Moved severity classifier to utils and exposed endpoint

main.py

@@ -1,20 +1,15 @@
-# main.py
-
 from fastapi import FastAPI
 from pydantic import BaseModel
-from typing import List, Dict
+from typing import List
 from core.preprocess import preprocess_text_list
 from core.analyzer import analyze_feedback
 from utils.counter import aggregate_results
 
-# ✅ Import severity logic
-from severity_service.main import classify_issues, InputPayload
+# ✅ NEW import (moved from severity_service)
+from utils.severity_classifier import classify_issues, InputPayload
 
 app = FastAPI(title="Batch Sentiment + Severity Classifier")
 
-# -----------------------
-# Route 1: Analyze Batch
-# -----------------------
 class FeedbackBatch(BaseModel):
     texts: List[str]
 
@@ -25,9 +20,6 @@ def analyze_batch_endpoint(batch: FeedbackBatch):
     summary = aggregate_results(results)
     return summary
 
-# -----------------------
-# Route 2: Classify Severity
-# -----------------------
 @app.post("/classify-severity")
 def classify_severity_endpoint(payload: InputPayload):
     return classify_issues(payload)

utils/severity_classifier.py

@@ -0,0 +1,48 @@
+from pydantic import BaseModel
+from typing import Dict
+
+# Input schema
+class IssueData(BaseModel):
+    count: int
+    severity: int
+    impact_score: int
+
+class EmotionSummary(BaseModel):
+    positive_total: int
+    negative_total: int
+    overall_mood: str
+
+class InputPayload(BaseModel):
+    issues: Dict[str, IssueData]
+    positive_emotions: Dict[str, int]
+    negative_emotions: Dict[str, int]
+    emotion_summary: EmotionSummary
+
+# Scoring logic
+def compute_combined_score(issue: IssueData, alpha: float = 1.0, beta: float = 4.0) -> float:
+    return alpha * issue.impact_score + beta * issue.severity
+
+def classify_severity(score: float) -> str:
+    if score < 20:
+        return "low"
+    elif score < 40:
+        return "medium"
+    else:
+        return "high"
+
+def classify_issues(payload: InputPayload):
+    results = {}
+    for issue_name, issue_data in payload.issues.items():
+        combined_score = compute_combined_score(issue_data)
+        final_sev = classify_severity(combined_score)
+        results[issue_name] = {
+            "combined_score": combined_score,
+            "final_severity": final_sev,
+            "original_count": issue_data.count,
+            "original_impact_score": issue_data.impact_score,
+            "original_severity": issue_data.severity
+        }
+    return {
+        "classified_issues": results,
+        "overall_mood": payload.emotion_summary.overall_mood
+    }