Upload app.py

app.py

@@ -0,0 +1,144 @@
+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1oMJ02G_2BU5UAUhlQvk2s3Uvbu2YgDSG
+"""
+
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score, confusion_matrix
+import gradio as gr
+
+# ==========================================
+# (A) Data Loading & Preprocessing
+# ==========================================
+
+# 1. Load the dataset
+# Using the URL directly from the quiz
+url = "https://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/ionosphere.data"
+
+# The dataset has 34 numerical features and 1 class label at the end.
+# It does not have a header row, so we use header=None.
+df = pd.read_csv(url, header=None)
+
+# Separate Features (X) and Target (y)
+X = df.iloc[:, :-1]  # All columns except the last one
+y = df.iloc[:, -1]   # The last column (labels: 'g' or 'b')
+
+# Encode the labels: 'g' (Good) -> 1, 'b' (Bad) -> 0
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+# 2. Split the dataset (80% train, 20% test, random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y_encoded, test_size=0.20, random_state=42
+)
+
+# 3. Standardize the features
+scaler = StandardScaler()
+# Fit on training set only to prevent data leakage, then transform both
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+print("Data loaded and preprocessed successfully.")
+print(f"Training shape: {X_train_scaled.shape}")
+print(f"Testing shape: {X_test_scaled.shape}")
+print("-" * 30)
+
+# ==========================================
+# (B) Build an ANN in scikit-learn
+# ==========================================
+
+# 4. Build the ANN (MLPClassifier) [cite: 15, 16, 17, 19]
+# Architecture: Hidden layers (32, 16), Activation 'relu', Solver 'adam'
+ann_model = MLPClassifier(
+    hidden_layer_sizes=(32, 16),
+    activation='relu',
+    solver='adam',
+    max_iter=500,  # Setting > 300 as requested
+    random_state=42
+)
+
+# 5. Train the model [cite: 20]
+ann_model.fit(X_train_scaled, y_train)
+
+# 6. Evaluate the model [cite: 21, 22, 23]
+y_pred = ann_model.predict(X_test_scaled)
+
+accuracy = accuracy_score(y_test, y_pred)
+conf_matrix = confusion_matrix(y_test, y_pred)
+
+print(f"Model Accuracy on Test Set: {accuracy:.4f}")
+print("Confusion Matrix:")
+print(conf_matrix)
+print("-" * 30)
+
+# ==========================================
+# (C) Build a GUI with Gradio
+# ==========================================
+
+def predict_radar_signal(input_str):
+    """
+    Function to process input from Gradio and return prediction.
+    Expects a comma-separated string of 34 numbers.
+    """
+    try:
+        # Convert string input to numpy array
+        # This handles the user pasting a row of data
+        input_list = [float(x.strip()) for x in input_str.split(',')]
+
+        if len(input_list) != 34:
+            return f"Error: Expected 34 values, but got {len(input_list)}."
+
+        # Reshape for a single sample (1, 34)
+        input_data = np.array(input_list).reshape(1, -1)
+
+        # IMPORTANT: Scale the input using the SAME scaler trained above
+        input_scaled = scaler.transform(input_data)
+
+        # Predict class and probability
+        prediction_idx = ann_model.predict(input_scaled)[0]
+        prediction_prob = ann_model.predict_proba(input_scaled)[0]
+
+        # Decode prediction back to 'Good' or 'Bad'
+        # Since we know 'g' is usually 1 and 'b' is 0, but let's use the encoder classes
+        # le.classes_ usually ['b', 'g']. So 0 -> 'b', 1 -> 'g'
+        predicted_label_code = le.inverse_transform([prediction_idx])[0]
+        label_text = "Good" if predicted_label_code == 'g' else "Bad"
+
+        # Get probability of the predicted class
+        # prob array is [prob_class_0, prob_class_1]
+        confidence = prediction_prob[prediction_idx]
+
+        # Format output as requested [cite: 28, 29]
+        return f"Prediction: {label_text}\nProbability of {label_text}: {confidence:.4f}"
+
+    except Exception as e:
+        return f"Error processing input: {str(e)}"
+
+# Define the Gradio Interface [cite: 25, 30]
+# We use a Textbox for input to handle the large number of features (34) easily.
+iface = gr.Interface(
+    fn=predict_radar_signal,
+    inputs=gr.Textbox(
+        label="Input Features",
+        placeholder="Paste 34 comma-separated values here (e.g., 1,0,0.9,...)",
+        lines=3
+    ),
+    outputs=gr.Textbox(label="Result"),
+    title="Ionosphere Radar Signal Predictor",
+    description="Enter the 34 features of the radar signal to predict if it is Good or Bad."
+)
+
+# Launch the app
+print("Launching Gradio Interface...")
+iface.launch(share=True) # Set share=True to create a public link
+
+
+