UppuluriKalyani · UppuluriKalyani · Nov 6, 2024 · Nov 6, 2024
diff --git a/tools/app.py b/tools/app.py
@@ -0,0 +1,46 @@
+from flask import Flask, render_template, request, redirect, url_for
+import os
+import pandas as pd
+from visualizations import plots
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+
+# Ensure the upload folder exists
+if not os.path.exists(app.config['UPLOAD_FOLDER']):
+    os.makedirs(app.config['UPLOAD_FOLDER'])
+
+# Route for the home page
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# Route to handle dataset uploads and generate visualizations
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    # Check if a file is included in the request
+    if 'file' not in request.files:
+        return redirect(url_for('index'))
+
+    file = request.files['file']
+    if file.filename == '':
+        return redirect(url_for('index'))
+
+    # Save the uploaded file
+    if file:
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
+        file.save(filepath)
+
+        # Read dataset and generate visualizations
+        try:
+            dataset = pd.read_csv(filepath)
+        except Exception as e:
+            return f"Error reading file: {e}"
+
+        plots_data = plots.generate_plots(dataset)
+
+        # Pass generated plots to the template
+        return render_template('index.html', plots=plots_data)
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/tools/requirements.txt b/tools/requirements.txt
@@ -0,0 +1,5 @@
+Flask==2.0.1
+matplotlib==3.4.3
+plotly==5.3.1
+pandas==1.3.3
+seaborn==0.11.2
diff --git a/tools/static/style.css b/tools/static/style.css
@@ -0,0 +1,17 @@
+body {
+    font-family: Arial, sans-serif;
+    margin: 20px;
+}
+
+h1, h2, h3 {
+    color: #333;
+}
+
+#plots-container {
+    display: flex;
+    flex-direction: column;
+}
+
+#plots-container div {
+    margin-top: 20px;
+}
diff --git a/tools/templates/index.html b/tools/templates/index.html
@@ -0,0 +1,33 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>ML Nexus - Data Visualization</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+</head>
+<body>
+    <h1>Upload Your Dataset</h1>
+    <form action="/upload" method="post" enctype="multipart/form-data">
+        <input type="file" name="file" accept=".csv">
+        <button type="submit">Upload</button>
+    </form>
+
+    {% if plots %}
+        <h2>Visualizations</h2>
+        <div id="plots-container">
+            {% if plots.scatter_plot %}
+                <div>
+                    <h3>Scatter Plot</h3>
+                    {{ plots.scatter_plot|safe }}
+                </div>
+            {% endif %}
+            {% if plots.correlation_heatmap %}
+                <div>
+                    <h3>Correlation Heatmap</h3>
+                    <img src="{{ plots.correlation_heatmap }}" alt="Correlation Heatmap">
+                </div>
+            {% endif %}
+        </div>
+    {% endif %}
+</body>
+</html>
diff --git a/tools/visualizations/plots.py b/tools/visualizations/plots.py
@@ -0,0 +1,33 @@
+import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+import seaborn as sns
+import io
+import base64
+
+def generate_plots(dataset):
+    plots = {}
+
+    # Scatter Plot for first two columns if numeric
+    if dataset.select_dtypes(include='number').shape[1] >= 2:
+        numeric_columns = dataset.select_dtypes(include='number').columns[:2]
+        fig = px.scatter(dataset, x=numeric_columns[0], y=numeric_columns[1])
+        plots['scatter_plot'] = fig.to_html(full_html=False)
+
+    # Correlation heatmap for all numeric columns
+    correlation_matrix = dataset.corr()
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', cbar=True)
+    heatmap = save_matplotlib_to_base64()
+    plots['correlation_heatmap'] = heatmap
+
+    return plots
+
+# Helper function to save Matplotlib figures as base64 strings
+def save_matplotlib_to_base64():
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    plt.close()
+    buf.seek(0)
+    img_base64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+    return f'data:image/png;base64,{img_base64}'