Update app.py

app.py

@@ -1,11 +1,11 @@
-# app.py
+# app.py - CPU SAFE VERSION (No CUDA, No GPU)
+
 import torch
 import gradio as gr
-import spaces
 from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 from peft import PeftModel
 
-# ────────────────────────────────────────────────────────────────
+# ─────────────────────────────────────────────
 BASE_MODEL = "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"
 LORA_PATH = "saadkhi/SQL_Chat_finetuned_model"
 
@@ -13,48 +13,37 @@ MAX_NEW_TOKENS = 180
 TEMPERATURE = 0.0
 DO_SAMPLE = False
 
-print("Loading quantized base model on CPU...")
-print("(GPU will be used only during inference if available)")
+print("Loading model on CPU...")
 
-# 4-bit quantization config
+# 4-bit config (works on CPU but slower)
 bnb_config = BitsAndBytesConfig(
     load_in_4bit=True,
     bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16,
-    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
 )
 
-# Load base model → always on CPU first
+# Load base model on CPU
 model = AutoModelForCausalLM.from_pretrained(
     BASE_MODEL,
     quantization_config=bnb_config,
     device_map="cpu",
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
 )
 
-print("Loading LoRA adapters...")
+print("Loading LoRA...")
 model = PeftModel.from_pretrained(model, LORA_PATH)
 
-# Merge for faster inference (very recommended)
-print("Merging LoRA into base model...")
+# Merge LoRA for simpler inference
 model = model.merge_and_unload()
 
-# Load tokenizer
 tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-tokenizer.pad_token = tokenizer.eos_token
-
 model.eval()
-# ────────────────────────────────────────────────────────────────
 
-@spaces.GPU(duration=60, max_requests=20)  # safe values for ZeroGPU
+# ─────────────────────────────────────────────
 def generate_sql(prompt: str):
-    # Prepare chat format
-    messages = [
-        {"role": "user", "content": prompt}
-    ]
+    messages = [{"role": "user", "content": prompt}]
 
-    # Tokenize on CPU (safe everywhere)
+    # Tokenize (CPU)
     inputs = tokenizer.apply_chat_template(
         messages,
         tokenize=True,
@@ -62,59 +51,44 @@ def generate_sql(prompt: str):
         return_tensors="pt"
     )
 
-    # Choose device dynamically - this is the ZeroGPU-safe way
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"→ Running inference on device: {device}")
-
-    inputs = inputs.to(device)
-
     with torch.inference_mode():
         outputs = model.generate(
             input_ids=inputs,
             max_new_tokens=MAX_NEW_TOKENS,
             temperature=TEMPERATURE,
             do_sample=DO_SAMPLE,
+            use_cache=True,
             pad_token_id=tokenizer.eos_token_id,
-            eos_token_id=tokenizer.eos_token_id,
         )
 
-    # Decode and clean output
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    # Remove user's prompt + assistant tag if present
+    # Cleanup
     if "<|assistant|>" in response:
         response = response.split("<|assistant|>", 1)[-1].strip()
-
-    # Cut at end token if exists
     if "<|end|>" in response:
-        response = response.split("<|end|>", 1)[0].strip()
-
-    return response.strip()
+        response = response.split("<|end|>")[0].strip()
 
+    return response
 
-# ────────────────────────────────────────────────────────────────
+# ─────────────────────────────────────────────
 demo = gr.Interface(
     fn=generate_sql,
     inputs=gr.Textbox(
-        label="Ask a question about SQL",
+        label="Ask SQL question",
         placeholder="Delete duplicate rows from users table based on email",
-        lines=3,
-    ),
-    outputs=gr.Textbox(label="Generated SQL Query"),
-    title="SQL Chatbot – Phi-3-mini + LoRA",
-    description=(
-        "Fine-tuned Phi-3-mini-4k-instruct (4bit) for generating SQL queries\n\n"
-        "Works on ZeroGPU and regular GPU hardware"
+        lines=3
     ),
+    outputs=gr.Textbox(label="Generated SQL"),
+    title="SQL Chatbot (CPU Mode)",
+    description="Phi-3-mini 4bit + LoRA (CPU only, slower inference)",
     examples=[
         ["Find duplicate emails in users table"],
         ["Top 5 highest paid employees"],
-        ["Count orders per customer last month"],
-        ["Show all products that haven't been ordered in the last 6 months"],
-        ["Update all orders from 2024 to status 'completed'"],
+        ["Count orders per customer last month"]
     ],
-    cache_examples=False,
+    cache_examples=False
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()