Upd analytics

helpers/coder.py

@@ -73,7 +73,7 @@ async def generate_code_artifacts(
             )
     except Exception:
         pass
-    code_md = await generate_answer_with_model(selection, system_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+    code_md = await generate_answer_with_model(selection, system_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "coding")
     code_md = (code_md or "").strip()
 
     if not code_md:

helpers/diagram.py

@@ -92,7 +92,7 @@ async def generate_mermaid_diagram(
             )
     except Exception:
         pass
-    diagram = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+    diagram = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "diagram")
     diagram = (diagram or "").strip()
 
     # Strip accidental code fences
@@ -186,7 +186,7 @@ Please provide the corrected Mermaid code that will render successfully."""
 
         # Use NVIDIA_LARGE for better error correction
         selection = {"provider": "nvidia_large", "model": "openai/gpt-oss-120b"}
-        response = await generate_answer_with_model(selection, sys_prompt, user_prompt, None, None)
+        response = await generate_answer_with_model(selection, sys_prompt, user_prompt, None, None, user_id, "diagram_fix")
         
         if response:
             # Clean up the response

routes/chats.py

@@ -732,7 +732,9 @@ async def _chat_impl(
             system_prompt=system_prompt,
             user_prompt=user_prompt,
             gemini_rotator=gemini_rotator,
-            nvidia_rotator=nvidia_rotator
+            nvidia_rotator=nvidia_rotator,
+            user_id=user_id,
+            context="chat"
         )
         logger.info(f"[CHAT] Answer generated successfully, length: {len(answer)}")
     except Exception as e:
@@ -930,7 +932,9 @@ async def chat_with_search(
             system_prompt=system_prompt,
             user_prompt=user_prompt,
             gemini_rotator=gemini_rotator,
-            nvidia_rotator=nvidia_rotator
+            nvidia_rotator=nvidia_rotator,
+            user_id=user_id,
+            context="chat"
         )
     except Exception as e:
         logger.warning(f"[CHAT] Web-augmented LLM error: {e}")

routes/reports.py

@@ -296,7 +296,7 @@ Create a detailed plan for this report."""
         logger.info(f"[REPORT] System prompt length: {len(sys_prompt)}")
         logger.info(f"[REPORT] User prompt length: {len(user_prompt)}")
         
-        response = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+        response = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_planning")
         
         # Parse JSON response
         import json
@@ -406,7 +406,7 @@ FILE SUMMARY: {file_summary[:500]}
 Create a simple plan for this report."""
 
             simple_selection = {"provider": "gemini", "model": "gemini-2.5-flash"}
-            simple_response = await generate_answer_with_model(simple_selection, simple_sys_prompt, simple_user_prompt, gemini_rotator, nvidia_rotator)
+            simple_response = await generate_answer_with_model(simple_selection, simple_sys_prompt, simple_user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_planning_simple")
             simple_json_text = simple_response.strip()
             
             if simple_json_text.startswith('```json'):
@@ -662,7 +662,7 @@ Perform the comprehensive analysis as specified, following all sub-actions, meet
 
     try:
         selection = {"provider": "gemini", "model": "gemini-2.5-flash"}
-        analysis = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+        analysis = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_analysis")
         return analysis.strip()
         
     except Exception as e:
@@ -746,7 +746,7 @@ Synthesize these analyses into a comprehensive, coherent section with proper hie
 
     try:
         selection = {"provider": "gemini", "model": "gemini-2.5-flash"}
-        synthesis = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+        synthesis = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_synthesis")
         return synthesis.strip()
         
     except Exception as e:
@@ -884,7 +884,7 @@ Create a comprehensive, authoritative report with proper hierarchical structure
     try:
         # Use Gemini Pro for final synthesis (better for long-form content)
         selection = {"provider": "gemini", "model": "gemini-2.5-pro"}
-        report = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+        report = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_final")
         
         # Post-process to remove any remaining meta-commentary and ensure proper formatting
         report = remove_meta_commentary(report)
@@ -935,7 +935,7 @@ async def generate_code_artifacts(subsection_id: str, task: str, reasoning: str,
         "Produce the code files and explanations as specified."
     )
     selection = {"provider": "gemini", "model": "gemini-2.5-pro"}
-    code_md = await generate_answer_with_model(selection, system_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+    code_md = await generate_answer_with_model(selection, system_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_coding")
     return code_md.strip()
 
 def should_generate_mermaid(instructions: str, report_text: str) -> bool:
@@ -970,7 +970,7 @@ async def generate_mermaid_diagram(instructions: str, detailed_analysis: Dict[st
 
         # Use NVIDIA_LARGE for diagram synthesis
         selection = {"provider": "nvidia_large", "model": os.getenv("NVIDIA_LARGE", "openai/gpt-oss-120b")}
-        diagram = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator)
+        diagram = await generate_answer_with_model(selection, sys_prompt, user_prompt, gemini_rotator, nvidia_rotator, user_id, "report_diagram")
         # Strip accidental code fences
         diagram = diagram.strip()
         if diagram.startswith("```"):
@@ -1272,7 +1272,7 @@ Return the renumbered headings in the format: "level: new_number: heading_text"
         
