update

haikalbot/services/analysis_service.py

@@ -1,49 +1,31 @@
-from django.db.models import Avg, Sum, Max, Min, ForeignKey, OneToOneField
 import inspect
+import hashlib
 from django.db import models
+from django.db.models import Avg, Sum, Max, Min, ForeignKey, OneToOneField, Count
 from django.utils.translation import gettext_lazy as _
+from django.utils import timezone
 
 
 def _localized_keys(language):
-    """
-    Get localized key names based on language.
-    
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: Dictionary of localized keys
-    :rtype: dict
-    """
-    if language == 'ar':
     return {
-            'type': 'نوع', 'model': 'النموذج', 'count': 'العدد', 'filters': 'الفلاتر_المطبقة',
+        'type': 'نوع' if language == 'ar' else 'type',
-            'error': 'خطأ', 'chart_type': 'نوع_الرسم_البياني', 'labels': 'التسميات', 'data': 'البيانات',
+        'model': 'النموذج' if language == 'ar' else 'model',
-            'visualization_data': 'بيانات_الرسم_البياني', 'field': 'الحقل', 'value': 'القيمة',
+        'count': 'العدد' if language == 'ar' else 'count',
-            'statistic_type': 'نوع_الإحصاء', 'results': 'النتائج', 'title': 'العنوان'
+        'filters': 'الفلاتر_المطبقة' if language == 'ar' else 'filters_applied',
-        }
+        'error': 'خطأ' if language == 'ar' else 'error',
-    else:
+        'chart_type': 'نوع_الرسم_البياني' if language == 'ar' else 'chart_type',
-        return {
+        'labels': 'التسميات' if language == 'ar' else 'labels',
-            'type': 'type', 'model': 'model', 'count': 'count', 'filters': 'filters_applied',
+        'data': 'البيانات' if language == 'ar' else 'data',
-            'error': 'error', 'chart_type': 'chart_type', 'labels': 'labels', 'data': 'data',
+        'visualization_data': 'بيانات_الرسم_البياني' if language == 'ar' else 'visualization_data',
-            'visualization_data': 'visualization_data', 'field': 'field', 'value': 'value',
+        'field': 'الحقل' if language == 'ar' else 'field',
-            'statistic_type': 'statistic_type', 'results': 'results', 'title': 'title'
+        'value': 'القيمة' if language == 'ar' else 'value',
+        'statistic_type': 'نوع_الإحصاء' if language == 'ar' else 'statistic_type',
+        'results': 'النتائج' if language == 'ar' else 'results',
+        'title': 'العنوان' if language == 'ar' else 'title',
     }
 
 
 def generate_count_insight(models, query_params, dealer_id=None, language='en'):
-    """
-    Generate count insights for the specified models.
-    
-    :param models: List of models to analyze
-    :type models: list
-    :param query_params: Query parameters for filtering
-    :type query_params: dict
-    :param dealer_id: Dealer ID for filtering
-    :type dealer_id: int
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: Count insights
-    :rtype: dict
-    """
     keys = _localized_keys(language)
     results = []
 
@@ -59,7 +41,9 @@ def generate_count_insight(models, query_params, dealer_id=None, language='en'):
 
             filters = {}
             for key, value in query_params.items():
-                if key not in ['field', 'operation'] and hasattr(model, key):
+                if key in ['field', 'operation']:
+                    continue
+                if hasattr(model, key):
                     try:
                         field = model._meta.get_field(key)
                         if isinstance(field, models.IntegerField):
@@ -76,13 +60,12 @@ def generate_count_insight(models, query_params, dealer_id=None, language='en'):
             results.append({
                 keys['model']: model.__name__,
                 keys['count']: queryset.count(),
-                keys['filters']: filters
+                keys['filters']: filters,
             })
-
         except Exception as e:
             results.append({
                 keys['model']: model.__name__,
-                keys['error']: str(e)
+                keys['error']: str(e),
             })
 
     return {
@@ -91,30 +74,17 @@ def generate_count_insight(models, query_params, dealer_id=None, language='en'):
         keys['visualization_data']: {
             keys['chart_type']: 'bar',
             keys['labels']: [r[keys['model']] for r in results if keys['count'] in r],
-            keys['data']: [r[keys['count']] for r in results if keys['count'] in r]
+            keys['data']: [r[keys['count']] for r in results if keys['count'] in r],
         }
     }
 
 
 def generate_statistics_insight(models, query_params, dealer_id=None, language='en'):
-    """
-    Generate statistical insights for the specified models.
-    
-    :param models: List of models to analyze
-    :type models: list
-    :param query_params: Query parameters for filtering
-    :type query_params: dict
-    :param dealer_id: Dealer ID for filtering
-    :type dealer_id: int
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: Statistical insights
-    :rtype: dict
-    """
     keys = _localized_keys(language)
     results = []
     field = query_params.get('field')
     operation = query_params.get('operation', 'average')
+    stat_map = {'average': Avg, 'sum': Sum, 'max': Max, 'min': Min}
 
     for model in models:
         try:
@@ -128,37 +98,28 @@ def generate_statistics_insight(models, query_params, dealer_id=None, language='
                 elif hasattr(model, 'dealer'):
                     queryset = queryset.filter(dealer=dealer_id)
 
-            filters = {}
+            filters = {
-            for k, v in query_params.items():
+                k: v for k, v in query_params.items()
-                if k not in ['field', 'operation'] and hasattr(model, k):
+                if k not in ['field', 'operation'] and hasattr(model, k)
-                    filters[k] = v
+            }
 
             if filters:
                 queryset = queryset.filter(**filters)
 
