Merge pull request #3 from Malinhara/changes

Changes

.env

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+
+DB_HOST='127.0.0.1'
+DB_USER='root'
+DB_PORT=3306
+DB_PASSWORD=''
+DB_NAME='flask'

.gitignore

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+/Lib

test.py

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+import numpy as np
+import pandas as pd
+from flask import Flask, request, render_template, jsonify
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+import mysql.connector
+from dotenv import load_dotenv
+import os
+from imblearn.over_sampling import SMOTE
+
+app = Flask(__name__)
+load_dotenv()
+
+# Get MySQL connection details from environment variables
+db_config = {
+    'host': os.getenv('DB_HOST'),
+    'user': os.getenv('DB_USER'),
+    'port': os.getenv('DB_PORT'),
+    'password': os.getenv('DB_PASSWORD'),
+    'database': os.getenv('DB_NAME')
+}
+
+# Connect to the MySQL database
+try:
+    connection = mysql.connector.connect(**db_config)
+    if connection.is_connected():
+        print("Successfully connected to the database")
+except mysql.connector.Error as err:
+    print(f"Error: {err}")
+
+# Read dataset from MySQL into a Pandas DataFrame
+try:
+    dataset_query = "SELECT * FROM survey_lung_cancer"
+    dataset = pd.read_sql(dataset_query, con=connection)
+except mysql.connector.Error as err:
+    print(f"Error reading dataset from MySQL: {err}")
+    dataset = pd.DataFrame()  # Empty DataFrame if there's an error
+
+# Data preprocessing
+dataset.columns = dataset.columns.str.strip()
+le_gender = LabelEncoder()
+le_lung_cancer = LabelEncoder()
+
+dataset['GENDER'] = le_gender.fit_transform(dataset['GENDER'])
+dataset['LUNG_CANCER'] = le_lung_cancer.fit_transform(dataset['LUNG_CANCER'])
+
+# Print dataset after preprocessing
+print("Dataset after preprocessing:\n", dataset)
+
+# Check class balance
+print("Class distribution in original dataset:\n", dataset['LUNG_CANCER'].value_counts())
+
+X = dataset.iloc[:, :15].values
+y = dataset.iloc[:, -1].values
+
+# Balance the dataset
+sm = SMOTE(random_state=42)
+X_res, y_res = sm.fit_resample(X, y)
+
+# Print class distribution after SMOTE
+print("Class distribution after SMOTE:\n", pd.Series(y_res).value_counts())
+
+X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.25, random_state=0)
+
+# Standardize the features
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Initialize and train the SVC model
+classifier = SVC(kernel='rbf', random_state=0)
+classifier.fit(X_train, y_train)
+
+# Evaluate model performance
+train_score = classifier.score(X_train, y_train)
+test_score = classifier.score(X_test, y_test)
+print(f"Training accuracy after balancing: {train_score}")
+print(f"Test accuracy after balancing: {test_score}")
+
+# Inspect the predictions on the test set
+y_pred = classifier.predict(X_test)
+print("Test set predictions:\n", y_pred)
+print("Actual test set labels:\n", y_test)
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Extract form data and print each field
+    gender = request.form['gender']
+    age = int(request.form['age'])
+    smoking = int(request.form['smoking'])
+    yellow_fingers = int(request.form['yellow_fingers'])
+    anxiety = int(request.form['anxiety'])
+    peer_pressure = int(request.form['peer_pressure'])
+    chronic_disease = int(request.form['chronic_disease'])
+    fatigue = int(request.form['fatigue'])
+    allergy = int(request.form['allergy'])
+    wheezing = int(request.form['wheezing'])
+    alcohol_consuming = int(request.form['alcohol_consuming'])
+    coughing = int(request.form['coughing'])
+    shortness_of_breath = int(request.form['shortness_of_breath'])
+    swallowing_difficulty = int(request.form['swallowing_difficulty'])
+    chest_pain = int(request.form['chest_pain'])
+
+    print(f"Gender: {gender}")
+    print(f"Age: {age}")
+    print(f"Smoking: {smoking}")
+    print(f"Yellow Fingers: {yellow_fingers}")
+    print(f"Anxiety: {anxiety}")
+    print(f"Peer Pressure: {peer_pressure}")
+    print(f"Chronic Disease: {chronic_disease}")
+    print(f"Fatigue: {fatigue}")
+    print(f"Allergy: {allergy}")
+    print(f"Wheezing: {wheezing}")
+    print(f"Alcohol Consuming: {alcohol_consuming}")
+    print(f"Coughing: {coughing}")
+    print(f"Shortness of Breath: {shortness_of_breath}")
+    print(f"Swallowing Difficulty: {swallowing_difficulty}")
+    print(f"Chest Pain: {chest_pain}")
+
+    # Map gender to numerical values if needed
+    gender_numeric = 1 if gender == 'M' else 0  # Assuming Male (M) is 1, Female (F) is 0
+
+    # Create feature array
+    features = [gender_numeric, age, smoking, yellow_fingers, anxiety, peer_pressure,
+                chronic_disease, fatigue, allergy, wheezing, alcohol_consuming,
+                coughing, shortness_of_breath, swallowing_difficulty, chest_pain]
+
+    # Reshape and predict
+    final_features = np.array(features).reshape(1, -1)
+    prediction = classifier.predict(sc.transform(final_features))
+
+    print(f"Features: {final_features}")
+    print(f"Prediction: {prediction}")
+
+    output = prediction[0]
+    prediction_text = 'YES' if output == 1 else 'NO'
+
+    return render_template('index.html', prediction_text='Lung Cancer Prediction: {}'.format(prediction_text))
+
+@app.route('/results', methods=['POST'])
+def results():
+    # Extract JSON data
+    data = request.get_json(force=True)
+
+    # Assuming the JSON keys are mapped to the correct order of features
+    features = [int(data['gender']), int(data['age']), int(data['smoking']), int(data['yellow_fingers']),
+                int(data['anxiety']), int(data['peer_pressure']), int(data['chronic_disease']),
+                int(data['fatigue']), int(data['allergy']), int(data['wheezing']), int(data['alcohol_consuming']),
+                int(data['coughing']), int(data['shortness_of_breath']), int(data['swallowing_difficulty']),
+                int(data['chest_pain'])]
+
+    # Reshape and predict
+    final_features = np.array(features).reshape(1, -1)
+    prediction = classifier.predict(sc.transform(final_features))
+
+    output = prediction[0]
+
+    return jsonify(output)
+
+if __name__ == "__main__":
+    app.run(debug=True)

