Update app.py

app.py

@@ -1,162 +1,145 @@
-import faicons as fa
-import plotly.express as px
-
-# Load data and compute static values
-from shared import app_dir, tips
-from shinywidgets import render_plotly
-
-from shiny import reactive, render
-from shiny.express import input, ui
-
-bill_rng = (min(tips.total_bill), max(tips.total_bill))
-
-# Add page title and sidebar
-ui.page_opts(title="Restaurant tipping", fillable=True)
-
-with ui.sidebar(open="desktop"):
-    ui.input_slider(
-        "total_bill",
-        "Bill amount",
-        min=bill_rng[0],
-        max=bill_rng[1],
-        value=bill_rng,
-        pre="$",
-    )
-    ui.input_checkbox_group(
-        "time",
-        "Food service",
-        ["Lunch", "Dinner"],
-        selected=["Lunch", "Dinner"],
-        inline=True,
-    )
-    ui.input_action_button("reset", "Reset filter")
-
-# Add main content
-ICONS = {
-    "user": fa.icon_svg("user", "regular"),
-    "wallet": fa.icon_svg("wallet"),
-    "currency-dollar": fa.icon_svg("dollar-sign"),
-    "ellipsis": fa.icon_svg("ellipsis"),
+from flask import Flask, request, jsonify, render_template
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+# Custom LabelEncoder Transformer
+class LabelEncoderTransformer(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        self.encoders = {}
+
+    def fit(self, X, y=None):
+        for col in X.columns:
+            le = LabelEncoder()
+            le.fit(X[col])
+            self.encoders[col] = le
+        return self
+
+    def transform(self, X):
+        X_transformed = X.copy()
+        for col in X.columns:
+            X_transformed[col] = self.encoders[col].transform(X[col])
+        return X_transformed
+
+    def inverse_transform(self, X):
+        X_inverse = X.copy()
+        for col in X.columns:
+            X_inverse[col] = self.encoders[col].inverse_transform(X[col])
+        return X_inverse
+
+app = Flask(__name__)
+
+# Load dataset
+df = pd.read_excel("Laptops_dataset.xlsx")
+
+# Extract unique values
+unique_values = {
+    'Ram': sorted(df['Ram'].unique().tolist()),
+    'Memory': sorted(df['Memory'].unique().tolist()),
+    'Size': sorted(df['Size'].unique().tolist()),
+    'GPU_type': sorted(df['GPU_type'].unique().tolist()),
+    'CPU_type': sorted(df['CPU_type'].unique().tolist())
 }
 