-            stat_map = {
+            value = queryset.aggregate(val=stat_map.get(operation, Count)(field))['val']
-                'average': Avg,
-                'sum': Sum,
-                'max': Max,
-                'min': Min
-            }
-
-            if operation in stat_map:
-                agg = queryset.aggregate(val=stat_map[operation](field))['val']
-                value = agg
-            else:
-                value = queryset.count()
 
             results.append({
                 keys['model']: model.__name__,
                 keys['field']: field,
                 keys['statistic_type']: operation,
                 keys['value']: value,
-                keys['filters']: filters
+                keys['filters']: filters,
             })
-
         except Exception as e:
-            results.append({keys['model']: model.__name__, keys['error']: str(e)})
+            results.append({
+                keys['model']: model.__name__,
+                keys['error']: str(e),
+            })
 
     return {
         keys['type']: keys['type'] + '_analysis',
@@ -173,18 +134,6 @@ def generate_statistics_insight(models, query_params, dealer_id=None, language='
 
 
 def generate_recommendations(model_classes, analysis_type, language='en'):
-    """
-    Generate recommendations based on model analysis.
-    
-    :param model_classes: List of models to analyze
-    :type model_classes: list
-    :param analysis_type: Type of analysis
-    :type analysis_type: str
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: List of recommendations
-    :rtype: list
-    """
     recs = []
     for model in model_classes:
         for field in model._meta.fields:
@@ -198,34 +147,20 @@ def generate_recommendations(model_classes, analysis_type, language='en'):
 
 
 def generate_model_insight(model, dealer_id=None, language='en'):
-    """
-    Generate insights for a specific model.
-    
-    :param model: Model to analyze
-    :type model: Model class
-    :param dealer_id: Dealer ID for filtering
-    :type dealer_id: int
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: Model insights
-    :rtype: dict
-    """
     keys = _localized_keys(language)
-    fields_info = [
+    fields_info = [{
-        {
         'name': f.name,
         'type': f.__class__.__name__,
         'null': f.null,
         'blank': f.blank,
         'unique': f.unique,
         'pk': f.primary_key
-        } for f in model._meta.fields
+    } for f in model._meta.fields]
-    ]
 
     try:
         qs = model.objects.all()
         if dealer_id:
-            if hasattr(model, 'dealer_id'):
+            if hasattr(model, 'dealer'):
                 qs = qs.filter(dealer_id=dealer_id)
             elif hasattr(model, 'dealer'):
                 qs = qs.filter(dealer=dealer_id)
@@ -242,20 +177,6 @@ def generate_model_insight(model, dealer_id=None, language='en'):
 
 
 def generate_relationship_insight(models, query_params=None, dealer_id=None, language='en'):
-    """
-    Generate relationship insights between models.
-    
-    :param models: List of models to analyze
-    :type models: list
-    :param query_params: Query parameters (unused)
-    :type query_params: dict
-    :param dealer_id: Dealer ID (unused)
-    :type dealer_id: int
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: Relationship insights
-    :rtype: dict
-    """
     from_ = "من" if language == 'ar' else "from"
     to_ = "إلى" if language == 'ar' else "to"
     rel_type = "نوع" if language == 'ar' else "type"
@@ -267,7 +188,7 @@ def generate_relationship_insight(models, query_params=None, dealer_id=None, lan
                 relationships.append({
                     from_: model.__name__,
                     to_: field.related_model.__name__,
-                    rel_type: field.__class__.__name__
+                    rel_type: field.__class__.__name__,
                 })
         for field in model._meta.many_to_many:
             relationships.append({
@@ -283,35 +204,22 @@ def generate_relationship_insight(models, query_params=None, dealer_id=None, lan
 
 
 def generate_performance_insight(models, query_params=None, dealer_id=None, language='en'):
-    """
-    Generate performance insights for models.
-    
-    :param models: List of models to analyze
-    :type models: list
-    :param query_params: Query parameters (unused)
-    :type query_params: dict
-    :param dealer_id: Dealer ID (unused)
-    :type dealer_id: int
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: Performance insights
-    :rtype: dict
-    """
     issues = []
+
     for model in models:
         for field in model._meta.fields:
             if isinstance(field, ForeignKey) and not field.db_index:
                 issues.append({
-                    'model': model.__name__,
+                    # 'model': model.__name__,
                     'field': field.name,
                     'issue': 'Missing index on ForeignKey'
                 })
-            if isinstance(field, models.CharField) and not field.db_index and field.name in ['name', 'title']:
+            # if isinstance(field, models.CharField) and not field.db_index and field.name in ['name', 'title']:
-                issues.append({
+            #     issues.append({
-                    'model': model.__name__,
+            #         'model': model.__name__,
-                    'field': field.name,
+            #         'field': field.name,
-                    'issue': 'Unindexed CharField used in filtering'
+            #         'issue': 'Unindexed CharField used in filtering'
-                })
+            #     })
 
     return {
         'type': 'تحليل_الأداء' if language == 'ar' else 'performance_analysis',

haikalbot/services/cache_service.py

@@ -1,93 +1,53 @@
+import hashlib
+import logging
 from django.utils import timezone
 from django.db import models
 from ..models import AnalysisCache
-import hashlib
-import logging
 
 logger = logging.getLogger(__name__)
 
