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App Files Community
SurenRavi commited on
Commit
1e5c067
·
1 Parent(s): 605cae1

Frontend + Backend

Browse files
Files changed (3) hide show
  1. app.py +121 -0
  2. index.html +18 -0
  3. result.html +15 -0
app.py ADDED
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+ # app.py
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+
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+ import pandas as pd
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+ import numpy as np
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+ import matplotlib.pyplot as plt
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+ import seaborn as sns
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+ import plotly.express as px
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+ from PIL import Image
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+ import json
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+ import csv
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+ from io import BytesIO
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+ import requests
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+ from sklearn.preprocessing import LabelEncoder
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+ from sklearn.ensemble import RandomForestClassifier
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+ from geopy.distance import geodesic
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+ import osmnx as ox
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+ from sklearn.linear_model import LogisticRegression
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+ from sklearn.svm import SVC
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+ from sklearn.tree import DecisionTreeClassifier
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+ from flask import Flask, request, render_template
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+
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+ app = Flask(__name__)
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+
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+ # Read data
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+ pd.set_option('display.max_columns', None)
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+ df = pd.read_json("./Tourist_related.json")
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+
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+ # Convert data to CSV
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+ with open('./data.csv', 'w') as data_file:
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+ csv_writer = csv.writer(data_file)
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+ csv_writer.writerow(df.columns)
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+ csv_writer.writerows(df.values)
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+
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+ # Read CSV data
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+ read_dat = pd.read_csv('./data.csv', on_bad_lines='skip')
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+
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+ # Function for data quality test
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+ def data_quality_test():
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+ check_na = read_dat.isnull().sum()
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+ for features, count in check_na.items():
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+ print(f'{features}: {count}')
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+
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+ # Function for correlation analysis
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+ def correlation_analysis():
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+ plt.figure(figsize=(12, 6))
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+ sns.heatmap(df.corr(), annot=True)
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+ plt.show()
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+
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+ # Geo Plot Visualization Function
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+ def geo_plot_visualization():
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+ df['lat'] = df['location'].apply(lambda x: x.get('lat', None))
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+ df['lng'] = df['location'].apply(lambda x: x.get('lng', None))
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+
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+ color_scale = [(0, 'orange'), (1, 'red')]
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+
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+ fig = px.scatter_mapbox(df,
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+ lat="lat",
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+ lon="lng",
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+ color_continuous_scale=color_scale,
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+ zoom=8,
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+ height=800,
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+ width=800)
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+
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+ fig.update_layout(mapbox_style="open-street-map")
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+ fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
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+
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+ fig.show()
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+
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+ # Function for image category recognition
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+ def predict_image_category(image_url):
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+ # Replace this part with your image classification logic
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+ # For example, you can use a pre-trained deep learning model
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+ # Here, I'm using a placeholder
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+ categories = ['Category A', 'Category B', 'Category C']
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+ prediction = np.random.choice(categories)
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+ return prediction
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+
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+ # Random Forest Classifier
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+ def train_random_forest():
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+ # Assuming 'target' is the target variable
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+ X = df.drop('target', axis=1)
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+ y = df['target']
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+
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+ # Assuming 'categorical_columns' is a list of categorical columns
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+ le = LabelEncoder()
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+ for col in categorical_columns:
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+ X[col] = le.fit_transform(X[col])
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+
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+ # Create and train the random forest classifier
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+ rf_classifier = RandomForestClassifier()
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+ rf_classifier.fit(X, y)
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+
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+ return rf_classifier
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+
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+ # Home route
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+ @app.route('/')
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+ def home():
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+ return render_template('index.html')
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+
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+ # Image upload route
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+ @app.route('/upload', methods=['POST'])
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+ def upload():
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+ if request.method == 'POST':
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+ file = request.files['file']
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+ if file:
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+ # Read the uploaded image
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+ img = Image.open(file.stream)
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+
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+ # Display the image
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+ plt.imshow(img)
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+ plt.axis('off')
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+ plt.show()
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+
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+ # Predict the image category
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+ prediction = predict_image_category(file.stream)
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+
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+ return render_template('result.html', prediction=prediction)
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+
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+ # Start the Flask app
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+ if __name__ == '__main__':
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+ app.run(debug=True)
index.html ADDED
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+ <!-- templates/index.html -->
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+
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+ <!DOCTYPE html>
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+ <html lang="en">
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+ <head>
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+ <meta charset="UTF-8">
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+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+ <title>Image Category Recognition</title>
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+ </head>
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+ <body>
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+ <h1>Image Category Recognition</h1>
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+ <form action="/upload" method="post" enctype="multipart/form-data">
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+ <input type="file" name="file" accept="image/*">
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+ <input type="submit" value="Upload">
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+ </form>
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+ </body>
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+ </html>
result.html ADDED
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+ <!-- templates/result.html -->
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+
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+ <!DOCTYPE html>
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+ <html lang="en">
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+ <head>
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+ <meta charset="UTF-8">
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+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+ <title>Image Category Recognition Result</title>
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+ </head>
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+ <body>
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+ <h1>Image Category Recognition Result</h1>
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+ <p>Prediction: {{ prediction }}</p>
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+ </body>
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+ </html>