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app.py

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+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_vgg19.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"])
+
+if _name_ == "_main_":
+ main_model.launch()

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "vocab_size": 32128
+}

emotion_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ff32f8d430c9c257a178738b50a63a84c598bf71c3bbac9a7e46bc9107f7ea0
+size 891778983

emotion_tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:231514df0ebafbdc7fb7e32858143ae29726155eb11c661234a08fd42068b596
+size 1393080

special_tokens_map.json

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+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}

spiece.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
+size 791656

tokenizer_config.json

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+{"model_max_length": 512}