'Addnewfiles'

app.py

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+from flask import Flask, request, render_template, redirect, url_for
+import pickle
+import numpy as np
+import os
+
+app = Flask(__name__)
+
+# Load the model and feature names
+def load_model_and_features():
+    try:
+        model_path = os.path.join(os.getcwd(), "models", "lr_regg.pkl")
+        feature_path = os.path.join(os.getcwd(), "models", "feature_names.pkl")
+
+        with open(model_path, "rb") as file:
+            model = pickle.load(file)
+
+        with open(feature_path, "rb") as file:
+            feature_names = pickle.load(file)
+
+        return model, feature_names
+    except FileNotFoundError as e:
+        raise FileNotFoundError(f"Required file not found: {str(e)}")
+
+def predict_price(location, sqft, bath, bhk, model, feature_names):
+    x = np.zeros(len(feature_names))
+
+    if 'total_sqft' in feature_names:
+        x[feature_names.index('total_sqft')] = sqft
+    if 'bath' in feature_names:
+        x[feature_names.index('bath')] = bath
+    if 'bhk' in feature_names:
+        x[feature_names.index('bhk')] = bhk
+    if location in feature_names:
+        loc_index = feature_names.index(location)
+        x[loc_index] = 1
+    else:
+        raise ValueError(f"Location '{location}' is not recognized.")
+
+    return model.predict([x])[0]
+
+# Load model and features when app starts
+model, feature_names = load_model_and_features()
+
+# Get list of locations (excluding non-location features)
+locations = [feat for feat in feature_names if feat not in ['total_sqft', 'bath', 'bhk']]
+locations.sort()
+
+@app.route('/', methods=['GET', 'POST'])
+def home():
+    if request.method == 'POST':
+        try:
+            location = request.form.get('location')
+            sqft = float(request.form.get('sqft'))
+            bath = int(request.form.get('bath'))
+            bhk = int(request.form.get('bhk'))
+
+            price = predict_price(location, sqft, bath, bhk, model, feature_names)
+            prediction = round(price / 10, 2)  # Convert to lakhs and round to 2 decimal places
+
+            # Redirect to results page with the prediction details
+            return redirect(url_for('results',
+                                    predicted_price=prediction,
+                                    location=location,
+                                    sqft=sqft,
+                                    bath=bath,
+                                    bhk=bhk))
+        except Exception as e:
+            error = f"Error: {str(e)}"
+            return render_template('index.html', locations=locations, error=error)
+
+    return render_template('index.html', locations=locations)
+
+@app.route('/results')
+def results():
+    # Fetch query parameters from the URL
+    predicted_price = request.args.get('predicted_price')
+    location = request.args.get('location')
+    sqft = request.args.get('sqft')
+    bath = request.args.get('bath')
+    bhk = request.args.get('bhk')
+
+    return render_template('results.html',
+                           predicted_price=predicted_price,
+                           location=location,
+                           sqft=sqft,
+                           bath=bath,
+                           bhk=bhk)
+
+if __name__ == '__main__':
+    debug_mode = os.getenv('FLASK_DEBUG', 'False').lower() in ['true', '1', 'yes']
+    app.run(debug=debug_mode)

requirements.txt

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+# Core packages
+pandas==2.1.1
+numpy==1.23.5
+scikit-learn==1.3.0
+flask==3.0.0
+# Model and Data Processing
+scipy==1.11.3
+joblib==1.3.2
+# Visualization
+plotly==5.15.0
+seaborn==0.12.2
+matplotlib==3.8.0
+# Dev and Utilities
+ipython==8.12.0
+jupyterlab==4.0.6
+pytest==7.4.2
+black==23.9.1
+flake8==6.1.0
+# Serialization
+pickle-mixin==1.0.2
+# Web App Utilities
+gunicorn==21.2.0
+# Environment Management
+python-dotenv==1.0.0