added a new linear regression model

Algolyzer/playground/templates/playground/home.html

@@ -5,9 +5,9 @@
     <h1 class="text-center my-10 font-bold font-poppins text-5xl">Playground</h1>
     <h1 class="font-bold text-xl mb-5">Pre-Trained AIML Models</h1>
 
-    {% comment %} Sentiment Analysis {% endcomment %}
     <div class="flex flex-col gap-5">
 
+        {% comment %} Sentiment Analysis {% endcomment %}
         <div class="rounded-xl border-2 border-solid border-primary p-5 hover:border-secondary">
             <div class="flex md:items-center justify-between md:flex-row flex-col">
                 <h1 class="text-4xl my-2 p-4 text-primary">Sentiment Analysis</h1>
@@ -59,6 +59,57 @@ <h1 class="text-4xl my-2 p-4 text-primary">Sentiment Analysis</h1>
             </div>
         </div>
 
+
+        {% comment %} Linear Regression {% endcomment %}
+        <div class="rounded-xl border-2 border-solid border-primary p-5 hover:border-secondary">
+            <div class="flex md:items-center justify-between md:flex-row flex-col">
+                <h1 class="text-4xl my-2 p-4 text-primary">Linear Regression</h1>
+                <div class="flex gap-4">
+                    <a href="{% url "linear_regression" %}" class="btn btn-accent font-bold font-poppins max-md:flex-1">Try Now</a>
+                    <a href="#" class="btn btn-primary btn-outline max-md:flex-1">Learn More</a>
+                </div>
+            </div>
+            <div class="flex flex-col md:flex-row gap-4">
+                {% comment %} Left Segment {% endcomment %}
+                <div>
+                    <div class="stats shadow max-w-lg">                  
+                        <div class="stat">
+                            <div class="stat-title">Accuracy</div>
+                            <div class="stat-value text-primary">75%</div>
+                            <div class="stat-desc">↗︎ 400 (22%)</div>
+                        </div>
+
+                        <div class="stat">
+                            <div class="stat-title">Ratings</div>
+                            <div class="stat-value">
+                                <form class="rating rating-md rating-half">
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-1 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-2 bg-yellow-400" disabled/>
+                                    <input
+                                    type="radio"
+                                    name="rating-10"
+                                    class="mask mask-star-2 mask-half-1 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-2 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-1 bg-yellow-400" checked="checked" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-2 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-1 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-2 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-1 bg-yellow-400" disabled/>
+                                    <input type="radio" name="rating-10" class="mask mask-star-2 mask-half-2 bg-yellow-400" disabled/>
+                                </form>
+                            </div>
+                            <div class="stat-desc">↘︎ 90 (14%)</div>
+                        </div>
+                    </div>
+                </div>
+
+                {% comment %} Right Segment {% endcomment %}
+                <div>
+                    <p>{% lorem 1 b random %}
+                    </p>
+                </div>
+            </div>
+        </div>
         {% comment %} Doodle Classifier {% endcomment %}
         <div class="rounded-xl border-2 border-solid border-primary p-5 hover:border-secondary">
             <div class="flex md:items-center justify-between md:flex-row flex-col">
@@ -101,7 +152,7 @@ <h1 class="text-4xl my-2 p-4 text-primary">Doodle Classifier</h1>
                         </div>
                     </div>
                 </div>
-
+        
                 {% comment %} Right Segment {% endcomment %}
                 <div>
                     <p>{% lorem 1 b random %}

