEB APP
+# -----------------------------
+
+interface = gr.Interface(
+    fn=predict_rent,
+    inputs=[
+        gr.Number(label="Apartment Size (sqft)", value=1000),
+        gr.Number(label="Bedrooms", value=3),
+        gr.Number(label="Bathrooms", value=2),
+        gr.Dropdown(
+            choices=["Dhanmondi", "Mirpur", "Gulshan", "Banani", "Uttara", "Mohammadpur"],
+            label="Location"
+        ),
+        gr.Dropdown(
+            choices=["Yes", "No"],
+            label="Furnished"
+        ),
+    ],
+    outputs="text",
+    title="🏙️ Dhaka Apartment Rent Predictor (AI)",
+    description="An AI-based system to predict apartment rents in Dhaka for newcomers."
+)
+
+interface.launch()