Update app.py

app.py

@@ -1,36 +1,37 @@
-# app.py
+# app.py - Fixed for recent Gradio versions (no allow_flagging)
+
 import torch
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 from peft import PeftModel
 
 # ────────────────────────────────────────────────────────────────
-#   Configuration - fastest practical settings
+#   Fastest practical configuration
 # ────────────────────────────────────────────────────────────────
 
 BASE_MODEL = "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"
 LORA_PATH  = "saadkhi/SQL_Chat_finetuned_model"
 
-MAX_NEW_TOKENS = 180          # ← keep reasonable
-TEMPERATURE    = 0.0          # greedy = fastest & most deterministic
-DO_SAMPLE      = False        # no sampling = faster
+MAX_NEW_TOKENS = 180
+TEMPERATURE    = 0.0          # greedy = fastest
+DO_SAMPLE      = False
 
 # ────────────────────────────────────────────────────────────────
-#   4-bit quantization config (this is the key speedup)
+#   4-bit quantization (very important for speed)
 # ────────────────────────────────────────────────────────────────
 
 bnb_config = BitsAndBytesConfig(
     load_in_4bit              = True,
-    bnb_4bit_quant_type       = "nf4",           # "nf4" usually fastest + good quality
-    bnb_4bit_use_double_quant = True,            # nested quantization → extra memory saving
-    bnb_4bit_compute_dtype    = torch.bfloat16   # fastest compute type on modern GPUs
+    bnb_4bit_quant_type       = "nf4",
+    bnb_4bit_use_double_quant = True,
+    bnb_4bit_compute_dtype    = torch.bfloat16
 )
 
 print("Loading quantized base model...")
 model = AutoModelForCausalLM.from_pretrained(
     BASE_MODEL,
     quantization_config = bnb_config,
-    device_map          = "auto",                # auto = best available (cuda > cpu)
+    device_map          = "auto",
     trust_remote_code   = True,
     torch_dtype         = torch.bfloat16
 )
@@ -38,71 +39,64 @@ model = AutoModelForCausalLM.from_pretrained(
 print("Loading LoRA adapters...")
 model = PeftModel.from_pretrained(model, LORA_PATH)
 
-# Important: merge LoRA weights into base (faster inference, less overhead)
+# Merge LoRA into base model → much faster inference
 model = model.merge_and_unload()
 
 tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
 
-# Optional: small speedup boost on supported hardware
-if torch.cuda.is_available():
-    try:
-        import torch.backends.cuda
-        torch.backends.cuda.enable_flash_sdp(True)  # flash scaled dot product
-    except:
-        pass
-
 model.eval()
 print("Model ready!")
 
 # ────────────────────────────────────────────────────────────────
 def generate_sql(prompt: str):
-    # Use proper chat template (Phi-3 expects it)
     messages = [{"role": "user", "content": prompt}]
     
     inputs = tokenizer.apply_chat_template(
         messages,
-        tokenize           = True,
-        add_generation_prompt = True,
-        return_tensors     = "pt"
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt"
     ).to(model.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,
+            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,
         )
 
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    # Clean output - try to get only assistant's answer
+    # Clean output
     if "<|assistant|>" in response:
         response = response.split("<|assistant|>", 1)[-1].strip()
-    response = response.split("<|end|>")[0].strip()
+    response = response.split("<|end|>")[0].strip() if "<|end|>" in response else response
 
     return response
 
 # ────────────────────────────────────────────────────────────────
+#   Gradio interface - modern style (no allow_flagging)
+# ─────────────���──────────────────────────────────────────────────
+
 demo = gr.Interface(
-    fn           = generate_sql,
-    inputs       = gr.Textbox(
-        label       = "Ask SQL related question",
-        placeholder = "Show me all employees with salary > 50000...",
-        lines       = 3
+    fn=generate_sql,
+    inputs=gr.Textbox(
+        label="Ask SQL related question",
+        placeholder="Show me all employees with salary > 50000...",
+        lines=3
     ),
-    outputs      = gr.Textbox(label="Generated SQL / Answer"),
-    title        = "SQL Chatbot - Fast Version",
-    description  = "Phi-3-mini 4bit quantized + LoRA",
-    examples     = [
+    outputs=gr.Textbox(label="Generated SQL / Answer"),
+    title="SQL Chatbot - Optimized",
+    description="Phi-3-mini 4bit + LoRA merged",
+    examples=[
         ["Find duplicate emails in users table"],
         ["Top 5 highest paid employees"],
         ["Count orders per customer last month"]
     ],
-    allow_flagging = "never"
+    # flag button is disabled by default in newer versions → no need for allow_flagging
 )
 
 if __name__ == "__main__":