Create app.py

app.py

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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)