updates

app.old

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def image_classifier(inp):
+    return {'cat': 0.3, 'dog': 0.7}
+
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+demo.launch()

app.py

@@ -1,7 +1,10 @@
 import gradio as gr
 
-def image_classifier(inp):
-    return {'cat': 0.3, 'dog': 0.7}
+def predict_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    outputs = model(**inputs)
+    prediction = outputs.logits.argmax(-1).item()
+    return dataset["train"].features["label"].int2str(prediction)
 
-demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
-demo.launch()
+interface = gr.Interface(fn=predict_emotion, inputs="text", outputs="label", title="Emotion Classifier")
+interface.launch(share=False)

train.py

@@ -0,0 +1,30 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+
+
+dataset = load_dataset("emotion")
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=128)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=6)
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=1,  # Reduce epochs for faster training
+    per_device_train_batch_size=8,  # Smaller batch size to fit CPU
+    logging_dir="./logs",
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"]
+)
+
+trainer.train()