Update app.py

app.py

@@ -1,33 +1,24 @@
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Define the model name correctly
-model_name = "willco-afk/my-model-name"
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("willco-afk/my-model-name")
+model = AutoModelForSequenceClassification.from_pretrained("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
+# Function to classify input text
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     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")
+        logits = model(**inputs).logits
+    predicted_class = logits.argmax().item()  # Get the predicted class
+    return f"Predicted class: {predicted_class}"
 
-# Create a Gradio interface for the model
-iface = gr.Interface(fn=predict_language, inputs="text", outputs="text", title="Slang Language Classifier")
+# Create a Gradio interface with customized layout
+demo = gr.Interface(fn=classify_text, 
+                    inputs=gr.Textbox(label="Enter your text"), 
+                    outputs=gr.Textbox(label="Prediction"),
+                    live=True)  # This option allows live feedback as you type
 
-# Launch the interface
-iface.launch()
+# Launch the Gradio interface
+demo.launch()