Update app.py

app.py

@@ -1,87 +1,53 @@
-# app.py - Optimized for Hugging Face Spaces (Unsloth = 2-4x faster)
+# app.py - ZeroGPU compatible version (NO Unsloth)
 
-import torch
 import gradio as gr
-from unsloth import FastLanguageModel
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+from huggingface_hub import spaces   # ← important!
 
-# ────────────────────────────────────────────────────────────────
+# Your model paths
 BASE_MODEL = "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"
-LORA_PATH  = "saadkhi/SQL_Chat_finetuned_model"
-
-MAX_NEW_TOKENS = 180
-TEMPERATURE    = 0.0   # Greedy = fastest & deterministic
-# ────────────────────────────────────────────────────────────────
-
-print("Loading base model with Unsloth (4-bit)...")
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = BASE_MODEL,
-    max_seq_length = 2048,
-    dtype = None,               # Auto: bfloat16 on GPU
-    load_in_4bit = True,        # Already quantized base
+LORA_PATH = "saadkhi/SQL_Chat_finetuned_model"
+
+print("Loading model on CPU first... (will use GPU only during @spaces.GPU)")
+bnb_config = BitsAndBytesConfig(load_in_4bit=True)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
 )
+model = PeftModel.from_pretrained(model, LORA_PATH)
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+model.eval()
 
-print("Applying your LoRA adapter...")
-model = FastLanguageModel.get_peft_model(
-    model,
-    r = 64,                     # Match your original rank
-    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj"],
-    lora_alpha = 128,
-    lora_dropout = 0,
-    bias = "none",
-    use_gradient_checkpointing = "unsloth",
-)
-
-# Enable 2x faster inference kernels
-FastLanguageModel.for_inference(model)
-
-print("Model ready! (very fast now)")
-
-# ────────────────────────────────────────────────────────────────
+@spaces.GPU   # ← this requests GPU slice only during this function
 def generate_sql(prompt: str):
     messages = [{"role": "user", "content": prompt}]
+    inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
     
-    inputs = tokenizer.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt"
-    ).to("cuda" if torch.cuda.is_available() else "cpu")
-
-    outputs = model.generate(
-        input_ids = inputs,
-        max_new_tokens = MAX_NEW_TOKENS,
-        temperature = TEMPERATURE,
-        do_sample = (TEMPERATURE > 0.01),
-        use_cache = True,
-        pad_token_id = tokenizer.eos_token_id,
-    )
-
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    with torch.inference_mode():
+        outputs = model.generate(
+            inputs,
+            max_new_tokens=180,
+            temperature=0.0,
+            do_sample=False,
+            use_cache=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
     
-    # Extract only assistant response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     if "<|assistant|>" in response:
         response = response.split("<|assistant|>", 1)[-1].strip()
-    response = response.split("<|end|>")[0].strip()
-
-    return response
+    return response.split("<|end|>")[0].strip()
 
-# ────────────────────────────────────────────────────────────────
 demo = gr.Interface(
-    fn = generate_sql,
-    inputs = gr.Textbox(
-        label = "Ask SQL question",
-        placeholder = "Delete duplicate rows from users table based on email",
-        lines = 3
-    ),
-    outputs = gr.Textbox(label="Generated SQL"),
-    title = "SQL Chatbot - Ultra Fast (Unsloth)",
-    description = "Phi-3-mini 4-bit + your LoRA",
-    examples = [
-        ["Find duplicate emails in users table"],
-        ["Top 5 highest paid employees"],
-        ["Count orders per customer last month"]
-    ]
+    fn=generate_sql,
+    inputs=gr.Textbox(label="Your SQL question"),
+    outputs="text",
+    title="SQL Chatbot (ZeroGPU)",
+    description="Free but limited daily GPU time"
 )
 
 if __name__ == "__main__":