app.py

import cv2
import gradio as gr
import numpy as np
from deepface import DeepFace

# Load the pre-trained emotion detection model
model = DeepFace.build_model("Emotion")

# Define emotion labels
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']

# Load face cascade classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

def predict_emotion(frame):
    # Convert frame to grayscale
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    for (x, y, w, h) in faces:
        # Extract the face ROI (Region of Interest)
        face_roi = gray_frame[y:y + h, x:x + w]

        # Resize the face ROI to match the input shape of the model
        resized_face = cv2.resize(face_roi, (48, 48), interpolation=cv2.INTER_AREA)

        # Normalize the resized face image
        normalized_face = resized_face / 255.0

        # Reshape the image to match the input shape of the model
        reshaped_face = normalized_face.reshape(1, 48, 48, 1)

        # Predict emotions using the pre-trained model
        preds = model.predict(reshaped_face)[0]
        emotion_idx = preds.argmax()
        emotion = emotion_labels[emotion_idx]

        # Draw rectangle around face and label with predicted emotion
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
        cv2.putText(frame, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

    return frame

# Gradio UI
iface = gr.Interface(fn=predict_emotion, inputs="webcam", outputs="image")
iface.launch()