Merge pull request #19 from MVP-Psicologia-Positiva/initial-sentiment-analysis

lrsmello · web-flow · commit efa0bdab3a41 · 2025-05-23T01:58:20.000-03:00
Sentiment Analysis, Memory Refactor, and RAG-Based Long-Term Memory for Lulu Assistant
diff --git a/happy-kids/api/migrations/0015_chat_memories_user_message_sentiment.py b/happy-kids/api/migrations/0015_chat_memories_user_message_sentiment.py
@@ -0,0 +1,18 @@
+# Generated by Django 5.1.5 on 2025-05-18 17:23
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('api', '0014_alter_chat_facts_feedback_bot_message_and_more'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='chat_memories',
+            name='user_message_sentiment',
+            field=models.CharField(blank=True, max_length=20, null=True),
+        ),
+    ]
diff --git a/happy-kids/api/migrations/0016_chat_memories_embedding.py b/happy-kids/api/migrations/0016_chat_memories_embedding.py
@@ -0,0 +1,19 @@
+# Generated by Django 5.1.5 on 2025-05-23 03:15
+
+import django.contrib.postgres.fields
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('api', '0015_chat_memories_user_message_sentiment'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='chat_memories',
+            name='embedding',
+            field=django.contrib.postgres.fields.ArrayField(base_field=models.FloatField(), blank=True, null=True, size=None),
+        ),
+    ]
diff --git a/happy-kids/api/models.py b/happy-kids/api/models.py
@@ -1,5 +1,6 @@
 from django.db import models
 from django.contrib.auth.models import User
+from django.contrib.postgres.fields import ArrayField
 
 class historicalSessions(models.Model):
     sessionDate = models.DateTimeField("session date", default="")
@@ -20,8 +21,10 @@ class UsersMilestones(models.Model):
 class chat_memories(models.Model):
     user_id = models.CharField(max_length=100)
     user_message = models.TextField()
+    user_message_sentiment = models.CharField(max_length=20, blank=True, null=True) 
     chat_message = models.TextField()
     date_time = models.DateTimeField(auto_now_add=True)
+    embedding = ArrayField(models.FloatField(), blank=True, null=True) 
 
 class dim_question_type(models.Model):
     question_type = models.TextField(unique=True)
diff --git a/happy-kids/frontend/templates/frontend/dashboard.html b/happy-kids/frontend/templates/frontend/dashboard.html
@@ -19,24 +19,33 @@
 
             <div class="dashboard-grid" style="width: 100%; max-width: 100%; margin: 0 auto; padding: 20px;">
                 <div class="dashboard-card">
-                    <h3>Mensagens por Dia</h3>
+                    <h3>Messages per day</h3>
                     <div id="chat-messages-graph" style="height:300px; width: 100%;"></div>
                 </div>
 
                 <div class="dashboard-card">
-                    <h3>Usuários Únicos por Dia</h3>
+                    <h3>Unique users per day</h3>
                     <div id="unique-users-graph" style="height:300px; width: 100%;"></div>
                 </div>
 
                 <div class="dashboard-card">
-                    <h3>Top 5 Usuários</h3>
+                    <h3>Top 5 Users</h3>
                     <div id="top-users-graph" style="height:300px; width: 100%;"></div>
                 </div>
 
                 <div class="dashboard-card">
                     <h3>Feedback Like/Dislike</h3>
                     <div id="feedback-pie" style="height:300px; width: 100%;"></div>
                 </div>
+
+                <div class="dashboard-card">
+                    <h3>Sentiments by Day</h3>
+                    <div id="sentiment-graph" style="height:350px; width: 100%;"></div>
+                </div>
+                <div class="dashboard-card">
+                    <h3>Sentiments Rank</h3>
+                    <div id="sentiment-bar-graph" style="height:300px; width: 100%;"></div>
+                </div>
             </div>
         </div>
     </main>
@@ -46,14 +55,13 @@ <h3>Feedback Like/Dislike</h3>
     const plotConfig = {
         responsive: true,
         displaylogo: false,
-        displayModeBar: true // ou 'hover' se quiser só ao passar o mouse
+        displayModeBar: true
     };
 
-    // Paleta personalizada mais próxima do CSS da aplicação
     const customLayout = {
-        paper_bgcolor: "#faf8ff",   // fundo lilás super claro
-        plot_bgcolor: "#ffffff",    // fundo interno branco
-        font: {color: "#4a2d7c"},   // roxo institucional
+        paper_bgcolor: "#faf8ff",
+        plot_bgcolor: "#ffffff",
+        font: {color: "#4a2d7c"},
         xaxis: {
             gridcolor: '#ddd',
             zerolinecolor: '#ccc',
@@ -70,45 +78,41 @@ <h3>Feedback Like/Dislike</h3>
         title: {font: {color: "#4a2d7c"}}
     };
 