         # Use NVIDIA model for heading re-numbering
         selection = {"provider": "nvidia", "model": "meta/llama-3.1-8b-instruct"}
-        response = await generate_answer_with_model(selection, sys_prompt, user_prompt, None, nvidia_rotator)
+        response = await generate_answer_with_model(selection, sys_prompt, user_prompt, None, nvidia_rotator, user_id, "report_heading_fix")
         
         # Parse the AI response
         renumbered_headings = []

static/analytics.js

@@ -208,12 +208,14 @@
     sortedModels.forEach(model => {
       const percentage = totalUsage > 0 ? Math.round((model.count / totalUsage) * 100) : 0;
       const lastUsed = new Date(model.last_used * 1000).toLocaleDateString();
+      const modelName = model.model_name || model._id;
+      const provider = model.provider || 'unknown';
       
       html += `
         <div class="model-usage-item">
           <div class="model-info">
-            <div class="model-name">${model._id}</div>
-            <div class="model-provider">${model.provider}</div>
+            <div class="model-name">${modelName}</div>
+            <div class="model-provider">${provider}</div>
           </div>
           <div class="model-stats">
             <div class="model-count">${model.count} requests</div>
@@ -291,11 +293,11 @@
     let html = '<div class="daily-trends-chart">';
     sortedDaily.forEach(day => {
       const date = new Date(day._id.year, day._id.month - 1, day._id.day);
-      const dateStr = date.toLocaleDateString();
-      const height = maxUsage > 0 ? (day.total_requests / maxUsage) * 100 : 0;
+      const dateStr = date.toLocaleDateString('en-US', { month: 'short', day: 'numeric' });
+      const height = maxUsage > 0 ? Math.max(10, (day.total_requests / maxUsage) * 100) : 10;
       
       html += `
-        <div class="daily-bar">
+        <div class="daily-bar" title="${day.total_requests} requests on ${date.toLocaleDateString()}">
           <div class="daily-bar-fill" style="height: ${height}%"></div>
           <div class="daily-label">${dateStr}</div>
           <div class="daily-count">${day.total_requests}</div>

static/styles.css

@@ -2300,6 +2300,7 @@
   gap: 0.5rem;
   height: 200px;
   padding: 1rem 0;
+  border-bottom: 1px solid var(--border);
 }
 
 .daily-bar {
@@ -2309,28 +2310,43 @@
   align-items: center;
   gap: 0.5rem;
   min-height: 100px;
+  cursor: pointer;
+  transition: transform 0.2s ease;
+}
+
+.daily-bar:hover {
+  transform: translateY(-2px);
 }
 
 .daily-bar-fill {
   width: 100%;
   background: var(--gradient-accent);
-  border-radius: 2px 2px 0 0;
-  min-height: 4px;
-  transition: height 0.3s ease;
+  border-radius: 4px 4px 0 0;
+  min-height: 8px;
+  transition: all 0.3s ease;
+  position: relative;
+}
+
+.daily-bar:hover .daily-bar-fill {
+  background: var(--accent);
+  box-shadow: 0 2px 8px rgba(59, 130, 246, 0.3);
 }
 
 .daily-label {
   font-size: 0.75rem;
   color: var(--muted);
   text-align: center;
-  writing-mode: vertical-rl;
-  text-orientation: mixed;
+  white-space: nowrap;
+  transform: rotate(-45deg);
+  transform-origin: center;
+  margin-top: 0.5rem;
 }
 
 .daily-count {
   font-size: 0.75rem;
   color: var(--text-secondary);
-  font-weight: 500;
+  font-weight: 600;
+  margin-top: 0.25rem;
 }
 
 /* Usage Summary Styles */

utils/api/router.py

@@ -89,9 +89,25 @@ def select_model(question: str, context: str) -> Dict[str, Any]:
 
 
 async def generate_answer_with_model(selection: Dict[str, Any], system_prompt: str, user_prompt: str,
-                                     gemini_rotator: APIKeyRotator, nvidia_rotator: APIKeyRotator) -> str:
+                                     gemini_rotator: APIKeyRotator, nvidia_rotator: APIKeyRotator, 
+                                     user_id: str = None, context: str = "") -> str:
     provider = selection["provider"]
     model = selection["model"]
+    
+    # Track model usage for analytics
+    try:
+        from utils.analytics import get_analytics_tracker
+        tracker = get_analytics_tracker()
+        if tracker and user_id:
+            await tracker.track_model_usage(
+                user_id=user_id,
+                model_name=model,
+                provider=provider,
+                context=context or "api_call",
+                metadata={"system_prompt_length": len(system_prompt), "user_prompt_length": len(user_prompt)}
+            )
+    except Exception as e:
+        logger.debug(f"[ROUTER] Analytics tracking failed: {e}")
 
     if provider == "gemini":
         # Try Gemini first