Update app.py

app.py

@@ -1,6 +1,6 @@
 """
-FomoFeed - Timing Optimizer AI
-Predicts optimal posting time based on user engagement patterns
+FomoFeed - Timing Optimizer AI v2
+WITH CLIP SCORE & VIDEO BOOST
 """
 
 from fastapi import FastAPI, HTTPException
@@ -10,7 +10,7 @@ from datetime import datetime, timedelta
 from collections import Counter
 import uvicorn
 
-app = FastAPI(title="FomoFeed Timing Optimizer", version="1.0.0")
+app = FastAPI(title="FomoFeed Timing Optimizer", version="2.0.0")
 
 class UserEngagementHistory(BaseModel):
     user_id: int
@@ -18,6 +18,8 @@ class UserEngagementHistory(BaseModel):
     engagement_weights: list[float]  # Corresponding weights (view=1, like=3, comment=5, save=7)
     content_type: str = "post"  # "post" or "moment"
     timezone_offset: int = 3  # Turkey = +3
+    clip_score: float = 0.0  # 🆕 CLIP visual quality score (0-10)
+    has_video: bool = False  # 🆕 Is this video content?
     
 class TimingRecommendation(BaseModel):
     optimal_hour: int  # 0-23
@@ -28,6 +30,7 @@ class TimingRecommendation(BaseModel):
 def calculate_optimal_time(history: UserEngagementHistory) -> dict:
     """
     Analyze user's engagement patterns and recommend best posting time
+    WITH CLIP SCORE & VIDEO BOOST
     """
     if not history.engagement_hours or not history.engagement_weights:
         # No data - return generic best times
@@ -37,7 +40,9 @@ def calculate_optimal_time(history: UserEngagementHistory) -> dict:
             "alternative_hours": [12, 13, 20, 21],
             "reasoning": {
                 "method": "default",
-                "note": "Using generic peak hours (no user data)"
+                "note": "Using generic peak hours (no user data)",
+                "clip_boost": False,
+                "video_boost": False
             }
         }
     
@@ -63,6 +68,35 @@ def calculate_optimal_time(history: UserEngagementHistory) -> dict:
         if hour in time_bonuses:
             hour_scores[hour] *= time_bonuses[hour]
     
+    # 🆕 CLIP SCORE BOOST
+    clip_boost_applied = False
+    if history.clip_score > 0:
+        # Yüksek kaliteli görseller prime-time'da daha iyi performans gösterir
+        prime_hours = [12, 13, 18, 19, 20, 21]
+        clip_multiplier = 1 + (history.clip_score / 10) * 0.3  # Max %30 boost
+        
+        for hour in prime_hours:
+            if hour in hour_scores:
+                hour_scores[hour] *= clip_multiplier
+                clip_boost_applied = True
+            else:
+                # Hiç veri yoksa CLIP skoruna göre başlangıç puanı ver
+                hour_scores[hour] = history.clip_score * 5 * time_bonuses.get(hour, 1.0)
+                clip_boost_applied = True
+    
+    # 🆕 VIDEO BOOST
+    video_boost_applied = False
+    if history.has_video:
+        # Videolar akşam saatlerinde daha iyi performans gösterir
+        evening_hours = [18, 19, 20, 21, 22]
+        for hour in evening_hours:
+            if hour in hour_scores:
+                hour_scores[hour] *= 1.2  # %20 video boost
+                video_boost_applied = True
+            else:
+                hour_scores[hour] = 50 * time_bonuses.get(hour, 1.0)
+                video_boost_applied = True
+    
     # Add neighboring hour influence (smooth distribution)
     smoothed_scores = {}
     for hour in range(24):
@@ -80,9 +114,17 @@ def calculate_optimal_time(history: UserEngagementHistory) -> dict:
     else:
         optimal_hour = 19  # Default
     
-    # Calculate confidence based on data quality
+    # Calculate confidence based on data quality + boosts
     total_engagements = len(history.engagement_hours)
-    confidence = min(0.95, 0.5 + (total_engagements / 200))  # Max at 100+ engagements
+    base_confidence = min(0.95, 0.5 + (total_engagements / 200))
+    
+    # Boost confidence if CLIP or video boosts were applied
+    if clip_boost_applied and history.clip_score >= 7:
+        base_confidence = min(0.95, base_confidence + 0.1)
+    if video_boost_applied:
+        base_confidence = min(0.95, base_confidence + 0.05)
+    
+    confidence = base_confidence
     
     # Get top 4 alternative hours
     sorted_hours = sorted(smoothed_scores.items(), key=lambda x: x[1], reverse=True)
@@ -90,11 +132,15 @@ def calculate_optimal_time(history: UserEngagementHistory) -> dict:
     
     # Reasoning
     reasoning = {
-        "method": "weighted_pattern_analysis",
+        "method": "weighted_pattern_analysis_v2",
         "total_engagements": total_engagements,
         "unique_hours": len(set(history.engagement_hours)),
         "peak_score": round(smoothed_scores[optimal_hour], 2),
-        "data_quality": "good" if total_engagements > 50 else "moderate" if total_engagements > 20 else "limited"
+        "data_quality": "good" if total_engagements > 50 else "moderate" if total_engagements > 20 else "limited",
+        "clip_boost": clip_boost_applied,
+        "clip_score": history.clip_score,
+        "video_boost": video_boost_applied,
+        "prime_time": optimal_hour in [12, 13, 18, 19, 20, 21]
     }
     
     return {
@@ -135,6 +181,14 @@ def calculate_next_optimal_times(history: UserEngagementHistory, count: int = 3)
             time_bonuses = {7: 60, 8: 65, 12: 80, 13: 80, 18: 85, 19: 95, 20: 95, 21: 85, 22: 70}
             score = time_bonuses.get(future_hour, 40)
         
+        # Boost if CLIP score is high and it's prime time
+        if history.clip_score >= 7 and future_hour in [12, 13, 18, 19, 20, 21]:
+            score = min(100, score + 10)
+        
+        # Boost if video and evening
+        if history.has_video and future_hour in [18, 19, 20, 21, 22]:
+            score = min(100, score + 5)
+        
         opportunities.append({
             "datetime": future_time.isoformat(),
             "hour": future_hour,
@@ -151,7 +205,8 @@ def root():
     return {
         "service": "FomoFeed Timing Optimizer",
         "status": "active",
-        "version": "1.0.0"
+        "version": "2.0.0",
+        "features": ["clip_boost", "video_boost", "prime_time_optimization"]
     }
 
 @app.get("/health")
@@ -162,6 +217,7 @@ def health():
 def predict_optimal_time(history: UserEngagementHistory):
     """
     Predict optimal posting time based on user's engagement history
+    WITH CLIP SCORE & VIDEO BOOST
     """
     try:
         result = calculate_optimal_time(history)