main.py

from fastapi import FastAPI
from pydantic import BaseModel
from typing import Dict

app = FastAPI(title="Severity Classifier")

# Models
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 functions
def compute_combined_score(issue: IssueData, alpha=1.0, beta=4.0):
    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"

@app.post("/classify-severity")
def classify_issues(payload: InputPayload):
    results = {}
    for issue_name, issue_data in payload.issues.items():
        score = compute_combined_score(issue_data)
        results[issue_name] = {
            "combined_score": score,
            "final_severity": classify_severity(score),
            "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
    }