Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Replace this with your actual model name
+model_name = "willco-afk/my-model-name"
+
+# Load the model and tokenizer from Hugging Face Hub
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Function to predict language from input text
+def predict_language(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    # Get predictions from the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Get the logits (raw predictions) and apply softmax to get probabilities
+    logits = outputs.logits
+    predicted_class = logits.argmax(dim=-1).item()
+    
+    # Return the predicted class label (you can map this to your language labels)
+    label_map = {0: "english", 1: "spanish", 2: "tagalog"}
+    return label_map.get(predicted_class, "Unknown")
+
+# Create a Gradio interface for the model
+iface = gr.Interface(fn=predict_language, inputs="text", outputs="text", title="Slang Language Classifier")
+
+# Launch the interface
+iface.launch()