+
 class CacheService:
-    """
-    Service for handling analysis result caching operations.
-    
-    This service provides methods for generating cache keys, retrieving
-    cached results, and storing new results in the cache.
-    """
-    
     def generate_hash(self, prompt, dealer_id, language):
         """
-        Generate a unique hash for the prompt, dealer, and language combination.
+        Generate a unique MD5 hash based on the prompt, dealer ID, and language.
-        
-        :param prompt: The user's prompt text
-        :type prompt: str
-        :param dealer_id: The dealer's ID
-        :type dealer_id: int
-        :param language: The language code
-        :type language: str
-        :return: MD5 hash string
-        :rtype: str
         """
-        cache_key = f"{prompt}:{dealer_id or 'all'}:{language}"
+        key = f"{prompt}:{dealer_id or 'all'}:{language}"
-        return hashlib.md5(cache_key.encode()).hexdigest()
+        return hashlib.md5(key.encode()).hexdigest()
 
     def get_cached_result(self, prompt_hash, user, dealer_id):
         """
-        Retrieve a cached result if available and not expired.
+        Retrieve a cached analysis result based on hash, dealer, and optionally user.
-        
-        :param prompt_hash: The hash key for the cache entry
-        :type prompt_hash: str
-        :param user: The user making the request
-        :type user: User
-        :param dealer_id: The dealer's ID
-        :type dealer_id: int
-        :return: Cached result or None if not found
-        :rtype: dict or None
         """
         try:
-            cache_entry = AnalysisCache.objects.filter(
+            # Check for user-specific cache if authenticated
-                prompt_hash=prompt_hash,
-                dealer_id=dealer_id,
-                expires_at__gt=timezone.now()
-            ).first()
-            
-            # If user is authenticated, also check user-specific cache
             if user and user.is_authenticated:
                 user_cache = AnalysisCache.objects.filter(
                     prompt_hash=prompt_hash,
                     user=user,
                     expires_at__gt=timezone.now()
                 ).first()
-                
-                # User-specific cache takes precedence
                 if user_cache:
                     return user_cache.result
 
-            return cache_entry.result if cache_entry else None
+            # Otherwise check for dealer-wide cache
+            dealer_cache = AnalysisCache.objects.filter(
+                prompt_hash=prompt_hash,
+                dealer_id=dealer_id,
+                expires_at__gt=timezone.now()
+            ).first()
+
+            return dealer_cache.result if dealer_cache else None
         except Exception as e:
-            logger.warning(f"Error retrieving cache: {str(e)}")
+            logger.warning(f"Cache retrieval failed: {str(e)}")
             return None
 
     def cache_result(self, prompt_hash, result, user, dealer_id, duration=3600):
         """
-        Store a result in the cache.
+        Save or update a cached result with an expiration timestamp.
-        
-        :param prompt_hash: The hash key for the cache entry
-        :type prompt_hash: str
-        :param result: The result to cache
-        :type result: dict
-        :param user: The user making the request
-        :type user: User
-        :param dealer_id: The dealer's ID
-        :type dealer_id: int
-        :param duration: Cache duration in seconds
-        :type duration: int
-        :return: None
         """
         try:
-            # Calculate expiration time
             expires_at = timezone.now() + timezone.timedelta(seconds=duration)
-            
-            # Create or update cache entry
             AnalysisCache.objects.update_or_create(
                 prompt_hash=prompt_hash,
                 user=user if user and user.is_authenticated else None,
@@ -98,4 +58,4 @@ class CacheService:
                 }
             )
         except Exception as e:
-            logger.warning(f"Error caching result: {str(e)}")
+            logger.warning(f"Cache saving failed: {str(e)}")

haikalbot/services/llm_service.py

@@ -1,25 +1,18 @@
+import json
+import logging
+from django.apps import apps
+from django.http import JsonResponse
+from django.db.models import Count, Avg, Max, Min
 from langchain_ollama import OllamaLLM
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from django.conf import settings
-import logging
 
 logger = logging.getLogger(__name__)
 
 
 def get_llm_instance():
-    """
-    Initialize and return an Ollama LLM instance configured for Arabic support.
-    
-    This function creates a new LLM instance with optimized parameters for
-    both Arabic and English language processing. It reads configuration from
-    Django settings or uses sensible defaults.
-    
-    :return: Configured OllamaLLM instance or None if initialization fails
-    :rtype: OllamaLLM or None
-    """
     try:
-        # Get settings from Django settings or use defaults
         base_url = getattr(settings, 'OLLAMA_BASE_URL', 'http://localhost:11434')
         model = getattr(settings, 'OLLAMA_MODEL', 'qwen3:8b')
         temperature = getattr(settings, 'OLLAMA_TEMPERATURE', 0.2)
@@ -45,23 +38,10 @@ def get_llm_instance():
 
 
 def get_llm_chain(language='en'):
-    """
-    Create a LangChain for analyzing prompts in Arabic or English.
-    
-    This function creates a chain that processes user prompts and extracts
-    structured information about the analysis request. It supports both
-    Arabic and English languages.
-    
-    :param language: Language code ('en' or 'ar')
-    :type language: str
-    :return: LangChain for prompt analysis or None if initialization fails
-    :rtype: LLMChain or None
-    """
     llm = get_llm_instance()
     if not llm:
         return None
 
-    # Define the prompt template based on language
     if language == 'ar':
         template = """
         قم بتحليل الاستعلام التالي وتحديد نوع التحليل المطلوب ونماذج البيانات المستهدفة وأي معلمات استعلام.
@@ -89,11 +69,82 @@ def get_llm_chain(language='en'):
         }
         """
 
-    # Create the prompt template
     prompt_template = PromptTemplate(
         input_variables=["prompt"],
         template=template
     )
 