Algolyzer/playground/templates/playground/linear_regression.html

@@ -0,0 +1,128 @@
+{% extends "base.html" %}
+
+{% block extra_head %}
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+{% endblock %}
+
+{% block content %}
+<div class="max-w-5xl mx-auto my-5 p-2 bg-base-200 shadow-lg ">
+    <div class="p-2 lg:p-5 rounded-xl border-2 border-solid border-primary">
+        <h1 class="text-4xl font-bold text-center my-5">Linear Regression</h1>
+
+
+        <form method="POST" class="space-y-4">
+            {% csrf_token %}
+            <label class="block text-lg font-semibold px-4">Enter values:</label>
+
+            {% for i in "12345" %}
+            <div class="px-4">
+                <label class="label">
+                    <span class="label-text">Value {{ forloop.counter }}</span>
+                    <span class="label-text-alt" id="slider-value-{{ forloop.counter }}">5</span>
+                </label>
+                <input 
+                    type="range" 
+                    name="value_{{ forloop.counter }}" 
+                    min="0" 
+                    max="10" 
+                    value="5" 
+                    class="range range-primary" 
+                    oninput="document.getElementById('slider-value-{{ forloop.counter }}').textContent = this.value"
+                />
+            </div>
+            {% endfor %}
+
+            <button class="btn btn-primary w-full">Submit</button>
+        </form>
+
+        {% if predicted_value %}
+        <div class="px-4 py-4">
+            <div class="alert alert-info">
+                <p><strong>Input Values:</strong> {{ input_values|join:", " }}</p>
+                <p><strong>Prediction:</strong> The next value is {{ predicted_value }}.</p>
+            </div>
+        </div>
+        {% endif %}
+
+        {% if show_chart %}
+            <div class="px-4 py-4">
+                <div class="card bg-base-100 shadow-xl">
+                    <div class="card-body">
+                        <h2 class="card-title">Linear Regression Chart</h2>
+                        <canvas id="regressionChart"></canvas>
+                    </div>
+                </div>
+            </div>
+
+            {% block scripts %}
+            <script>
+                // Prepare data for Chart.js
+                const inputValues = {{ input_values|safe }};
+                const predictedValue = {{ predicted_value|safe }};
+                const slope = {{ slope|safe }};
+                const intercept = {{ intercept|safe }};
+
+                // Data points (indices 1 to 5 for inputs, 6 for prediction)
+                const dataPoints = inputValues.map((val, idx) => ({
+                    x: idx + 1,
+                    y: val
+                })).concat([{ x: 6, y: predictedValue }]);
+
+                // Regression line points (extend slightly for visibility)
+                const linePoints = [
+                    { x: 0.5, y: intercept + 0.5 * slope },
+                    { x: 6.5, y: intercept + 6.5 * slope }
+                ];
+
+                // Initialize Chart.js
+                const ctx = document.getElementById('regressionChart').getContext('2d');
+                new Chart(ctx, {
+                    type: 'scatter',
+                    data: {
+                        datasets: [
+                            {
+                                label: 'Data Points',
+                                data: dataPoints,
+                                backgroundColor: 'rgb(59, 130, 246)', // DaisyUI primary color
+                                borderColor: 'rgb(59, 130, 246)',
+                                showLine: false,
+                                pointRadius: 6
+                            },
+                            {
+                                label: 'Regression Line',
+                                data: linePoints,
+                                type: 'line',
+                                borderColor: 'rgb(239, 68, 68)', // Contrast color (red)
+                                backgroundColor: 'transparent',
+                                fill: false,
+                                pointRadius: 0,
+                                borderWidth: 2
+                            }
+                        ]
+                    },
+                    options: {
+                        responsive: true,
+                        scales: {
+                            x: {
+                                title: { display: true, text: 'Index' },
+                                min: 0,
+                                max: 7
+                            },
+                            y: {
+                                title: { display: true, text: 'Value' },
+                                min: 0,
+                                max: 12
+                            }
+                        },
+                        plugins: {
+                            legend: { display: true }
+                        }
+                    }
+                });
+            </script>
+            {% endblock %}
+        {% endif %}
+
+    </div>
+</div>
+{% endblock %}

