add app

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load from local checkpoint
+# or whatever your checkpoint number is
+model_id = "/Users/kennyho/Dev/k-code-experiments/political-bert-classifier/src/results/checkpoint-2391"
+tokenizer = AutoTokenizer.from_pretrained(
+    'bert-base-uncased')  # Original tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+
+def predict(text):
+    # Tokenize and predict
+    inputs = tokenizer(text,
+                       truncation=True,
+                       padding=True,
+                       max_length=64,
+                       return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        prediction = probs.argmax(-1).item()
+        confidence = probs[0][prediction].item()
+        return probs
+
+    label_map = {0: 'Left', 1: 'Right', 2: 'Centrist'}
+    return f"{label_map[prediction]} (Confidence: {confidence:.2%})"
+
+
+# Create the interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=4, placeholder="Enter text to analyze..."),
+    outputs="text",
+    title="Political Text Classifier",
+    description="Classify political text as Left, Right, or Centrist"
+)
+
+demo.launch()