-    # Create and return the LLM chain
     return prompt_template | llm
+
+
+def analyze_models_with_orm(analysis_type, target_models, query_params):
+    results = {}
+
+    for model_name in target_models:
+        try:
+            model = apps.get_model('your_app_name', model_name)
+        except LookupError:
+            results[model_name] = {"error": f"Model '{model_name}' not found"}
+            continue
+
+        try:
+            queryset = model.objects.filter(**query_params)
+
+            if analysis_type == 'count':
+                results[model_name] = {'count': queryset.count()}
+
+            elif analysis_type == 'statistics':
+                numeric_fields = [f.name for f in model._meta.fields if f.get_internal_type() in ['IntegerField', 'FloatField', 'DecimalField']]
+                stats = {}
+                for field in numeric_fields:
+                    stats[field] = {
+                        'avg': queryset.aggregate(avg=Avg(field))['avg'],
+                        'max': queryset.aggregate(max=Max(field))['max'],
+                        'min': queryset.aggregate(min=Min(field))['min']
+                    }
+                results[model_name] = stats
+
+            elif analysis_type == 'relationship':
+                related = {}
+                for field in model._meta.get_fields():
+                    if field.is_relation and field.many_to_one:
+                        related[field.name] = queryset.values(field.name).annotate(count=Count(field.name)).count()
+                results[model_name] = related
+
+            elif analysis_type == 'performance':
+                results[model_name] = {'note': 'Performance analysis logic not implemented.'}
+
+            else:
+                results[model_name] = list(queryset.values())
+
+        except Exception as e:
+            results[model_name] = {'error': str(e)}
+
+    return results
+
+
+def analyze_prompt_and_return_json(request):
+    try:
+        prompt = request.POST.get('prompt')
+        language = request.POST.get('language', 'en')
+
+        chain = get_llm_chain(language)
+        if not chain:
+            return JsonResponse({'success': False, 'error': 'LLM not initialized'})
+
+        result = chain.invoke({'prompt': prompt})
+        parsed = json.loads(result)
+
+        analysis_type = parsed.get('analysis_type')
+        target_models = parsed.get('target_models', [])
+        query_params = parsed.get('query_params', {})
+
+        if not analysis_type or not target_models:
+            return JsonResponse({'success': False, 'error': 'Incomplete analysis instruction returned by LLM'})
+
+        orm_results = analyze_models_with_orm(analysis_type, target_models, query_params)
+
+        return JsonResponse({'success': True, 'data': orm_results})
+
+    except Exception as e:
+        return JsonResponse({'success': False, 'error': str(e)})

templates/haikalbot/chatbot.html

@@ -3,18 +3,18 @@
 
 {% block title %}
     {{ _("Haikalbot") }}
-{% endblock %}
+{% endblock title %}
 
 {% block description %}
 AI assistant
-{% endblock %}
+{% endblock description %}
 
 {% block customCSS %}
-<!-- No custom CSS as requested -->
+<style>
-{% endblock %}
+    .chart-container {
-
+        width: 100%;
-{% block content %}
+        margin-top: 1rem;
-    <style>
+    }
     .chat-container {
         max-width: 800px;
         margin: 0 auto;
@@ -50,16 +50,22 @@ AI assistant
         font-size: 0.8rem;
         color: #6c757d;
     }
-    </style>
+    .character-count.warning {
+        color: #dc3545;
+        font-weight: bold;
+    }
+</style>
+{% endblock customCSS %}
+
+{% block content %}
+<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
 <div class="card shadow-none mb-3">
   <div class="card-header d-flex justify-content-between align-items-center">
-
       <div class="d-flex align-items-center fw-bolder fs-3 d-inline-block">
             <img class="d-dark-none" src="{% static 'images/favicons/haikalbot_v1.png' %}" alt="{% trans 'home' %}" width="32" />
             <img class="d-light-none" src="{% static 'images/favicons/haikalbot_v2.png' %}" alt="{% trans 'home' %}" width="32" />
       </div>
-
-
       <div class="d-flex gap-3">
           <span id="clearChatBtn" class="translate-middle-y cursor-pointer" title="{% if LANGUAGE_CODE == 'ar' %}مسح المحادثة{% else %}Clear Chat{% endif %}">
             <i class="fas fa-trash-alt text-danger"></i>
@@ -69,52 +75,71 @@ AI assistant
           </span>
       </div>
   </div>
-
   <div class="card-body p-0">
-    <div id="chatMessages" class="overflow-auto p-3" style="height: 60vh;">
+    <div id="chatMessages" class="overflow-auto p-3" style="height: 60vh;"></div>
-      <!-- Chat messages will be appended here -->
-    </div>
-
     <div class="bg-100 border-top p-3">
       <div class="d-flex gap-2 flex-wrap mb-3" id="suggestionChips">
-        <button class="btn btn-sm btn-outline-primary suggestion-chip">
+        <button class="btn btn-sm btn-outline-primary suggestion-chip">{{ _("How many cars are in inventory")}}?</button>
-            {{ _("How many cars are in inventory")}}?
+        <button class="btn btn-sm btn-outline-primary suggestion-chip">{{ _("Show me sales analysis")}}</button>
-        </button>
+        <button class="btn btn-sm btn-outline-primary suggestion-chip">{{ _("What are the best-selling cars")}}?</button>
-        <button class="btn btn-sm btn-outline-primary suggestion-chip">
-            {{ _("Show me sales analysis")}}
-        </button>
-        <button class="btn btn-sm btn-outline-primary suggestion-chip">
-            {{ _("What are the best-selling cars")}}?
-        </button>
       </div>
       <div class="chat-container">
-
-
         <div class="textarea-container mb-3">
-<label for="messageInput"></label>
+          <label for="messageInput"></label>
-                <textarea class="form-control chat-textarea" id="messageInput" rows="3"
+          <textarea class="form-control chat-textarea" id="messageInput" rows="3" placeholder="{{ _("Type your message here")}}..." maxlength="400"></textarea>
-                          placeholder="{{ _("Type your message here")}}..."></textarea>
           <div class="textarea-footer">
             <div class="character-count">
               <span id="charCount">0</span>/400
             </div>
-
+            <span class="send-button position-absolute top-50 end-0 translate-middle-y cursor-pointer" id="sendMessageBtn" disabled>
-                    <span class="send-button position-absolute top-50 end-0 translate-middle-y cursor-pointer"
-          id="sendMessageBtn" disabled>
               <i class="fas fa-paper-plane text-body"></i>
             </span>
           </div>
-</div>
+        </div>
       </div>
     </div>
   </div>
 </div>
 