Algolyzer/playground/urls.py

@@ -1,9 +1,15 @@
 from django.urls import path
 
-from .views import doodle_classifier, playground_home, sentiment_analysis
+from .views import (
+    doodle_classifier,
+    linear_regression,
+    playground_home,
+    sentiment_analysis,
+)
 
 urlpatterns = [
     path("", playground_home, name="playground_home"),
+    path("linear_regression", linear_regression, name="linear_regression"),
     path("sentiment_analysis/", sentiment_analysis, name="sentiment_analysis"),
     path("doodle_classifier/", doodle_classifier, name="doodle_classifier"),
 ]

Algolyzer/playground/views.py

@@ -2,13 +2,15 @@
 import json
 import os
 
+import numpy as np
 from django.conf import settings
 from django.contrib.auth.decorators import login_required
 from django.core.files.base import ContentFile
 from django.shortcuts import redirect, render
 from django.utils.timezone import now
 from home.decorators import profile_required
 from PIL import Image
+from sklearn.linear_model import LinearRegression
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
 from .models import PlaygroundTask
@@ -159,3 +161,48 @@ def doodle_classifier(request):
 
         context = {"previous_results": previous_results}
         return render(request, "playground/doodle_classifier.html", context=context)
+
+
+@login_required
+def linear_regression(request):
+    if request.method == "POST":
+        # Collect form data (values from 5 sliders)
+        values = [
+            float(request.POST.get("value_1", 0)),
+            float(request.POST.get("value_2", 0)),
+            float(request.POST.get("value_3", 0)),
+            float(request.POST.get("value_4", 0)),
+            float(request.POST.get("value_5", 0)),
+        ]
+
+        # Prepare data for linear regression
+        X = np.array(range(1, 6)).reshape(-1, 1)  # Indices 1 to 5
+        y = np.array(values)  # Slider values
+
+        # Train linear regression model
+        model = LinearRegression()
+        model.fit(X, y)
+
+        # Predict the next value (for index 6)
+        next_index = np.array([[6]])
+        predicted_value = model.predict(next_index)[0]
+
+        # Get regression line parameters for plotting
+        slope = model.coef_[0]
+        intercept = model.intercept_
+
+        # Pass data to template
+        return render(
+            request,
+            "playground/linear_regression.html",
+            {
+                "predicted_value": round(predicted_value, 2),
+                "input_values": values,
+                "slope": slope,
+                "intercept": intercept,
+                "show_chart": True,  # Flag to display chart
+            },
+        )
+
+    else:
+        return render(request, "playground/linear_regression.html")

requirements.txt

@@ -33,6 +33,7 @@ identify==2.6.4
 idna==3.10
 isort==5.13.2
 Jinja2==3.1.5
+joblib==1.4.2
 keras==3.8.0
 kombu==5.4.2
 libclang==18.1.1
@@ -58,7 +59,6 @@ nvidia-curand-cu12==10.3.5.147
 nvidia-cusolver-cu12==11.6.1.9
 nvidia-cusparse-cu12==12.3.1.170
 nvidia-cusparselt-cu12==0.6.2
-nvidia-nccl-cu12==2.21.5
 nvidia-nvjitlink-cu12==12.4.127
 nvidia-nvtx-cu12==12.4.127
 opt_einsum==3.4.0
@@ -83,22 +83,25 @@ regex==2024.11.6
 requests==2.32.3
 rich==13.9.4
 safetensors==0.5.2
+scikit-learn==1.6.1
+scipy==1.15.2
 setuptools==75.8.0
 six==1.17.0
 sqlparse==0.5.3
 sympy==1.13.1
 tensorboard==2.18.0
 tensorboard-data-server==0.7.2
 tensorflow==2.18.0
+tensorflow_intel==2.18.0
 termcolor==2.5.0
 tf_keras==2.18.0
+threadpoolctl==3.6.0
 tokenizers==0.21.0
 torch==2.6.0
 torchaudio==2.6.0
 torchvision==0.21.0
 tqdm==4.67.1
 transformers==4.48.3
-triton==3.2.0
 typing_extensions==4.12.2
 tzdata==2025.1
 urllib3==2.3.0