Create use.py

use.py

@@ -0,0 +1,98 @@
+import torch
+from transformers import T5Config, T5ForConditionalGeneration
+import os
+
+# ============================================================
+# 1. SETUP & LOADING
+# ============================================================
+
+SAVE_PATH = "model.pt"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+if not os.path.exists(SAVE_PATH):
+    print(f"Error: File {SAVE_PATH} not found!")
+    exit()
+
+torch.serialization.add_safe_globals([T5Config])
+
+checkpoint = torch.load(SAVE_PATH, map_location=DEVICE, weights_only=True)
+
+char2id = checkpoint["char2id"]
+id2char = checkpoint["id2char"]
+PAD_ID = char2id["<pad>"]
+BOS_ID = char2id["<bos>"]
+EOS_ID = char2id["<eos>"]
+
+config = checkpoint["config"]
+model = T5ForConditionalGeneration(config).to(DEVICE)
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+print(f"Model loaded (Accuracy: {checkpoint['accuracy']:.2f}% from epoch {checkpoint['epoch']})")
+
+# ============================================================
+# 2. HELPER FUNCTIONS
+# ============================================================
+
+def encode(text, max_len=20):
+    tokens = []
+    for c in text:
+        tokens.append(char2id.get(c, PAD_ID))
+    tokens.append(EOS_ID)
+    # Padding
+    tokens = tokens[:max_len]
+    tokens += [PAD_ID] * (max_len - len(tokens))
+    return tokens
+
+def decode(token_ids):
+    result = []
+    for tid in token_ids:
+        if tid == EOS_ID: break
+        if tid in (PAD_ID, BOS_ID): continue
+        result.append(id2char.get(tid, "?"))
+    return "".join(result)
+
+def solve(expression):
+    if not expression.endswith("="):
+        expression += "="
+    
+    input_ids = torch.tensor([encode(expression)], dtype=torch.long).to(DEVICE)
+    attention_mask = (input_ids != PAD_ID).long()
+    
+    with torch.no_grad():
+        generated = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=12,
+            eos_token_id=EOS_ID,
+            pad_token_id=PAD_ID,
+            do_sample=False
+        )
+    
+    return decode(generated[0].cpu().tolist())
+
+# ============================================================
+# 3. INTERACTIVE MODE
+# ============================================================
+
+print("\n--- Mini Math Model interactive ---")
+print("Enter an arithmetic task (e.g. 15*15) or type 'exit' to quit this.")
+
+while True:
+    user_input = input("\nTask > ").strip().replace(" ", "")
+    if user_input.lower() in ("exit", "quit", "q"):
+        break
+    
+    if not any(op in user_input for op in "+-*/"):
+        print("Input an arithmetic task!")
+        continue
+        
+    prediction = solve(user_input)
+    
+    try:
+        calc_input = user_input.replace("/", "//")
+        true_val = str(eval(calc_input))
+        status = "✅" if prediction == true_val else "❌"
+        print(f"Model: {prediction} | Correct: {true_val} {status}")
+    except:
+        print(f"Model: {prediction}")