Delete app.py

app.py

@@ -1,96 +0,0 @@
-import os
-from fastai.vision.all import *
-import gradio as gr
-import pickle
-from transformers import AutoTokenizer, AutoModelWithLMHead
-
-
-
-# Facial expression classifier
-
-# Emotion
-learn_emotion = load_learner('emotions_vgg.pkl')
-learn_emotion_labels = learn_emotion.dls.vocab
-
-
-# Predict
-def predict(img):
-    img = PILImage.create(img)
-    pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(img)
-    predicted_emotion = learn_emotion_labels[pred_emotion_idx]
-    return predicted_emotion
-
-
-# Gradio
-title = "Facial Emotion Detector"
-
-description = gr.Markdown(
-                """Ever wondered what a person might be feeling looking at their picture?
-                 Well, now you can! Try this fun app. Just upload a facial image in JPG or
-                 PNG format. You can now see what they might have felt when the picture
-                 was taken.
-
-                 **Tip**: Be sure to only include face to get best results. Check some sample images
-                 below for inspiration!""").value
-
-article = gr.Markdown(
-             """**DISCLAIMER:** This model does not reveal the actual emotional state of a person. Use and
-             interpret results at your own risk!. 
-
-             **PREMISE:** The idea is to determine an overall emotion of a person
-             based on the pictures. We are restricting pictures to only include close-up facial
-             images.
-
-             **DATA:** FER2013 dataset consists of 48x48 pixel grayscale images of faces.Images
-             are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral.
-             
-              """).value
-
-enable_queue=True
-
-examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg']
-
-image_mode=gr.Interface(fn = predict, 
-             inputs = gr.Image( image_mode='L'), 
-             outputs = [gr.Label(label='Emotion')], #gr.Label(),
-             title = title,
-             examples = examples,
-             description = description,
-             article=article,
-             allow_flagging='never')
-
-
-
-
-# Txet Model
-
-# Load tokenizer and model from pickles
-with open("emotion_tokenizer.pkl", "rb") as f:
-    tokenizer = pickle.load(f)
-
-with open("emotion_model.pkl", "rb") as f:
-    model = pickle.load(f)
-
-
-
-def classify_emotion(text):
-    # Tokenize input text and generate output
-    input_ids = tokenizer.encode("emotion: " + text, return_tensors="pt")
-    output = model.generate(input_ids)
-    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    
-    # Classify the emotion into positive, negative, or neutral
-    if output_text in ["joy", "love"]:
-        return "Positive"
-    elif output_text == "surprise":
-        return "Neutral"
-    else:
-        return "Negative"
-    return output_text
-
-
-text_model = gr.Interface(fn=classify_emotion, inputs="textbox", outputs="textbox")
-
-
-main_model = gr.TabbedInterface([text_model, image_mode], ["Text Emotion Recognition", "Image Emotion Recognition"])
-main_model.launch()