Update app.py

app.py

@@ -3,8 +3,26 @@ import torch
 import torch.nn as nn
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
+# --- ToyModel placeholder ---
+# Replace this with your actual ToyModel import if it exists
+class ToyModel:
+    def __init__(self, model, inputs, dynamic_shapes=None):
+        self.model = model
+        self.inputs = inputs
+        self.dynamic_shapes = dynamic_shapes
+
+    def predict(self, input_ids, attention_mask):
+        with torch.no_grad():
+            logits = self.model(input_ids=input_ids, attention_mask=attention_mask)
+        return logits
+
+    def evaluation(self):
+        print("Evaluation complete.")
+
+# ----------------------------
+
 def main():
-    # Get Hugging Face token from env
+    # Hugging Face token from environment
     auth_token = os.environ.get("HF_TOKEN")
     if not auth_token:
         raise ValueError("Please set HF_TOKEN environment variable")
@@ -39,12 +57,22 @@ def main():
     input_ids = tokens["input_ids"].to(device)
     attention_mask = tokens["attention_mask"].to(device)
 
-    # Forward pass
-    with torch.no_grad():
-        logits = model(input_ids, attention_mask)
+    # Dynamic shapes (optional, can be used by ToyModel)
+    batch_dim = 1
+    seq_dim = input_ids.shape[1]
+    dynamic_shapes = {
+        "input_ids": {0: batch_dim, 1: seq_dim},
+        "attention_mask": {0: batch_dim, 1: seq_dim},
+    }
+
+    # --- ToyModel usage ---
+    ir = ToyModel(model, (input_ids, attention_mask), dynamic_shapes=dynamic_shapes)
+    io_data = ir.predict(input_ids, attention_mask)
+    ir.evaluation()
 
-    print("Logits shape:", logits.shape)
-    print("Sample logits:", logits[0, :5, :5])
+    # Output
+    print("Predicted logits shape:", io_data.shape)
+    print("Sample logits:", io_data[0, :5, :5])
 
 if __name__ == "__main__":
     main()