-
-    <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
-
 <script>
-    $(document).ready(function() {
+// Global configuration
+const MAX_MESSAGE_LENGTH = 400;
+const WARNING_THRESHOLD = 350;
+const isArabic = '{{ LANGUAGE_CODE }}' === 'ar';
+
+// Chart rendering function
+function renderInsightChart(labels, data, chartType = 'bar', title = 'Insight Chart') {
+  const canvasId = 'chart_' + Date.now();
+  const chartHtml = `<div class="chart-container"><canvas id="${canvasId}"></canvas></div>`;
+  $('#chatMessages').append(chartHtml);
+
+  new Chart(document.getElementById(canvasId), {
+    type: chartType,
+    data: {
+      labels: labels,
+      datasets: [{
+        label: title,
+        data: data,
+        borderWidth: 1,
+        backgroundColor: 'rgba(75, 192, 192, 0.5)',
+        borderColor: 'rgba(75, 192, 192, 1)',
+      }]
+    },
+    options: {
+      responsive: true,
+      plugins: {
+        legend: { display: true },
+        title: { display: true, text: title }
+      },
+      scales: {
+        y: { beginAtZero: true }
+      }
+    }
+  });
+}
+
+$(document).ready(function() {
+    // DOM elements
     const messageInput = $('#messageInput');
     const charCount = $('#charCount');
     const sendMessageBtn = $('#sendMessageBtn');
@@ -123,82 +148,63 @@ AI assistant
     const exportChatBtn = $('#exportChatBtn');
     const suggestionChips = $('.suggestion-chip');
 
-        // Enable/disable send button based on input
+    // Initialize character count
-        messageInput.on('input', function() {
+    updateCharacterCount();
-            sendMessageBtn.prop('disabled', !messageInput.val().trim());
 
-            // Auto-resize textarea
+    // Event handlers
-            this.style.height = 'auto';
+    messageInput.on('input', handleInput);
-            this.style.height = (this.scrollHeight) + 'px';
+    messageInput.on('keydown', handleKeyDown);
-        });
+    sendMessageBtn.on('click', sendMessage);
+    suggestionChips.on('click', handleSuggestionClick);
+    clearChatBtn.on('click', clearChat);
+    exportChatBtn.on('click', exportChat);
 
-        // Send message on Enter key (but allow Shift+Enter for new line)
+    // Input handling
-        messageInput.on('keydown', function(e) {
+    function handleInput() {
+        updateCharacterCount();
+        autoResizeTextarea();
+        toggleSendButton();
+    }
+
+    function handleKeyDown(e) {
         if (e.key === 'Enter' && !e.shiftKey) {
             e.preventDefault();
             if (!sendMessageBtn.prop('disabled')) {
                 sendMessage();
             }
         }
-        });
+    }
 
-        // Send message on button click
+    function handleSuggestionClick() {
-        sendMessageBtn.on('click', sendMessage);
-
-        // Use suggestion chips
-        suggestionChips.on('click', function() {
         messageInput.val($(this).text().trim());
+        updateCharacterCount();
+        autoResizeTextarea();
         sendMessageBtn.prop('disabled', false);
         sendMessage();
-        });
-
-        // Clear chat
-        clearChatBtn.on('click', function() {
-            if (confirm('{% if LANGUAGE_CODE == "ar" %}هل أنت متأكد من أنك تريد مسح المحادثة؟{% else %}Are you sure you want to clear the chat?{% endif %}')) {
-                // Keep only the first welcome message
-                const welcomeMessage = chatMessages.children().first();
-                chatMessages.empty().append(welcomeMessage);
     }
-        });
 
-        // Export chat
+    // UI utilities
-        exportChatBtn.on('click', function() {
+    function updateCharacterCount() {
-            let chatContent = '';
+        const currentLength = messageInput.val().length;
-            $('.message').each(function() {
+        charCount.text(currentLength);
-                const isUser = $(this).hasClass('user-message');
-                const sender = isUser ? '{% if LANGUAGE_CODE == "ar" %}أنت{% else %}You{% endif %}' : '{% if LANGUAGE_CODE == "ar" %}المساعد الذكي{% else %}AI Assistant{% endif %}';
-                const text = $(this).find('.chat-message').text().trim();
-                const time = $(this).find('.text-400').text().trim();
 
-                chatContent += `${sender} (${time}):\n${text}\n\n`;
+        if (currentLength > WARNING_THRESHOLD) {
-            });
+            charCount.parent().addClass('warning');
+        } else {
+            charCount.parent().removeClass('warning');
+        }
+    }
 
-            // Create and trigger download
+    function autoResizeTextarea() {
-            const blob = new Blob([chatContent], { type: 'text/plain' });
+        messageInput[0].style.height = 'auto';
-            const url = URL.createObjectURL(blob);
+        messageInput[0].style.height = (messageInput[0].scrollHeight) + 'px';
-            const a = document.createElement('a');
+    }
-            a.href = url;
-            a.download = 'chat-export-' + new Date().toISOString().slice(0, 10) + '.txt';
-            document.body.appendChild(a);
-            a.click();
-            document.body.removeChild(a);
-            URL.revokeObjectURL(url);
-        });
 
