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README.md

@@ -1,14 +1,40 @@
 ---
 title: AI Code Reviewer
-emoji: 🦀
-colorFrom: indigo
-colorTo: indigo
+colorFrom: gray
+colorTo: red
 sdk: gradio
-sdk_version: 6.26.0
-python_version: '3.12'
+sdk_version: 6.3.0
 app_file: app.py
 pinned: false
-short_description: AI Code Reviewer
+license: apache-2.0
+short_description: Paste code to get AI-powered code review.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Code Reviewer
+
+Paste a code snippet and get a short review covering bugs, edge cases,
+complexity, and style.
+
+Powered by `Qwen/Qwen2.5-Coder-0.5B-Instruct`.
+
+The model runs on the GPU configured for the Hugging Face Space using
+`@spaces.GPU`.
+
+## Features
+
+- Bug detection
+- Edge-case identification
+- Complexity feedback
+- Code style suggestions
+- Concise AI-generated review
+- GPU-accelerated inference
+
+## Stack
+
+Gradio · Transformers · PyTorch · Hugging Face Spaces
+
+## Note
+
+The 0.5B model favors speed over depth. It works best for short and
+medium-sized code snippets and may miss subtle issues in large or complex
+projects.

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_NAME = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
+
+# Load tokenizer and model once at startup
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+)
+
+# Use GPU when available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = model.to(device)
+model.eval()
+
+
+SYSTEM_PROMPT = (
+    "You are a concise code reviewer. Given a code snippet, point out bugs, "
+    "edge cases, complexity issues, and style improvements. "
+    "Be specific and brief."
+)
+
+
+@spaces.GPU
+def review_code(code):
+    if not code or not code.strip():
+        return "Paste some code first."
+
+    messages = [
+        {
+            "role": "system",
+            "content": SYSTEM_PROMPT
+        },
+        {
+            "role": "user",
+            "content": f"Review this code:\n\n{code}"
+        }
+    ]
+
+    # Apply the model's chat template
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+        truncation=True,
+        max_length=4096
+    )
+
+    # Move inputs to the same device as the model
+    inputs = {
+        key: value.to(device)
+        for key, value in inputs.items()
+    }
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=300,
+            do_sample=False,
+            temperature=None,
+            top_p=None
+        )
+
+    # Only decode newly generated tokens
+    generated_ids = output_ids[0][inputs["input_ids"].shape[1]:]
+
+    review = tokenizer.decode(
+        generated_ids,
+        skip_special_tokens=True
+    )
+
+    return review.strip()
+
+
+with gr.Blocks(title="AI Code Reviewer") as demo:
+
+    gr.Markdown(
+        "## AI Code Reviewer\n"
+        "Paste a code snippet and get feedback on bugs, edge cases, "
+        "complexity, and style."
+    )
+
+    code_input = gr.Code(
+        label="Your code",
+        language="python",
+        lines=15
+    )
+
+    review_btn = gr.Button(
+        "Review",
+        variant="primary"
+    )
+
+    output = gr.Markdown()
+
+    review_btn.click(
+        review_code,
+        inputs=code_input,
+        outputs=output
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio==6.3.0
+transformers>=4.42
+torch
+accelerate
+spaces