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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline
import torch
app = FastAPI()
# Load the emotion analysis model
device = 0 if torch.cuda.is_available() else -1 # Use GPU if available
emotion_analyzer = pipeline("text-classification",
model="j-hartmann/emotion-english-distilroberta-base",
return_all_scores=True,
device=device)
# Define request model
class EmotionRequest(BaseModel):
questions: list[str]
answers: list[str]
@app.post("/analyze-emotions")
def analyze_emotions(data: EmotionRequest):
"""
Analyze emotions in answers using a multi-label emotion model.
"""
try:
questions = data.questions
answers = data.answers
results = []
for q, a in zip(questions, answers):
emotions = emotion_analyzer(a)[0]
result = {
"question": q,
"answer": a,
"emotions": {emotion['label']: round(emotion['score'], 4) for emotion in emotions},
"dominant_emotion": max(emotions, key=lambda x: x['score'])['label'],
"confidence": round(max(emotions, key=lambda x: x['score'])['score'], 4)
}
results.append(result)
return results
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
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