Create app.py

app.py

@@ -0,0 +1,20 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+model_name = "Ploypatcha/my-model-upload"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+labels = ["happy", "love", "angry", "sadness", "fear", "trust", "disgust", "surprise", "anticipation", "optimism", "pessimism"]
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.sigmoid(outputs.logits).numpy()[0]
+    results = {labels[i]: float(np.round(probs[i], 3)) for i in range(len(labels))}
+    return results
+
+gr.Interface(fn=predict, inputs=gr.Textbox(label="Enter english comment"), outputs="label").launch()