emotion_detection.py

from transformers import AutoModelForImageClassification, AutoProcessor
from PIL import Image
import torch
import numpy as np
import os
import cv2
from textblob import TextBlob


model_name = "gerhardien/face-emotion"
# Load the model and processor
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoProcessor.from_pretrained(model_name)


def detect_emotion(image_path):
    
    img = cv2.imread(image_path)
    inputs = processor(images=img, return_tensors="pt")

    # Run inference
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = torch.argmax(logits, dim=1).item()
    label = model.config.id2label[predicted_class_idx]

    return label

def detect_sentiment(text):
    analysis = TextBlob(text)
    return "Positive" if analysis.sentiment.polarity > 0 else "Negative"