Update app.py

app.py

@@ -1,6 +1,63 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-gr.load(
-   "models/microsoft/deberta-v3-small",
-   provider="hf-inference",
-).launch()
+# Define your actions
+ACTIONS = [
+    "move_towards_player",
+    "stand_still",
+    "follow_player",
+    "run_away",
+    "jump",
+    "mine_block",
+    "place_block",
+    "attack",
+    "use_item",
+    "chat_only"
+]
+
+# Load model and tokenizer (you'll fine-tune this on your specific task)
+model_name = "models/microsoft/deberta-v3-small"  # Starting point, you'll need to fine-tune
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(ACTIONS))
+
+# This function will need to be trained/fine-tuned with your data
+def predict_action(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+    
+    # Return both the action and the confidence score
+    return {
+        "action": ACTIONS[predicted_class],
+        "confidence": probabilities[0][predicted_class].item(),
+        "all_actions": {ACTIONS[i]: probabilities[0][i].item() for i in range(len(ACTIONS))}
+    }
+
+# Create the Gradio interface
+def process_text(text):
+    result = predict_action(text)
+    return result["action"], result["confidence"], result["all_actions"]
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Minecraft Action Predictor")
+    with gr.Row():
+        text_input = gr.Textbox(label="Character text")
+        action_output = gr.Textbox(label="Predicted action")
+    with gr.Row():
+        confidence_output = gr.Number(label="Confidence")
+        all_actions_output = gr.JSON(label="All action probabilities")
+    
+    text_input.change(process_text, inputs=text_input, outputs=[action_output, confidence_output, all_actions_output])
+    
+    gr.Interface(
+        fn=predict_action,
+        inputs=gr.Textbox(label="Character text"),
+        outputs=gr.JSON(label="Result"),
+        title="Minecraft Action Predictor API",
+        description="Predicts the best action based on character text"
+    )
+
+demo.launch()