app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoImageProcessor, AutoModelForImageClassification
from torch.nn.functional import sigmoid
import torch
from PIL import Image



# Load text emotion model
model_name = "SamLowe/roberta-base-go_emotions"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Load image emotion model
image_model_name = "Celal11/resnet-50-finetuned-FER2013-0.001"
image_processor = AutoImageProcessor.from_pretrained(image_model_name)
image_model = AutoModelForImageClassification.from_pretrained(image_model_name)

# Analyze image emotion using processor and model
def analyze_image_emotion(image):
    if image is None:
        return "No image provided."
    inputs = image_processor(images=image, return_tensors="pt")

    with torch.no_grad():
        logits = image_model(**inputs).logits
    probs = torch.nn.functional.softmax(logits, dim=1)[0]
    pred_idx = torch.argmax(probs).item()
    label = image_model.config.id2label[pred_idx]
    score = probs[pred_idx].item()
    return f"{label} ({score:.2f})"

# Emotion label to icon mapping (subset)
emotion_icons = {
    inputs=[
        gr.Textbox(lines=5, placeholder="Write a sentence or a full paragraph...", label="Your Text"),
        gr.Slider(minimum=0.1, maximum=0.9, value=0.3, step=0.05, label="Threshold"),
        gr.Image(type="pil", label="Upload Face Photo")
    ],
    outputs=[
        gr.Textbox(label="Detected Text Emotions", elem_classes=["output-textbox"]),
    css=custom_css
)

demo.launch()