Delete inference.py with huggingface_hub

inference.py

@@ -1,128 +0,0 @@
-# ==============================================================================
-# 🚀 ViuTranslate — Interactive Translation CLI
-# ==============================================================================
-# Run: python inference.py
-# Type any sentence in English or Hindi to get real-time neural translation.
-# ==============================================================================
-
-import os
-import sys
-import torch
-from tokenizers import Tokenizer
-from huggingface_hub import hf_hub_download
-
-# UTF-8 encoding
-if hasattr(sys.stdout, "reconfigure"):
-    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
-
-cur_dir = os.path.dirname(os.path.abspath(__file__)) if "__file__" in locals() else os.getcwd()
-if cur_dir not in sys.path:
-    sys.path.insert(0, cur_dir)
-
-from model import ViuAI
-from config import ViuAIConfig
-
-REPO_ID = "ViuAI/ViuTranslate"
-EOT_ID = 64002
-
-def load_model():
-    print("=" * 75)
-    print(f"🚀 Initializing ViuTranslate-500M from {REPO_ID}...")
-    print("=" * 75)
-    
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"• Hardware Device: {device.type.upper()}")
-    
-    # Checkpoint candidates
-    ckpt_candidates = [
-        "viutranslate_final.pt",
-        "checkpoints/viutranslate_final.pt",
-        os.path.join(cur_dir, "viutranslate_final.pt")
-    ]
-    ckpt_path = None
-    for c in ckpt_candidates:
-        if os.path.exists(c):
-            ckpt_path = c
-            break
-            
-    if ckpt_path is None:
-        print(f"📥 Downloading viutranslate_final.pt from {REPO_ID}...")
-        ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="viutranslate_final.pt")
-        
-    tok_candidates = [
-        "tokenizer.json",
-        os.path.join(cur_dir, "tokenizer.json")
-    ]
-    tok_path = None
-    for tc in tok_candidates:
-        if os.path.exists(tc):
-            tok_path = tc
-            break
-            
-    if tok_path is None:
-        print(f"📥 Downloading tokenizer.json from {REPO_ID}...")
-        tok_path = hf_hub_download(repo_id=REPO_ID, filename="tokenizer.json")
-        
-    tokenizer = Tokenizer.from_file(tok_path)
-    
-    cfg = ViuAIConfig(vocab_size=64003, context_length=2048)
-    model = ViuAI(cfg).to(device)
-    
-    state = torch.load(ckpt_path, map_location=device, weights_only=False)
-    weights = state.get("model_state_dict", state)
-    model.load_state_dict(weights, strict=False)
-    model.eval()
-    
-    print("✅ ViuTranslate Engine loaded and ready for inference!\n")
-    return model, tokenizer, device
-
-@torch.no_grad()
-def translate(model, tokenizer, device, text: str, mode: str = "direct") -> str:
-    text = text.strip()
-    if mode == "direct":
-        prompt = f"<|user|>\n{text}<|endofturn|>\n<|assistant|>\n"
-    elif mode == "to_hi":
-        prompt = f"<|user|>\nTranslate to Hindi: '{text}'<|endofturn|>\n<|assistant|>\n"
-    elif mode == "to_en":
-        prompt = f"<|user|>\nTranslate to English: '{text}'<|endofturn|>\n<|assistant|>\n"
-    else:
-        prompt = f"<|user|>\n{text}<|endofturn|>\n<|assistant|>\n"
-
-    input_ids = torch.tensor([tokenizer.encode(prompt).ids], dtype=torch.long, device=device)
-    prompt_len = input_ids.shape[1]
-    
-    out = model.generate(
-        input_ids,
-        max_new_tokens=150,
-        temperature=0.2,
-        top_p=0.9,
-        repetition_penalty=1.15,
-        eos_token_id=EOT_ID
-    )
-    gen_tokens = out[0][prompt_len:].tolist()
-    if EOT_ID in gen_tokens:
-        gen_tokens = gen_tokens[:gen_tokens.index(EOT_ID)]
-        
-    return tokenizer.decode(gen_tokens).strip()
-
-def interactive_loop():
-    model, tokenizer, device = load_model()
-    print("💡 Enter text to translate (Google Translate style). Type 'exit' or 'quit' to stop.\n")
-    
-    while True:
-        try:
-            inp = input("📝 [Input]: ").strip()
-            if not inp:
-                continue
-            if inp.lower() in ["exit", "quit", "q"]:
-                print("👋 Exiting ViuTranslate.")
-                break
-                
-            out = translate(model, tokenizer, device, inp, mode="direct")
-            print(f"🌐 [ViuTranslate]: {out}\n")
-        except (KeyboardInterrupt, EOFError):
-            print("\n👋 Exiting ViuTranslate.")
-            break
-
-if __name__ == "__main__":
-    interactive_loop()