File size: 2,643 Bytes
156a467 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import os
os.environ["KERAS_BACKEND"] = "jax"
import gradio as gr
import keras
import numpy as np
import cv2
from PIL import Image
import io
from huggingface_hub import hf_hub_download
# Load model and set desired emotions
model = None
desired_emotions = ['happy', 'sad', 'neutral']
original_emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
desired_indices = [original_emotion_labels.index(emotion) for emotion in desired_emotions]
# Load model from Hugging Face Hub
def load_emotion_model():
global model
try:
print("🔄 Downloading model from HuggingFace Hub...")
model_path = hf_hub_download(repo_id="Shees7/facial_model", filename="emotion_model.keras")
print("✅ Model file downloaded at:", model_path)
model = keras.saving.load_model(model_path)
print("✅ Model loaded successfully.")
except Exception as e:
print("❌ Failed to load model:", str(e))
# Call model loader on startup
load_emotion_model()
# Preprocess uploaded image to extract and prepare face
def preprocess_face(image):
try:
np_img = np.array(image.convert('RGB'))
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(np_img, cv2.COLOR_RGB2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
if len(faces) == 0:
return None
x, y, w, h = faces[0]
face = np_img[y:y+h, x:x+w]
face_resized = cv2.resize(face, (224, 224))
face_normalized = face_resized / 255.0
face_expanded = np.expand_dims(face_normalized, axis=0)
return face_expanded
except Exception as e:
print("❌ Error during preprocessing:", str(e))
return None
# Inference function for Gradio
def predict_emotion(image):
if model is None:
return "Model not loaded."
processed_face = preprocess_face(image)
if processed_face is None:
return "neutral"
predictions = model.predict(processed_face)[0]
filtered_predictions = [predictions[i] for i in desired_indices]
predicted_index = np.argmax(filtered_predictions)
predicted_emotion = desired_emotions[predicted_index]
return predicted_emotion
# Gradio UI
iface = gr.Interface(
fn=predict_emotion,
inputs=gr.Image(type="pil"),
outputs=gr.Text(label="Predicted Emotion"),
title="Facial Emotion Recognition",
description="Upload an image with a visible face. The model predicts one of: happy, sad, or neutral."
)
# Launch for Hugging Face Spaces
if __name__ == "__main__":
iface.launch()
|