-    // Dados vindos do Django
     const chatDates = {{ chat_dates_json|safe }};
     const minDate = chatDates.length > 0 ? chatDates[0] : null;
 
-    // Mensagens por dia
+    // Messages per day
     Plotly.newPlot('chat-messages-graph', [{
         x: chatDates,
         y: {{ chat_counts_json|safe }},
         type: 'scatter',
         mode: 'lines+markers',
-        marker: {color: '#8c30f5'} // roxo vibrante
+        marker: {color: '#8c30f5'}
     }], Object.assign({}, customLayout, {
-        title: 'Mensagens por Dia',
-        xaxis: Object.assign({}, customLayout.xaxis, {title: 'Data', range: [minDate, null]}),
-        yaxis: Object.assign({}, customLayout.yaxis, {title: 'Mensagens'})
+        xaxis: Object.assign({}, customLayout.xaxis, {range: [minDate, null]}),
+        yaxis: Object.assign({}, customLayout.yaxis, {title: 'Messages'})
     }), plotConfig);
 
-    // Usuários únicos por dia
+    //Unique users per day
     Plotly.newPlot('unique-users-graph', [{
         x: {{ unique_user_dates_json|safe }},
         y: {{ unique_user_counts_json|safe }},
         type: 'bar',
         marker: {color: '#8c30f5'}
     }], Object.assign({}, customLayout, {
-        title: 'Usuários Únicos por Dia',
-        xaxis: Object.assign({}, customLayout.xaxis, {title: 'Data'}),
-        yaxis: Object.assign({}, customLayout.yaxis, {title: 'Usuários'})
+        xaxis: Object.assign({}, customLayout.xaxis),
+        yaxis: Object.assign({}, customLayout.yaxis, {title: 'Users'})
     }), plotConfig);
 
-    // Top usuários
+    // Top 5 Users
     Plotly.newPlot('top-users-graph', [{
         x: {{ top_usernames_json|safe }},
         y: {{ top_user_counts_json|safe }},
         type: 'bar',
-        marker: {color: '#3bace2'} // azul institucional
+        marker: {color: '#3bace2'} 
     }], Object.assign({}, customLayout, {
-        title: 'Top 5 Usuários',
-        xaxis: Object.assign({}, customLayout.xaxis, {title: 'Usuário'}),
-        yaxis: Object.assign({}, customLayout.yaxis, {title: 'Mensagens'})
+        xaxis: Object.assign({}, customLayout.xaxis),
+        yaxis: Object.assign({}, customLayout.yaxis, {title: 'Messages'})
     }), plotConfig);
 
     // Feedback Pie
@@ -118,9 +122,37 @@ <h3>Feedback Like/Dislike</h3>
         type: 'pie',
         marker: {colors: ['#81d4af', '#f78da7']}, 
     }], Object.assign({}, customLayout, {
-        title: 'Likes vs Dislikes',
         legend: {orientation: "h", x: 0.5, xanchor: 'center', font: {color: '#4a2d7c'}}
     }), plotConfig);
+
+    // Sentiments by Day
+    fetch("{% url 'sentiment_over_time' %}")
+      .then(response => response.json())
+      .then(data => {
+        const layout = Object.assign({}, customLayout, {
+          xaxis: Object.assign({}, customLayout.xaxis),
+          yaxis: Object.assign({}, customLayout.yaxis, {title: 'Messages', rangemode: 'tozero'}),
+          legend: {orientation: "h", x: 0.5, xanchor: 'center', font: {color: '#4a2d7c'}}
+        });
+        Plotly.newPlot('sentiment-graph', data.datasets, layout, plotConfig);
+      });
+    
+    // Sentiments Rank
+    fetch("{% url 'sentiment_ranking' %}")
+        .then(response => response.json())
+        .then(data => {
+            const barLayout = Object.assign({}, customLayout, {
+            xaxis: Object.assign({}, customLayout.xaxis),
+            yaxis: Object.assign({}, customLayout.yaxis, {title: 'Messages', rangemode: 'tozero'}),
+            margin: { t: 50, l: 40, r: 20, b: 40 }
+            });
+            Plotly.newPlot('sentiment-bar-graph', [{
+            x: data.sentiments,
+            y: data.counts,
+            type: 'bar',
+            marker: {color: '#8c30f5'}
+            }], barLayout, plotConfig);
+        });
 </script>
 
