fix: re-pushing dynamic config updates

api/main.py

@@ -44,7 +44,7 @@ MODELS_DIR = os.path.join(ROOT_DIR, 'models')
 
 # ✅ FIX: Swapped to a smaller, memory-friendly model to avoid crashing on free tier
 MODEL_REPO = "TheBloke/phi-2-GGUF"
-MODEL_FILENAME = "phi-2.Q2_K.gguf" # <-- This is a very small, low-quality version that fits in memory
+MODEL_FILENAME = "phi-2.Q2_K.gguf" 
 
 MODEL_SAVE_DIRECTORY = os.path.join(os.environ.get("WRITABLE_DIR", "/data"), "llm_model")
 LLAMA_MODEL_PATH = os.path.join(MODEL_SAVE_DIRECTORY, MODEL_FILENAME)
@@ -71,7 +71,28 @@ _performance_scorer = None
 def to_snake(name: str) -> str:
     return re.sub(r'(?<!^)(?=[A-Z])', '_', name).lower()
 
-# --- Pydantic Models (Single Source of Truth) ---
+# ==============================================================
+# 🎯 FIX 1: DEFINE NESTED CLASSES FIRST
+# These MUST come before they are used in ForecastResponse.
+# ==============================================================
+
+class PerformanceForecast(BaseModel):
+    predicted_engagement_rate: float
+    predicted_reach: int
+
+class PayoutForecast(BaseModel):
+    estimated_earning: float
+
+class RequestConfig(BaseModel):
+    model_name: Optional[str] = "phi-2"
+    temperature: Optional[float] = 0.7
+    system_prompt: Optional[str] = None
+
+class DirectPromptPayload(BaseModel):
+    prompt: str
+    config: Optional[RequestConfig] = None 
+
+# --- Other Pydantic Models ---
 
 class CreativeChatRequest(BaseModel): message: str; history: list; task_context: str
 class FinalizeScriptRequest(BaseModel): history: list; task_context: str
@@ -128,11 +149,15 @@ class CaptionAssistResponse(BaseModel): new_text: str
 class ForecastRequest(BaseModel):
     budget: float; category: str; follower_count: int; engagement_rate: float
     config: Optional[Dict[str, str]] = None
-class PerformanceForecast(BaseModel): predicted_engagement_rate: float; predicted_reach: int
-class PayoutForecastInput(BaseModel):
-    total_budget_active_campaigns: float
-    config: Optional[Dict[str, str]] = None
-class ForecastResponse(BaseModel): performance: PerformanceForecast; payout: PayoutForecast
+
+# ==========================================================
+# 🎯 FIX 2: NOW DEFINE ForecastResponse
+# PerformanceForecast and PayoutForecast are already defined above.
+# ==========================================================
+class ForecastResponse(BaseModel):
+    performance: PerformanceForecast
+    payout: PayoutForecast
+
 class InfluencerKpiData(BaseModel): totalReach: int; totalLikes: int; totalComments: int; avgEngagementRate: float; totalSubmissions: int
 class InfluencerAnalyticsSummaryResponse(BaseModel): summary: str
 class PortfolioOption(BaseModel): id: str; contentUrl: str; caption: Optional[str] = ""; likes: Optional[int] = 0; campaign: dict
@@ -171,25 +196,6 @@ class WeeklyPlanContext(BaseModel): niche: str; current_mood: str; recent_achiev
 class WeeklyPlanRequest(BaseModel): context: WeeklyPlanContext
 class PlanOption(BaseModel): type: str; title: str; platform: str; contentType: str; instructions: str; reasoning: str
 class WeeklyPlanResponse(BaseModel): options: List[PlanOption]
-class RequestConfig(BaseModel):
-    model_name: Optional[str] = "phi-2"
-    temperature: Optional[float] = 0.7
-    system_prompt: Optional[str] = None
-
-class DirectPromptPayload(BaseModel):
-    prompt: str
-    config: Optional[RequestConfig] = None 
-
-class PerformanceForecast(BaseModel):
-    predicted_engagement_rate: float
-    predicted_reach: int
-
-class PayoutForecast(BaseModel):
-    estimated_earning: float
-
-class ForecastResponse(BaseModel):
-    performance: PerformanceForecast
-    payout: PayoutForecast
 
 # --- FastAPI App ---
 app = FastAPI(title="Reachify AI Service (Deploy-Ready)", version="11.0.0")
@@ -705,7 +711,7 @@ def predict_payout(data: PayoutForecastInput):
     return {"forecastedAmount": max(0, pred), "commentary": "Based on budget trends."}
 
 
-@app.post("/analyze/content_quality", response_model=ContentQualityResponse, summary="Analyzes a caption for a quality score")
+@app.post("/analyze/content-quality", response_model=ContentQualityResponse, summary="Analyzes a caption for a quality score")
 def analyze_content_quality(request: ContentQualityRequest):
     """
     Uses the loaded LLM to analyze a social media caption based on several criteria
@@ -760,8 +766,14 @@ Respond ONLY with a valid JSON object in the following format:
         import json
         analysis_result = json.loads(clean_json_text)
         
+        final_result = {
+            "overall_score": analysis_result_raw.get("overall_score"),
+            "feedback": analysis_result_raw.get("feedback"),
+            "scores": analysis_result_raw.get("scores") or analysis_result_raw.get("score")
+        }
+
         print("--- Successfully received and parsed JSON response from LLM.")
-        return ContentQualityResponse(**analysis_result)
+        return ContentQualityResponse(**final_result)
 
     except (json.JSONDecodeError, KeyError, ValueError) as e:
         print(f"🚨 ERROR parsing LLM response: {e}. Raw response was: {json_text}")
@@ -1234,9 +1246,10 @@ async def rank_by_similarity_endpoint(request: RankBySimilarityRequest):
 @app.post("/analyze/content-quality", response_model=ContentQualityResponse, summary="Analyzes a caption for a quality score")
 def analyze_content_quality(request: ContentQualityRequest):
     """
-    Uses the loaded LLM to analyze a social media caption and returns a robustly parsed response.
+    Uses the loaded LLM to analyze a social media caption based on several criteria
+    and returns a quantitative score and qualitative feedback.
     """
-    print(f"\n✅ Received request on /analyze/content-quality")
+    print(f"\n✅ Received request on /analyze/content_quality")
     if not _llm_instance:
         raise HTTPException(status_code=503, detail="The Llama model is not available.")
 
@@ -1270,12 +1283,14 @@ You are a social media expert. Analyze the following caption... Respond ONLY wit
         clean_json_text = json_text[start_index:end_index]
         
         import json
-        analysis_result_raw = json.loads(clean_json_text)
+        
+        # ✅ FIX: Using consistent variable name 'analysis_result' everywhere
+        analysis_result = json.loads(clean_json_text)
         
         final_result = {
-            "overall_score": analysis_result_raw.get("overall_score"),
-            "feedback": analysis_result_raw.get("feedback"),
-            "scores": analysis_result_raw.get("scores") or analysis_result_raw.get("score")
+            "overall_score": analysis_result.get("overall_score"),
+            "feedback": analysis_result.get("feedback"),
+            "scores": analysis_result.get("scores") or analysis_result.get("score")
         }
 
         print("--- Successfully received and parsed JSON response from LLM.")
@@ -1648,4 +1663,4 @@ def create_campaign_from_prompt_endpoint(payload: DirectPromptPayload):
         )
         return {"response": response_text}
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))        
+        raise HTTPException(status_code=500, detail=str(e))