removed onnx runtim

app.py

@@ -1,22 +1,23 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer
-from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
 
+# Check if GPU is available
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
+# Load the model with 8-bit precision
 model_name = "Rahmat82/DistilBERT-finetuned-on-emotion"
-model = ORTModelForSequenceClassification.from_pretrained(model_name, export=True)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, load_in_8bit=True, device_map="auto")
 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)
+    inputs = {k: v.to(device) for k, v in inputs.items()}  # Move inputs to the appropriate device
     outputs = model(**inputs)
     outputs = torch.sigmoid(outputs.logits)
     outputs = outputs.detach().cpu().numpy()
     
+    # Define label to ID mapping
     label2ids = {
                 "sadness": 0,
                 "joy": 1,
@@ -30,19 +31,71 @@ def predict(query: str) -> dict:
     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 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 = [
+    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)
+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)