feature(backend) integrating analysis script with backend api correction

api-analysis.py

@@ -0,0 +1,17 @@
+from flask import Flask, request, jsonify
+from app import performm_analyses
+
+app = Flask(__name__)
+
+# #Developing an api end point that will trigger the analysis script
+@app.route('/api/analyze', methods=['POST'])
+def analyze_data():
+    # Get the data from the request
+    data = request.get_data(as_text=True)
+    # Perform analyses
+    results = perform_analyses(data)
+    # Return the analysis results as JSON
+    return jsonify(results)
+
+if __name__ == '__main__':
+    app.run(debug=True)

templates/test.html

@@ -1,11 +0,0 @@
-{% extends 'base.html' %}
-
-{% set active_page = "results" %}
-
-{% block content %}
-<h1>Get your analysis here!</h1>
-{% endblock %}
-
-{% block title %}
-    Results
-{% endblock %}

test.ipynb

@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flask import Flask, request, jsonify\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from io import StringIO\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "app = Flask(__name__)\n",
+    "\n",
+    "def perform_analyses(data):\n",
+    "    try:\n",
+    "        is_json = False\n",
+    "        is_csv = False\n",
+    "        # Try to parse as JSON\n",
+    "        try:\n",
+    "            data = json.loads(data)\n",
+    "            is_json = True\n",
+    "        except json.JSONDecodeError:\n",
+    "            pass\n",
+    "        # If JSON parsing failed, try parsing as CSV\n",
+    "        if not is_json:\n",
+    "            try:\n",
+    "                # If the data has headers\n",
+    "                data = list(csv.DictReader(StringIO(data)))\n",
+    "                is_csv = True\n",
+    "            except csv.Error:\n",
+    "                pass\n",
+    "        if not is_json and not is_csv:\n",
+    "            raise ValueError(\"The data format is not supported.\")\n",
+    "        if is_json and not isinstance(data, list):\n",
+    "            raise ValueError(\"The data should be formatted as a list of objects.\")\n",
+    "        df = pd.DataFrame(data)\n",
+    "        # Perform analyses\n",
+    "        total_websites = df.shape[0]\n",
+    "        average_char_count = df['char_count'].mean()\n",
+    "        average_image_count = df['image_count'].mean()\n",
+    "        # Generate histograms\n",
+    "        char_count_hist = df['char_count'].hist(bins=50).get_figure()\n",
+    "        image_count_hist = df['image_count'].hist(bins=50).get_figure()\n",
+    "        # Save histograms as images\n",
+    "        char_count_hist.savefig('char_count_hist.png')\n",
+    "        image_count_hist.savefig('image_count_hist.png')\n",
+    "        # Return analysis results\n",
+    "        return {\n",
+    "            'total_websites': total_websites,\n",
+    "            'average_char_count': average_char_count,\n",
+    "            'average_image_count': average_image_count\n",
+    "        }\n",
+    "    except ValueError as e:\n",
+    "        return str(e)\n",
+    "\n",
+    "@app.route('/api/analyze', methods=['POST'])\n",
+    "def analyze_data():\n",
+    "    # Get the data from the request\n",
+    "    data = request.get_data(as_text=True)\n",
+    "    # Perform analyses\n",
+    "    results = perform_analyses(data)\n",
+    "    # Return the analysis results as JSON\n",
+    "    return jsonify(results)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}