import numpy as np
from tensorflow.keras.preprocessing.image import img_to_array, load_img
import gradio as gr
import tensorflow as tf
from PIL import Image
import cv2
from tensorflow.keras.preprocessing import image

# Pipelining the model
#from transformers import AutoImageProcessor, AutoModelForImageClassification

#processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
#model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")

# Loading pre-trained model
model = tf.keras.models.load_model('ResNet50_Transfer_Learning.keras')

# Emotion labels dictionary
emotion_labels = {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}
index_to_emotion = {v: k for k, v in emotion_labels.items()}
index_to_emotion

def prepare_image(img_pil):
  """Preprocess the PIL image to fit model's input requirements."""
  # Convert the PIL image to a numpy array with the target size
  img = img_pil.resize((224, 224))
  img_array = img_to_array(img)
  img_array = np.expand_dims(img_array, axis=0)  # Convert single image to a batch.
  img_array /= 255.0  # Rescale pixel values to [0,1], as done during training
  return img_array


# Define the Gradio interface (assuming you have an index_to_emotion dictionary)
def predict_emotion(image):
  """Predict emotion from an uploaded image."""
  # Preprocess the image
  processed_image = prepare_image(image)
  # Make prediction using the model
  prediction = model.predict(processed_image)
  # Get the emotion label with the highest probability
  predicted_class = np.argmax(prediction, axis=1)
  predicted_emotion = index_to_emotion.get(predicted_class[0], "Unknown Emotion")
  return predicted_emotion

interface = gr.Interface(
  fn=predict_emotion,  # Your prediction function
  inputs=gr.Image(type="pil"),  # Input for uploading an image, directly compatible with PIL images
  outputs="text",  # Output as text displaying the predicted emotion
  title="Emotion Detection",
  description="Upload an pimage and see the predicted emotion."
)

# Launch the Gradio interface
interface.launch(share=True)