-        // Copy message text
+    function toggleSendButton() {
-        $(document).on('click', '.copy-btn', function() {
+        sendMessageBtn.prop('disabled', !messageInput.val().trim());
-            const text = $(this).closest('.d-flex').find('.chat-message').text().trim();
+    }
-            navigator.clipboard.writeText(text).then(() => {
-                // Show temporary success indicator
-                const originalIcon = $(this).html();
-                $(this).html('<i class="fas fa-check"></i>');
-                setTimeout(() => {
-                    $(this).html(originalIcon);
-                }, 1500);
-            });
-        });
 
-        // Function to send message
+    // Chat actions
     function sendMessage() {
         const message = messageInput.val().trim();
         if (!message) return;
@@ -206,9 +212,8 @@ AI assistant
         // Add user message to chat
         addMessage(message, true);
 
-            // Clear input and reset height
+        // Clear input and reset UI
-            messageInput.val('').css('height', 'auto');
+        resetInputUI();
-            sendMessageBtn.prop('disabled', true);
 
         // Show typing indicator
         showTypingIndicator();
@@ -226,63 +231,147 @@ AI assistant
                 'X-CSRFToken': '{{ csrf_token }}'
             },
             success: function(response) {
-                    // Hide typing indicator
+                processBotResponse(response);
-                    hideTypingIndicator();
-
-                    // Process response
-                    let botResponse = '';
-
-                    if (response.response) {
-                        botResponse = response.response;
-                    } else if (response.insights) {
-                        // Format insights as a readable response
-                        botResponse = formatInsightsResponse(response);
-                    } else {
-                        botResponse = '{% if LANGUAGE_CODE == "ar" %}عذرًا، لم أتمكن من معالجة طلبك.{% else %}Sorry, I couldn\'t process your request.{% endif %}';
-                    }
-
-                    // Add bot response to chat
-                    addMessage(botResponse, false);
-
-                    // Scroll to bottom
-                    scrollToBottom();
             },
             error: function(xhr, status, error) {
-                    // Hide typing indicator
+                handleRequestError(error);
-                    hideTypingIndicator();
-
-                    // Add error message
-                    const errorMsg = '{% if LANGUAGE_CODE == "ar" %}عذرًا، حدث خطأ أثناء معالجة طلبك. يرجى المحاولة مرة أخرى.{% else %}Sorry, an error occurred while processing your request. Please try again.{% endif %}';
-                    addMessage(errorMsg, false);
-
-                    console.error('Error:', error);
             }
         });
     }
 
-        // Function to add message to chat
+    function resetInputUI() {
+        messageInput.val('').css('height', 'auto');
+        charCount.text('0').parent().removeClass('warning');
+        sendMessageBtn.prop('disabled', true);
+    }
+
+    function processBotResponse(response) {
+        hideTypingIndicator();
+
+        // Debug response structure
+        console.log("API Response:", response);
+
+        let botResponse = '';
+
+        // Check for direct response first
+        if (response.response) {
+            botResponse = response.response;
+        }
+        // Then check for insights data
+        else if (hasInsightsData(response)) {
+            botResponse = formatInsightsResponse(response);
+        }
+        // Fallback
+        else {
+            botResponse = isArabic ?
+                'عذرًا، لم أتمكن من معالجة طلبك. يبدو أن هيكل الاستجابة غير متوقع.' :
+                'Sorry, I couldn\'t process your request. The response structure appears unexpected.';
+            console.error("Unexpected response structure:", response);
+        }
+
+        addMessage(botResponse, false);
+        scrollToBottom();
+    }
+
+    function hasInsightsData(response) {
+        return response.insights || response['التحليلات'] ||
+               response.recommendations || response['التوصيات'];
+    }
+
+    function handleRequestError(error) {
+        hideTypingIndicator();
+        const errorMsg = isArabic ?
+            'عذرًا، حدث خطأ أثناء معالجة طلبك. يرجى المحاولة مرة أخرى.' :
+            'Sorry, an error occurred while processing your request. Please try again.';
+        addMessage(errorMsg, false);
+        console.error('API Error:', error);
+    }
+
+    // Chat management
+    function clearChat() {
+        if (confirm(isArabic ?
+            'هل أنت متأكد من أنك تريد مسح المحادثة؟' :
+            'Are you sure you want to clear the chat?')) {
+            const welcomeMessage = chatMessages.children().first();
+            chatMessages.empty().append(welcomeMessage);
+        }
+    }
+
+    function exportChat() {
+        let chatContent = '';
+        $('.message').each(function() {
+            const isUser = $(this).hasClass('user-message');
+            const sender = isUser ?
+                (isArabic ? 'أنت' : 'You') :
+                (isArabic ? 'المساعد الذكي' : 'AI Assistant');
+            const text = $(this).find('.chat-message').text().trim();
+            const time = $(this).find('.text-400').text().trim();
+
+            chatContent += `${sender} (${time}):\n${text}\n\n`;
+        });
+
+        downloadTextFile(chatContent, 'chat-export-' + new Date().toISOString().slice(0, 10) + '.txt');
+    }
+
+    function downloadTextFile(content, filename) {
+        const blob = new Blob([content], { type: 'text/plain' });
+        const url = URL.createObjectURL(blob);
+        const a = document.createElement('a');
+        a.href = url;
+        a.download = filename;
+        document.body.appendChild(a);
+        a.click();
+        document.body.removeChild(a);
+        URL.revokeObjectURL(url);
+    }
+
+    // Message display functions
     function addMessage(text, isUser) {
         const time = new Date().toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' });
-            const messageClass = isUser ? 'user-message justify-content-end' : '';
+        const messageClass = isUser ? 'user-message justify-content-between' : '';
 