 </body>
diff --git a/happy-kids/frontend/urls.py b/happy-kids/frontend/urls.py
@@ -34,6 +34,8 @@
 
     path('create_diary/', views.create_diary, name='create_diary'),
     path("generate_suggestions/", views.generate_suggestions, name="generate_suggestions"),
-    path('dashboard', views.dashboard_view, name='dashboard')
+    path('dashboard', views.dashboard_view, name='dashboard'),
+    path('sentiment-over-time/', views.sentiment_over_time, name='sentiment_over_time'),
+    path('sentiment-ranking/', views.sentiment_ranking, name='sentiment_ranking'),
 
 ]
diff --git a/happy-kids/frontend/utils.py b/happy-kids/frontend/utils.py
@@ -4,22 +4,90 @@
 from django.utils import timezone
 from datetime import timedelta
 from api import models
+import openai
+import numpy as np
 
 def get_short_term_memory(user_id):
-    """Recupera a memória de curto prazo do banco de dados."""
+    """Recupera a memória de curto prazo do banco de dados"""
     time_threshold = timezone.now() - timedelta(minutes=30)
     
     conversations = (
         models.chat_memories.objects
         .filter(user_id=user_id, date_time__gte=time_threshold)
-        .order_by('-date_time')[:20]
+        .order_by('date_time') 
     )
     
-    messages = [
-        f"User: {conv.user_message}\nChat: {conv.chat_message}"
-        for conv in conversations
-    ]
-    
-    return messages
+    memory = []
+    for conv in conversations:
+        
+        if conv.user_message:
+            memory.append({"role": "user", "content": conv.user_message})
+        
+        if conv.chat_message:
+            memory.append({"role": "assistant", "content": conv.chat_message})
+
+    return memory
+
+
+def classify_sentiment(text):
+    system_prompt = (
+        "You are an expert assistant in human emotion and sentiment analysis.\n"
+        "Your task is to read any provided text and classify the predominant emotion expressed, choosing only one category from the following list.\n"
+        "Consider the context, nuance, and tone. If the text does not clearly express any of these emotions, respond with \"Neutral\".\n\n"
+        "Possible sentiment categories:\n"
+        "- Anger\n"
+        "- Fear\n"
+        "- Sadness\n"
+        "- Joy\n"
+        "- Disgust\n"
+        "- Surprise\n"
+        "- Neutral\n\n"
+        "Respond only with the category, no explanation."
+    )
+    user_prompt = f'Text: "{text}"\nCategory:'
+    try:
+        client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+        response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            max_tokens=10,
+            temperature=0
+        )
+        category = response.choices[0].message.content.strip()
+        return category
+    except Exception as e:
+        print(f"Error during sentiment analysis: {e}")
+        return None
+
+
+client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+def get_embedding(text):
+    response = client.embeddings.create(
+        model="text-embedding-3-small",
+        input=text,
+    )
+    return response.data[0].embedding
+
+
+
+def cosine_similarity(a, b):
+    a = np.array(a)
+    b = np.array(b)
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
 
+def get_relevant_memories(user_id, question, top_k=3):
+    question_emb = get_embedding(question)
 
+    history = models.chat_memories.objects.filter(user_id=user_id).exclude(embedding=None)
+    scored = []
+    for h in history:
+        sim = cosine_similarity(question_emb, h.embedding)
+        scored.append((sim, h))
+    scored.sort(reverse=True, key=lambda x: x[0])
+    top_memories = [x[1] for x in scored[:top_k]]
+    return top_memories
diff --git a/happy-kids/frontend/views.py b/happy-kids/frontend/views.py
diff --git a/happy-kids/polls b/happy-kids/polls
diff --git a/happy-kids/requirements.txt b/happy-kids/requirements.txt

Original file line number	Diff line number	Diff line change
`@@ -34,6 +34,8 @@`
`34`	`34`
`35`	`35`	`path('create_diary/', views.create_diary, name='create_diary'),`
`36`	`36`	`path("generate_suggestions/", views.generate_suggestions, name="generate_suggestions"),`
`37`		`- path('dashboard', views.dashboard_view, name='dashboard')`
	`37`	`+ path('dashboard', views.dashboard_view, name='dashboard'),`
	`38`	`+ path('sentiment-over-time/', views.sentiment_over_time, name='sentiment_over_time'),`
	`39`	`+ path('sentiment-ranking/', views.sentiment_ranking, name='sentiment_ranking'),`
`38`	`40`
`39`	`41`	`]`