Adds application

app.py

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+import traceback
+
+import datasets
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+
+
+def process_data(vendor, model):
+    data = datasets.load_dataset('anakib1/mango-ria', '13.08.2024')['train']
+    # Handle cases where vendor or model is None or empty string
+    if vendor:
+        vendor = vendor.strip().lower()
+    else:
+        vendor = ''
+    if model:
+        model = model.strip().lower()
+    else:
+        model = ''
+
+    rows = data.filter(lambda x: vendor in x['Title'].lower() and model in x['Title'].lower())
+
+    dots = []
+    for row in rows:
+        # row[2] is the 'Title' field
+        try:
+            price = float(row['Price'])
+            mileage = float(row['Mileage'].split()[0])
+            dots.append((price, mileage))
+        except:
+            print(f"Could not parse row {row}. Ex = {traceback.format_exc()}")
+
+    if not dots:
+        return "No data found for the specified vendor and model.", None, None
+
+    price, mileage = list(zip(*dots))
+
+    # First plot: Histogram of prices
+    fig1, ax1 = plt.subplots()
+    ax1.hist(price)
+    ax1.set_title('Histogram of Prices')
+    ax1.set_xlabel('Price')
+    ax1.set_ylabel('Frequency')
+
+    # Second plot: Scatter plot with regression line
+    fig2, ax2 = plt.subplots()
+    model_lr = LinearRegression()
+    model_lr.fit(np.array(mileage).reshape(-1, 1), price)
+    y_hat = model_lr.predict(np.array(mileage).reshape(-1, 1))
+    ax2.scatter(mileage, price)
+    ax2.plot(mileage, y_hat, color='r',
+             label='y = {:.2f} * x + {:.2f}. R2 = {:.2f}'.format(model_lr.coef_[0], model_lr.intercept_,
+                                                                 r2_score(y_true=price, y_pred=y_hat)))
+    ax2.legend()
+    ax2.set_xlabel('Mileage')
+    ax2.set_ylabel('Price')
+    ax2.set_title('Price vs Mileage with Regression Line')
+
+    # Return the figures
+    return None, fig1, fig2
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Car Data Analysis")
+    with gr.Row():
+        vendor_input = gr.Textbox(lines=1, label="Vendor", placeholder="Enter vendor, e.g., infiniti")
+        model_input = gr.Textbox(lines=1, label="Model", placeholder="Enter model, e.g., q50")
+    submit_btn = gr.Button("Submit")
+    message_output = gr.Textbox(label="Message", interactive=False)
+    plot_output1 = gr.Plot(label="Histogram of Prices")
+    plot_output2 = gr.Plot(label="Price vs Mileage with Regression Line")
+
+    submit_btn.click(process_data, inputs=[vendor_input, model_input],
+                     outputs=[message_output, plot_output1, plot_output2])
+
+demo.launch()

requirements.txt

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+gradio
+scikit-learn
+matplotlib
+numpy
+datasets