-            let avatarHtml = '';
+        const avatarHtml = getAvatarHtml(isUser);
-            let messageHtml = '';
+        const messageHtml = getMessageHtml(text, isUser, time);
 
-            if (isUser) {
+        const fullMessageHtml = `
-                // User message
+            <div class="message d-flex mb-3 ${messageClass}">
-                avatarHtml = `
+                ${avatarHtml}
-                    <div class="avatar avatar-l ms-3 order-1">
+                ${messageHtml}
-                        <div class="avatar-name rounded-circle bg-primary text-white"><span><i class="fas fa-user"></i></span></div>
             </div>
         `;
 
-                // Process text (no markdown for user messages)
+        chatMessages.append(fullMessageHtml);
-                messageHtml = `
+        scrollToBottom();
+    }
+
+    function getAvatarHtml(isUser) {
+        if (isUser) {
+            return `
+                <div class="avatar avatar-l ms-3 order-1">
+                    <div class="avatar-name rounded-circle">
+                        <span><i class="fas fa-user"></i></span>
+                    </div>
+                </div>
+            `;
+        }
+        return `
+            <div class="me-3">
+                <div class="d-flex align-items-center fw-bolder fs-3 d-inline-block">
+                    <img class="d-dark-none" src="{% static 'images/favicons/haikalbot_v1.png' %}" width="32" />
+                    <img class="d-light-none" src="{% static 'images/favicons/haikalbot_v2.png' %}" width="32" />
+                </div>
+            </div>
+        `;
+    }
+
+    function getMessageHtml(text, isUser, time) {
+        if (isUser) {
+            return `
                 <div class="flex-1 order-0">
                     <div class="w-xxl-75 ms-auto">
                         <div class="d-flex hover-actions-trigger align-items-center">
                             <div class="hover-actions start-0 top-50 translate-middle-y">
-                                    <button class="btn btn-phoenix-secondary btn-icon fs--2 round-btn copy-btn" type="button" title="{% if LANGUAGE_CODE == 'ar' %}نسخ{% else %}Copy{% endif %}">
+                                <button class="btn btn-phoenix-secondary btn-icon fs--2 round-btn copy-btn" type="button" title="${isArabic ? 'نسخ' : 'Copy'}">
                                     <i class="fas fa-copy"></i>
                                 </button>
                             </div>
@@ -296,21 +385,11 @@ AI assistant
                     </div>
                 </div>
             `;
-            } else {
+        }
-                // Bot message
-                avatarHtml = `
-                    <div class="me-3">
-                        <div class="d-flex align-items-center fw-bolder fs-3 d-inline-block">
-                            <img class="d-dark-none" src="{% static 'images/favicons/haikalbot_v1.png' %}" width="32" />
-                            <img class="d-light-none" src="{% static 'images/favicons/haikalbot_v2.png' %}" width="32" />
-                        </div>
-                    </div>
-                `;
 
-                // Process markdown for bot messages
         const processedText = marked.parse(text);
 
-                messageHtml = `
+        return `
             <div class="flex-1">
                 <div class="w-xxl-75">
                     <div class="d-flex hover-actions-trigger align-items-center">
@@ -318,7 +397,7 @@ AI assistant
                             ${processedText}
                         </div>
                         <div class="hover-actions end-0 top-50 translate-middle-y">
-                                    <button class="btn btn-phoenix-secondary btn-icon fs--2 round-btn copy-btn" type="button" title="{{_("Copy")}}">
+                            <button class="btn btn-phoenix-secondary btn-icon fs--2 round-btn copy-btn" type="button" title="${isArabic ? 'نسخ' : 'Copy'}">
                                 <i class="fas fa-copy"></i>
                             </button>
                         </div>
@@ -331,27 +410,17 @@ AI assistant
         `;
     }
 
-            const fullMessageHtml = `
-                <div class="message d-flex mb-3 ${messageClass}">
-                    ${avatarHtml}
-                    ${messageHtml}
-                </div>
-            `;
-
-            chatMessages.append(fullMessageHtml);
-            scrollToBottom();
-        }
-
-        // Function to show typing indicator
     function showTypingIndicator() {
         const typingHtml = `
             <div class="message d-flex mb-3" id="typingIndicator">
                 <div class="avatar avatar-l me-3">
-                        <div class="avatar-name rounded-circle"><span><i class="fas fa-robot"></i></span></div>
+                    <div class="avatar-name rounded-circle">
+                        <span><i class="fas fa-robot"></i></span>
+                    </div>
                 </div>
                 <div class="flex-1 d-flex align-items-center">
                     <div class="spinner-border text-phoenix-secondary me-2" role="status"></div>
-                        <span class="fs-9">{% if LANGUAGE_CODE == 'ar' %}جاري الكتابة...{% else %}Typing...{% endif %}</span>
+                    <span class="fs-9">${isArabic ? 'جاري الكتابة...' : 'Typing...'}</span>
                 </div>
             </div>
         `;
@@ -360,98 +429,95 @@ AI assistant
         scrollToBottom();
     }
 
-        // Function to hide typing indicator
     function hideTypingIndicator() {
         $('#typingIndicator').remove();
     }
 
-        // Function to scroll chat to bottom
     function scrollToBottom() {
         chatMessages.scrollTop(chatMessages[0].scrollHeight);
     }
 