venv/Lib/site-packages/dotenv/__init__.py

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+from typing import Any, Optional
+
+from .main import (dotenv_values, find_dotenv, get_key, load_dotenv, set_key,
+                   unset_key)
+
+
+def load_ipython_extension(ipython: Any) -> None:
+    from .ipython import load_ipython_extension
+    load_ipython_extension(ipython)
+
+
+def get_cli_string(
+    path: Optional[str] = None,
+    action: Optional[str] = None,
+    key: Optional[str] = None,
+    value: Optional[str] = None,
+    quote: Optional[str] = None,
+):
+    """Returns a string suitable for running as a shell script.
+
+    Useful for converting a arguments passed to a fabric task
+    to be passed to a `local` or `run` command.
+    """
+    command = ['dotenv']
+    if quote:
+        command.append(f'-q {quote}')
+    if path:
+        command.append(f'-f {path}')
+    if action:
+        command.append(action)
+        if key:
+            command.append(key)
+            if value:
+                if ' ' in value:
+                    command.append(f'"{value}"')
+                else:
+                    command.append(value)
+
+    return ' '.join(command).strip()
+
+
+__all__ = ['get_cli_string',
+           'load_dotenv',
+           'dotenv_values',
+           'get_key',
+           'set_key',
+           'unset_key',
+           'find_dotenv',
+           'load_ipython_extension']

venv/Lib/site-packages/dotenv/__main__.py

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+"""Entry point for cli, enables execution with `python -m dotenv`"""
+
+from .cli import cli
+
+if __name__ == "__main__":
+    cli()