pdate

docs/app.py

@@ -0,0 +1,41 @@
+from flask import Flask, render_template, request, redirect, jsonify
+import pandas as pd
+import joblib  # Assuming you're using a pre-trained model
+import os
+
+app = Flask(__name__)
+
+# Load your pre-trained model
+model = joblib.load('modelo_seleccionado.pkl')  # Use your trained model file
+
+# Home route to display the HTML form
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# Route to handle the CSV upload and prediction
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        return redirect('/')
+
+    file = request.files['file']
+
+    if file.filename == '':
+        return redirect('/')
+
+    if file:
+        # Assuming the file is a CSV, load it into a DataFrame
+        data = pd.read_csv(file)
+
+        # Make predictions using your model
+        predictions = model.predict(data)
+
+        # Convert predictions to a format you can return (e.g., a list or JSON)
+        results = {'predictions': predictions.tolist()}
+
+        # Return the predictions as a JSON response
+        return jsonify(results)
+
+if __name__ == "__main__":
+    app.run(debug=True)

docs/index.html

@@ -168,7 +168,7 @@ <h2>Data Analysis Plus 📊</h2>
             <a href="descriptive statistics report.html" class="action-button">View Descriptive Statistical Analysis</a>
         </section>
 
-        <section id="usage-guide">
+        <section id="usage-guide WEB">
             <h2>How to Use the Model</h2>
             <p>To utilize our model, follow these simple steps:</p>
             <h3>1. Open the Jupyter Notebook</h3>
@@ -178,8 +178,10 @@ <h3>1. Open the Jupyter Notebook</h3>
                 <li>Click on the file to open it.</li>
             </ol>
             <h3>2. Upload Your CSV File</h3>
-            <p>Use the button below to upload your CSV file for analysis:</p>
-            <a href="upload.html" class="action-button">Upload CSV</a>
+            <form method="POST" enctype="multipart/form-data" action="/upload">
+                <input type="file" name="file" accept=".csv" required />
+                <button type="submit">Upload and Predict</button>
+            </form>
             <h3>3. Analyze the Results</h3>
             <p>Run the notebook cells and discover insights from your data!</p>
         </section>