-        // Function to format insights response
+    // Insights formatting
     function formatInsightsResponse(response) {
+        console.log("Formatting insights response:", response);
+
         let formattedResponse = '';
-            const insightsKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'التحليلات' : 'insights';
-            const recsKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'التوصيات' : 'recommendations';
 
-            if (response[insightsKey] && response[insightsKey].length > 0) {
+        // Get data using both possible key formats
-                formattedResponse += '{{ LANGUAGE_CODE }}' === 'ar' ? '## نتائج التحليل\n\n' : '## Analysis Results\n\n';
+        const insightsData = response.insights || response['التحليلات'] || [];
+        const recommendationsData = response.recommendations || response['التوصيات'] || [];
 
-                response[insightsKey].forEach(insight => {
+        // Process insights
+        if (insightsData.length > 0) {
+            formattedResponse += isArabic ? '## نتائج التحليل\n\n' : '## Analysis Results\n\n';
+
+            insightsData.forEach(insight => {
                 if (insight.type) {
                     formattedResponse += `### ${insight.type}\n\n`;
                 }
 
-                    if (insight.results) {
+                if (insight.results && Array.isArray(insight.results)) {
                     insight.results.forEach(result => {
                         if (result.error) {
                             formattedResponse += `- **${result.model || ''}**: ${result.error}\n`;
                         } else if (result.count !== undefined) {
                             formattedResponse += `- **${result.model || ''}**: ${result.count}\n`;
                         } else if (result.value !== undefined) {
-                                const fieldKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'الحقل' : 'field';
+                            const field = getLocalizedValue(result, 'field', 'الحقل');
-                                const statTypeKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'نوع_الإحصاء' : 'statistic_type';
+                            const statType = getLocalizedValue(result, 'statistic_type', 'نوع_الإحصاء');
-                                formattedResponse += `- **${result.model || ''}**: ${result[statTypeKey]} of ${result[fieldKey]} = ${result.value}\n`;
+                            formattedResponse += `- **${result.model || ''}**: ${statType} ${isArabic ? 'لـ' : 'of'} ${field} = ${result.value}\n`;
                         }
                     });
                     formattedResponse += '\n';
                 }
 
-                    if (insight.relationships) {
+                if (insight.relationships && Array.isArray(insight.relationships)) {
-                        formattedResponse += '{{ LANGUAGE_CODE }}' === 'ar' ? ' العلاقات:\n\n' : ' Relationships:\n\n';
+                    formattedResponse += isArabic ? '### العلاقات\n\n' : '### Relationships\n\n';
                     insight.relationships.forEach(rel => {
-                            const fromKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'من' : 'from';
+                        const from = getLocalizedValue(rel, 'from', 'من');
-                            const toKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'إلى' : 'to';
+                        const to = getLocalizedValue(rel, 'to', 'إلى');
-                            const typeKey = '{{ LANGUAGE_CODE }}' === 'ar' ? 'نوع' : 'type';
+                        const type = getLocalizedValue(rel, 'type', 'نوع');
-                            formattedResponse += `- ${rel[fromKey]} → ${rel[toKey]} (${rel[typeKey]})\n`;
+                        formattedResponse += `- ${from} → ${to} (${type})\n`;
                     });
                     formattedResponse += '\n';
                 }
             });
         }
 
-            if (response[recsKey] && response[recsKey].length > 0) {
+        // Process recommendations
-                formattedResponse += '{{ LANGUAGE_CODE }}' === 'ar' ? ' التوصيات\n\n' : ' Recommendations\n\n';
+        if (recommendationsData.length > 0) {
-                response[recsKey].forEach(rec => {
+            formattedResponse += isArabic ? '## التوصيات\n\n' : '## Recommendations\n\n';
+            recommendationsData.forEach(rec => {
                 formattedResponse += `- ${rec}\n`;
             });
         }
 
-            return formattedResponse || '{{ LANGUAGE_CODE }}' === 'ar' ? 'تم تحليل البيانات بنجاح.' : 'Data analyzed successfully.';
+        return formattedResponse.trim() ||
+               (isArabic ? 'تم تحليل البيانات بنجاح ولكن لا توجد نتائج للعرض.' : 'Data analyzed successfully but no results to display.');
+    }
+
+    function getLocalizedValue(obj, englishKey, arabicKey) {
+        return isArabic ? (obj[arabicKey] || obj[englishKey] || '') : (obj[englishKey] || obj[arabicKey] || '');
+    }
+
+    // Copy message functionality
+    $(document).on('click', '.copy-btn', function() {
+        const text = $(this).closest('.d-flex').find('.chat-message').text().trim();
+        navigator.clipboard.writeText(text).then(() => {
+            showCopySuccess($(this));
+        });
+    });
+
+    function showCopySuccess(button) {
+        const originalIcon = button.html();
+        button.html('<i class="fas fa-check"></i>');
+        setTimeout(() => {
+            button.html(originalIcon);
+        }, 1500);
     }
 
     // Initialize
     scrollToBottom();
-    });
+});
-    messageInput.addEventListener('input', function() {
-            const currentLength = this.value.length;
-            charCount.textContent = currentLength;
-
-            // Optional: Add warning when approaching limit
-            if (currentLength > 350) {
-                charCount.style.color = 'red';
-            } else {
-                charCount.style.color = 'inherit';
-            }
-        });
 </script>
-{% endblock %}
+{% endblock content %}
-
-{% block customJS %}
-<!-- JS will be loaded from static file or added separately -->
-{% endblock %}
-
-
-
-
-
-
-
-
-
-
-
-
-