Delete ml_tinygpt.py

ml_tinygpt.py

@@ -1,66 +0,0 @@
-import torch
-import torch.nn.functional as F
-from model import MiniGPT
-from dataset import MiniBPETokenizr,SimpleTokenizr
-import json
-import os
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load tokenizer
-tokenizer = SimpleTokenizr()
-tokenizer.load("./customchatbot-v1/trained-mini-gpt/tokenizer.json")
-
-# Load model
-model = MiniGPT(vocab_size=len(tokenizer))
-model.load_state_dict(torch.load("./customchatbot-v1/trained-mini-gpt/mini-gpt.pth", map_location=device) if os.path.exists("./customchatbot-v1/trained-mini-gpt/mini-gpt.pth") else torch.load("./customchatbot-v1/trained-mini-gpt/checkpoint-mini-gpt.pth", map_location=device)["model_state_dict"] )
-model.eval().to(device)
-totalparams = sum(p.numel() for p in model.parameters())
-print(f"Model total params: {totalparams:,}")
-
-def sample_token(logits, temperature=1.0):
-    logits = logits / temperature
-    logits = torch.nan_to_num(logits, nan=-1e9)
-    probs = F.softmax(logits, dim=-1)
-
-    if torch.any(torch.isnan(probs)) or torch.any(probs < 0):
-        print("⚠️ Invalid probs detected. Using uniform fallback.")
-        probs = torch.ones_like(probs) / probs.size(-1)
-
-    return torch.multinomial(probs, num_samples=1).item()
-
-def generate_reply(prompt, max_tokens=100):
-    tokens = tokenizer.encode(prompt)
-    if not tokens:
-        print("⚠️ Empty prompt after encoding.")
-        return
-    input_ids = torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(device)
-    generated = []
-
-    with torch.no_grad():
-        for _ in range(max_tokens):
-            logits = model(input_ids)
-            logits = logits[:, -1, :]
-            next_token = sample_token(logits)
-            generated.append(next_token)
-
-            next_str = tokenizer.itos.get(next_token, "")
-            encoded_text = tokenizer.encode(next_str)
-            decoded_text = tokenizer.decode(encoded_text)
-            print(decoded_text, end=" ", flush=True)
-
-            if next_str == "<END>":
-                break
-
-            input_ids = torch.cat([input_ids, torch.tensor([[next_token]]).to(device)], dim=1)
-    print()
-
-# Chat loop
-print("🧠 MiniGPT Chat (type 'exit' to quit')")
-while True:
-    user_input = input("User: ")
-    if user_input.lower() == "exit":
-        break
-    prompt = f"^User: {user_input}\nMiniGPT:"
-    print("MiniGPT: ", end="", flush=True)
-    generate_reply(prompt)