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| # import gradio as gr | |
| # from transformers import AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig | |
| # import torch | |
| # # Set device to CPU since GPU quantization is unavailable | |
| # device = 'cpu' | |
| # # Set up 8-bit quantization with BitsAndBytesConfig | |
| # quantization_config = BitsAndBytesConfig( | |
| # load_in_8bit=True, # Enable 8-bit quantization | |
| # llm_int8_enable_fp32_cpu_offload=True # Use CPU for 8-bit quantization operations | |
| # ) | |
| # # Load the model with quantization configuration | |
| # model_name = "Rahmat82/DistilBERT-finetuned-on-emotion" | |
| # model = AutoModelForSequenceClassification.from_pretrained( | |
| # model_name, | |
| # quantization_config=quantization_config, | |
| # device_map={"": device} # Ensures everything runs on CPU | |
| # ) | |
| # tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) | |
| # def predict(query: str) -> dict: | |
| # inputs = tokenizer(query, return_tensors='pt') | |
| # inputs = {k: v.to(device) for k, v in inputs.items()} # Ensure inputs are on CPU | |
| # outputs = model(**inputs) | |
| # outputs = torch.sigmoid(outputs.logits) | |
| # outputs = outputs.detach().cpu().numpy() | |
| # # Define label to ID mapping | |
| # label2ids = { | |
| # "sadness": 0, | |
| # "joy": 1, | |
| # "love": 2, | |
| # "anger": 3, | |
| # "fear": 4, | |
| # "surprise": 5, | |
| # } | |
| # for i, k in enumerate(label2ids.keys()): | |
| # label2ids[k] = outputs[0][i] | |
| # label2ids = {k: float(v) for k, v in sorted(label2ids.items(), key=lambda item: item[1], reverse=True)} | |
| # return label2ids | |
| # # Gradio interface setup | |
| # demo = gr.Interface( | |
| # theme=gr.themes.Soft(), | |
| # title="RHM Emotion Classifier 😊", | |
| # description="Beyond Words: Capturing the Essence of Emotion in Text<h3>On CPU with 8-bit quantization</h3>", | |
| # fn=predict, | |
| # inputs=gr.components.Textbox(label='Write your text here', lines=3), | |
| # outputs=gr.components.Label(label='Predictions', num_top_classes=6), | |
| # allow_flagging='never', | |
| # examples=[ | |
| # ["The gentle touch of your hand on mine is a silent promise that echoes through the corridors of my heart."], | |
| # ["The rain mirrored the tears I couldn't stop, each drop a tiny echo of the ache in my heart. The world seemed muted, colors drained, and a heavy weight settled upon my soul."], | |
| # ["Walking through the dusty attic, I stumbled upon a hidden door. With a mix of trepidation and excitement, I pushed it open, expecting cobwebs and forgotten junk. Instead, a flood of sunlight revealed a secret garden, blooming with vibrant flowers and buzzing with life. My jaw dropped in pure astonishment."], | |
| # ] | |
| # ) | |
| # demo.launch(share=True) | |
| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer | |
| from optimum.onnxruntime import ORTModelForSequenceClassification | |
| import torch | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| model_name = "Rahmat82/DistilBERT-finetuned-on-emotion" | |
| model = ORTModelForSequenceClassification.from_pretrained(model_name, export=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) | |
| model.to(device) | |
| def predict(query: str) -> dict: | |
| inputs = tokenizer(query, return_tensors='pt') | |
| inputs.to(device) | |
| outputs = model(**inputs) | |
| outputs = torch.sigmoid(outputs.logits) | |
| outputs = outputs.detach().cpu().numpy() | |
| label2ids = { | |
| "sadness": 0, | |
| "joy": 1, | |
| "love": 2, | |
| "anger": 3, | |
| "fear": 4, | |
| "surprise": 5, | |
| } | |
| for i, k in enumerate(label2ids.keys()): | |
| label2ids[k] = outputs[0][i] | |
| label2ids = {k: float(v) for k, v in sorted(label2ids.items(), key=lambda item: item[1], reverse=True)} | |
| return label2ids | |
| demo = gr.Interface( | |
| theme = gr.themes.Soft(), | |
| title = "RHM Emotion Classifier 😊", | |
| description = "Beyond Words: Capturing the Essence of Emotion in Text<h3>On GPU it is much faster 🚀</h3>", | |
| fn = predict, | |
| inputs = gr.components.Textbox(label='Write your text here', lines=3), | |
| outputs = gr.components.Label(label='Predictions', num_top_classes=6), | |
| allow_flagging = 'never', | |
| examples = [ | |
| ["The gentle touch of your hand on mine is a silent promise that echoes through the corridors of my heart."], | |
| ["The rain mirrored the tears I couldn't stop, each drop a tiny echo of the ache in my heart. The world seemed muted, colors drained, and a heavy weight settled upon my soul."], | |
| ["Walking through the dusty attic, I stumbled upon a hidden door. With a mix of trepidation and excitement, I pushed it open, expecting cobwebs and forgotten junk. Instead, a flood of sunlight revealed a secret garden, blooming with vibrant flowers and buzzing with life. My jaw dropped in pure astonishment."], | |
| ] | |
| ) | |
| demo.launch(share=True) |