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app.py

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+"""
+Stack X Ultimate — Hugging Face Space Inference
+================================================
+A free HF Space that serves our model 24/7 on T4 GPU.
+Works after training completes — auto-loads LoRA adapter + base model.
+
+Run on: https://huggingface.co/spaces/my-ai-stack/Stack-X-Ultimate-Inference
+"""
+
+import os
+import torch
+from typing import Optional
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# ─── Config ─────────────────────────────────────────────────────────────────
+BASE_MODEL = "Qwen/Qwen2.5-Coder-3B-Instruct"
+ADAPTER_REPO = "my-ai-stack/Stack-X-Ultimate"
+FALLBACK_ADAPTER = "my-ai-stack/Stack-4.0-Qwen-3B-Agentic"
+
+# ─── Model Loading ──────────────────────────────────────────────────────────
+
+def load_model():
+    """Load model with LoRA adapter."""
+    global model, tokenizer
+
+    print("Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "right"
+
+    print(f"Loading base: {BASE_MODEL}")
+    base = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True,
+    )
+
+    # Try to load adapter
+    try:
+        print(f"Loading adapter: {ADAPTER_REPO}")
+        model = PeftModel.from_pretrained(base, ADAPTER_REPO)
+        print(f"✅ Loaded {ADAPTER_REPO}")
+    except Exception as e1:
+        print(f"Failed to load {ADAPTER_REPO}: {e1}")
+        try:
+            print(f"Falling back to: {FALLBACK_ADAPTER}")
+            model = PeftModel.from_pretrained(base, FALLBACK_ADAPTER)
+            print(f"✅ Loaded {FALLBACK_ADAPTER}")
+        except Exception as e2:
+            print(f"Both adapters failed. Using base model. Error: {e2}")
+            model = base
+
+    model.eval()
+    total = sum(p.numel() for p in model.parameters()) / 1e9
+    print(f"Model ready: {total:.1f}B parameters")
+
+
+# Load at startup
+print("Initializing Stack X Ultimate Space...")
+try:
+    load_model()
+    STATUS = "✅ Model loaded"
+except Exception as e:
+    STATUS = f"⚠️  Load error: {e}"
+    model = None
+    tokenizer = None
+
+# ─── Inference Functions ─────────────────────────────────────────────────────
+
+def generate(prompt: str, max_tokens: int = 512, temperature: float = 0.7, top_p: float = 0.9):
+    """Generate text response."""
+    if model is None or tokenizer is None:
+        return "Model not loaded yet. Please try again in a moment."
+
+    if not prompt.strip():
+        return ""
+
+    try:
+        messages = [
+            {"role": "system", "content": "You are Stack X, a helpful AI coding assistant with tool-use capabilities."},
+            {"role": "user", "content": prompt},
+        ]
+        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+        with torch.no_grad():
+            out = model.generate(
+                **inputs,
+                max_new_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=temperature > 0,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                repetition_penalty=1.1,
+            )
+
+        response = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+        return response
+
+    except Exception as e:
+        return f"Error: {e}"
+
+
+def chat(messages: list, max_tokens: int = 512, temperature: float = 0.7):
+    """Chat with message history."""
+    if model is None or tokenizer is None:
+        return "Model not loaded yet."
+
+    if not messages:
+        return ""
+
+    try:
+        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+        with torch.no_grad():
+            out = model.generate(
+                **inputs,
+                max_new_tokens=max_tokens,
+                temperature=temperature,
+                do_sample=temperature > 0,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+            )
+
+        response = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+        return response
+
+    except Exception as e:
+        return f"Error: {e}"
+
+
+# ─── Gradio Interface ─────────────────────────────────────────────────────────
+
+with gr.Blocks(title="Stack X Ultimate", theme=gr.themes.Default()) as demo:
+    gr.Markdown("# 🚀 Stack X Ultimate Inference")
+    gr.Markdown(f"**Status:** {STATUS}")
+    gr.Markdown("Built on Qwen2.5-Coder-3B-Instruct + LoRA adapter trained on NVIDIA Nemotron + Stack-4.0 agentic data.")
+
+    with gr.Tab("Generate"):
+        prompt = gr.Textbox(label="Prompt", placeholder="Write a quicksort in Python...", lines=5)
+        with gr.Row():
+            max_tok = gr.Slider(32, 1024, value=512, step=32, label="Max tokens")
+            temp = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(0.5, 1.0, value=0.9, step=0.05, label="Top-p")
+        generate_btn = gr.Button("Generate", variant="primary")
+        output = gr.Textbox(label="Output", lines=10)
+        generate_btn.click(fn=generate, inputs=[prompt, max_tok, temp, top_p], outputs=output)
+
+    with gr.Tab("Chat"):
+        chatbot = gr.Chatbot(label="Conversation")
+        chat_msg = gr.Textbox(label="Your message", placeholder="Ask me anything...")
+        chat_clear = gr.Button("Clear")
+        chat_send = gr.Button("Send", variant="primary")
+
+        def user_msg(msg, history):
+            return "", history + [[msg, None]]
+
+        def bot_resp(history):
+            if not history:
+                return history
+            msgs = [{"role": "user" if i % 2 == 0 else "assistant", "content": c}
+                    for i, c in enumerate(sum(history, []))]
+            # Build proper format
+            formatted = []
+            for i, (role, content) in enumerate(zip(msgs[::2], msgs[1::2])):
+                formatted.append({"role": role["role"], "content": content["content"]})
+            response = chat(formatted, max_tokens=512, temperature=0.7)
+            history[-1][1] = response
+            return history
+
+        chat_msg.submit(user_msg, [chat_msg, chatbot], [chat_msg, chatbot], queue=False).then(
+            bot_resp, [chatbot], [chatbot]
+        )
+        chat_send.click(user_msg, [chat_msg, chatbot], [chat_msg, chatbot], queue=False).then(
+            bot_resp, [chatbot], [chatbot]
+        )
+        chat_clear.click(fn=None, inputs=None, outputs=chatbot)
+
+demo.launch(share=False)