Update app.py

app.py

@@ -1,3 +1,4 @@
+# Compatibility fix
 import huggingface_hub
 
 if not hasattr(huggingface_hub, "HfFolder"):
@@ -6,27 +7,114 @@ if not hasattr(huggingface_hub, "HfFolder"):
         def get_token():
             return huggingface_hub.get_token()
 
+        @staticmethod
+        def save_token(token):
+            return huggingface_hub.login(token=token)
+
+        @staticmethod
+        def delete_token():
+            try:
+                huggingface_hub.logout()
+            except Exception:
+                pass
+
     huggingface_hub.HfFolder = HfFolder
 
+
 import spaces
-import torch
 import gradio as gr
+import torch
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
+
+
+ADAPTER = "lsgz/lsgz-personality-clone"
+
+# Get base model from your LoRA config
+config = PeftConfig.from_pretrained(ADAPTER)
+BASE_MODEL = config.base_model_name_or_path
+
+print("Base model:", BASE_MODEL)
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+
+# -------------------------
+# LOAD MODEL ON CPU
+# -------------------------
+
+print("Loading base model...")
 
-print("Torch:", torch.__version__)
-print("CUDA patched:", torch.cuda.is_available())
-print("spaces:", spaces.__file__)
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+)
 
-@spaces.GPU
-def test_gpu():
-    return (
-        f"CUDA: {torch.cuda.is_available()}\n"
-        f"Device count: {torch.cuda.device_count()}\n"
-        f"Device: {torch.cuda.get_device_name(0)}"
+print("Loading LSGZ adapter...")
+
+model = PeftModel.from_pretrained(
+    base_model,
+    ADAPTER
+)
+
+model.eval()
+
+print("Model ready on CPU.")
+
+
+# -------------------------
+# GPU INFERENCE
+# -------------------------
+
+@spaces.GPU(duration=120)
+def respond(message, history):
+
+    print("GPU available:", torch.cuda.is_available())
+    print("GPU:", torch.cuda.get_device_name(0))
+
+    # GPU exists HERE
+    model.to("cuda")
+
+    inputs = tokenizer(
+        message,
+        return_tensors="pt"
+    ).to("cuda")
+
+    with torch.inference_mode():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    generated = outputs[0][inputs["input_ids"].shape[1]:]
+
+    response = tokenizer.decode(
+        generated,
+        skip_special_tokens=True
     )
 
-gr.Interface(
-    fn=test_gpu,
-    inputs=None,
-    outputs="text",
-    title="ZeroGPU Test"
-).launch()
+    return response.strip()
+
+
+# -------------------------
+# GRADIO
+# -------------------------
+
+demo = gr.ChatInterface(
+    fn=respond,
+    title="LSGZ Personality Clone",
+    description="Chat with LSGZ 💬",
+)
+
+demo.queue()
+demo.launch()