-with ui.layout_columns(fill=False):
-    with ui.value_box(showcase=ICONS["user"]):
-        "Total tippers"
-
-        @render.express
-        def total_tippers():
-            tips_data().shape[0]
-
-    with ui.value_box(showcase=ICONS["wallet"]):
-        "Average tip"
-
-        @render.express
-        def average_tip():
-            d = tips_data()
-            if d.shape[0] > 0:
-                perc = d.tip / d.total_bill
-                f"{perc.mean():.1%}"
-
-    with ui.value_box(showcase=ICONS["currency-dollar"]):
-        "Average bill"
-
-        @render.express
-        def average_bill():
-            d = tips_data()
-            if d.shape[0] > 0:
-                bill = d.total_bill.mean()
-                f"${bill:.2f}"
-
-
-with ui.layout_columns(col_widths=[6, 6, 12]):
-    with ui.card(full_screen=True):
-        ui.card_header("Tips data")
-
-        @render.data_frame
-        def table():
-            return render.DataGrid(tips_data())
-
-    with ui.card(full_screen=True):
-        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
-            "Total bill vs tip"
-            with ui.popover(title="Add a color variable", placement="top"):
-                ICONS["ellipsis"]
-                ui.input_radio_buttons(
-                    "scatter_color",
-                    None,
-                    ["none", "sex", "smoker", "day", "time"],
-                    inline=True,
-                )
-
-        @render_plotly
-        def scatterplot():
-            color = input.scatter_color()
-            return px.scatter(
-                tips_data(),
-                x="total_bill",
-                y="tip",
-                color=None if color == "none" else color,
-                trendline="lowess",
-            )
-
-    with ui.card(full_screen=True):
-        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
-            "Tip percentages"
-            with ui.popover(title="Add a color variable"):
-                ICONS["ellipsis"]
-                ui.input_radio_buttons(
-                    "tip_perc_y",
-                    "Split by:",
-                    ["sex", "smoker", "day", "time"],
-                    selected="day",
-                    inline=True,
-                )
-
-        @render_plotly
-        def tip_perc():
-            from ridgeplot import ridgeplot
-
-            dat = tips_data()
-            dat["percent"] = dat.tip / dat.total_bill
-            yvar = input.tip_perc_y()
-            uvals = dat[yvar].unique()
-
-            samples = [[dat.percent[dat[yvar] == val]] for val in uvals]
-
-            plt = ridgeplot(
-                samples=samples,
-                labels=uvals,
-                bandwidth=0.01,
-                colorscale="viridis",
-                colormode="row-index",
-            )
-
-            plt.update_layout(
-                legend=dict(
-                    orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5
-                )
-            )
-
-            return plt
-
-
-ui.include_css(app_dir / "styles.css")
-
-# --------------------------------------------------------
-# Reactive calculations and effects
-# --------------------------------------------------------
-
-
-@reactive.calc
-def tips_data():
-    bill = input.total_bill()
-    idx1 = tips.total_bill.between(bill[0], bill[1])
-    idx2 = tips.time.isin(input.time())
-    return tips[idx1 & idx2]
-
-
-@reactive.effect
-@reactive.event(input.reset)
-def _():
-    ui.update_slider("total_bill", value=bill_rng)
-    ui.update_checkbox_group("time", selected=["Lunch", "Dinner"])
+# Load model and preprocessor
+model = joblib.load("best_model.pkl")
+preprocessor = joblib.load("preprocessor.pkl")
+
+@app.route('/')
+def index():
+    return render_template('new_web.html')
+
+@app.route('/form')
+def form():
+    # Extract unique values from dataset
+    ram_values = sorted(df['Ram'].unique().tolist())
+    memory_values = sorted(df['Memory'].unique().tolist())
+    size_values = sorted(df['Size'].unique().tolist())
+    gpu_values = sorted(df['GPU_type'].unique().tolist())
+    cpu_values = sorted(df['CPU_type'].unique().tolist())
+    
+    return render_template('form_web.html',
+                         ram_values=ram_values,
+                         memory_values=memory_values,
+                         size_values=size_values,
+                         gpu_values=gpu_values,
+                         cpu_values=cpu_values)
+
+@app.route("/submit_form", methods=['POST'])
+def prediction():
+    if request.method == "POST":
+        try:
+            # Get form data
+            input_data = {
+                'Ram': int(request.form['ram']),
+                'Memory': int(request.form['memory']),
+                'Size': float(request.form['size']),
+                'GPU_type': request.form['gpu_type'],
+                'CPU_type': request.form['cpu_type']
+            }
+            
+            # Create DataFrame and process
+            input_df = pd.DataFrame([input_data])
+            processed_features = preprocessor.transform(input_df)
+            prediction = model.predict(processed_features)[0]
+            
+            # Find similar laptops in price range
+            price_range = (prediction - 2000000, prediction + 2000000)
+            similar_laptops = df[
+                (df['Price'] >= price_range[0]) & 
+                (df['Price'] <= price_range[1])
+            ][['Name', 'Link', 'Price']].to_dict('records')
+
+            # Get values for the sidebar form
+            ram_values = sorted(df['Ram'].unique().tolist())
+            memory_values = sorted(df['Memory'].unique().tolist())
+            size_values = sorted(df['Size'].unique().tolist())
+            gpu_values = sorted(df['GPU_type'].unique().tolist())
+            cpu_values = sorted(df['CPU_type'].unique().tolist())
+            
+            return render_template('output_web.html',
+                                ram=input_data['Ram'],
+                                memory=input_data['Memory'],
+                                size=input_data['Size'],
+                                gpu_type=input_data['GPU_type'],
+                                cpu_type=input_data['CPU_type'],
+                                output=f"{prediction:,.0f}",
+                                similar_laptops=similar_laptops,
+                                ram_values=ram_values,
+                                memory_values=memory_values,
+                                size_values=size_values,
+                                gpu_values=gpu_values,
+                                cpu_values=cpu_values)
+        
+        except Exception as e:
+            print(f"Error in prediction: {e}")
+            return render_template('error.html', error=str(e))
+
+@app.route("/api/predict", methods=["POST"])
+def api_predict():
+    try:
+        data = request.get_json()
+        input_data = {
+            'Ram': int(data['ram']),
+            'Memory': int(data['memory']),
+            'Size': float(data['size']),
+            'GPU_type': data['gpu_type'],
+            'CPU_type': data['cpu_type']
+        }
+        
+        # Create DataFrame with proper column order
+        columns = ['Ram', 'Memory', 'Size', 'GPU_type', 'CPU_type']
+        input_df = pd.DataFrame([input_data])[columns]
+        
+        processed_features = preprocessor.transform(input_df)
+        prediction = model.predict(processed_features)[0]
+        
+        return jsonify({"price": float(prediction)})
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 400
+
+if __name__ == "__main__":
+    app.run(debug=True)