Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,8 @@
+__pycache__/
+*.pyc
+.DS_Store
+*.safetensors
+*.bin
+*.pt
+*.pth
+.huggingface/

README.md

@@ -1,13 +1,46 @@
 ---
-title: Codecraft
-emoji: 👀
-colorFrom: yellow
-colorTo: yellow
+title: CodeCraft - Qwen2.5-Coder-7B
+emoji: 💻
+colorFrom: indigo
+colorTo: purple
 sdk: gradio
-sdk_version: 6.20.0
-python_version: '3.13'
+sdk_version: 5.23.3
 app_file: app.py
 pinned: false
+python_version: "3.12"
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CodeCraft - AI Coding Assistant
+
+Powered by **Qwen2.5-Coder-7B-Instruct** running on Hugging Face ZeroGPU.
+
+Chat with a state-of-the-art coding assistant. Supports code generation, debugging,
+refactoring, explanation, and general programming help across all major languages.
+
+## Features
+
+- 💬 **Chat interface** with syntax-highlighted code blocks
+- ⚙️ **Adjustable parameters**: temperature, top-p, max tokens, system prompt
+- 📡 **Built-in API endpoint** at `/api` for programmatic use
+- 🎨 **Syntax-highlighted output** via Gradio Markdown + code blocks
+
+## API Usage
+
+Every Gradio Space exposes a Rest API at `/api`. For this Space:
+
+```python
+import requests
+
+response = requests.post(
+    "https://<your-space>.hf.space/gradio_api/call/generate",
+    json={
+        "data": ["write a fibonacci function in rust", "You are a helpful coding assistant.", 0.3, 0.9, 2048]
+    }
+)
+print(response.json())
+```
+
+## Model
+
+[Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) —
+7B parameter code-specific LLM with 128K context, instruction-tuned for chat and coding tasks.

app.py

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+import spaces
+import gradio as gr
+import torch
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    TextIteratorStreamer,
+)
+from threading import Thread
+from typing import Optional, Generator
+
+# ---------------------------------------------------------------------------
+# Module-scope model loading — ZeroGPU manages GPU offload transparently
+# ---------------------------------------------------------------------------
+MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"
+
+quant_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=quant_config,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+model.eval()
+
+DEFAULT_SYSTEM = "You are an expert coding assistant. Write clean, efficient, well-documented code."
+
+
+# ---------------------------------------------------------------------------
+# ZeroGPU-decorated generation
+# ---------------------------------------------------------------------------
+@spaces.GPU(duration=120)
+def generate(
+    messages: list[dict],
+    temperature: float,
+    top_p: float,
+    max_new_tokens: int,
+) -> str:
+    """Run model inference inside a ZeroGPU worker process.
+
+    Args are pickled across the process boundary.
+    Returns CPU text — safe for unpickling in the main process.
+    """
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    with torch.inference_mode():
+        outputs = model.generate(
+            inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=temperature > 0.0,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    generated = outputs[0][inputs.shape[1]:]
+    return tokenizer.decode(generated, skip_special_tokens=True)
+
+
+# ---------------------------------------------------------------------------
+# Streaming variant — yields tokens as they're generated
+# ---------------------------------------------------------------------------
+@spaces.GPU(duration=120)
+def generate_stream(
+    messages: list[dict],
+    temperature: float,
+    top_p: float,
+    max_new_tokens: int,
+) -> Generator[str, None, None]:
+    """Stream tokens from the model one-by-one."""
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    streamer = TextIteratorStreamer(
+        tokenizer,
+        skip_prompt=True,
+        skip_special_tokens=True,
+    )
+
+    generation_kwargs = dict(
+        inputs=inputs,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=temperature > 0.0,
+        pad_token_id=tokenizer.eos_token_id,
+        streamer=streamer,
+    )
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    for token in streamer:
+        yield token
+
+
+# ---------------------------------------------------------------------------
+# Non-streaming wrapper (for API endpoint)
+# ---------------------------------------------------------------------------
+def predict(
+    message: str,
+    history: list,
+    system_prompt: str,
+    temperature: float,
+    top_p: float,
+    max_tokens: int,
+):
+    """Chat function — called both from UI and the auto-generated Gradio API."""
+    messages = [{"role": "system", "content": system_prompt}]
+    for user_msg, asst_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        if asst_msg:
+            messages.append({"role": "assistant", "content": asst_msg})
+    messages.append({"role": "user", "content": message})
+
+    output = generate(messages, temperature, top_p, max_tokens)
+    return output
+
+
+# ---------------------------------------------------------------------------
+# Streaming chat handler
+# ---------------------------------------------------------------------------
+def chat_fn(
+    message: str,
+    history: list,
+    system_prompt: str,
+    temperature: float,
+    top_p: float,
+    max_tokens: int,
+):
+    """Generator that yields partial (message, history) tuples for streaming UI."""
+    messages = [{"role": "system", "content": system_prompt}]
+    for user_msg, asst_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        if asst_msg:
+            messages.append({"role": "assistant", "content": asst_msg})
+    messages.append({"role": "user", "content": message})
+
+    partial = ""
+    for token in generate_stream(messages, temperature, top_p, max_tokens):
+        partial += token
+        yield partial
+
+
+# ---------------------------------------------------------------------------
+# Lang / theme helper
+# ---------------------------------------------------------------------------
+LANGUAGES = ["python", "javascript", "typescript", "rust", "go", "java", "cpp",
+             "csharp", "ruby", "php", "sql", "bash", "html", "css", "json", "yaml"]
+
+
+def build_examples():
+    return [
+        ["Write a Python function that checks if a string is a palindrome."],
+        ["Create a Rust function that reads a CSV file and returns the row count."],
+        ["Explain the difference between an interface and a type in TypeScript."],
+        ["Write a Go HTTP server that serves static files on port 8080."],
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+def create_ui():
+    with gr.Blocks(
+        title="CodeCraft - AI Coding Assistant",
+        theme=gr.themes.Soft(
+            primary_hue="indigo",
+            neutral_hue="slate",
+        ),
+        fill_width=True,
+    ) as demo:
+        gr.Markdown(
+            "# 💻 CodeCraft — AI Coding Assistant\n"
+            "Powered by **Qwen2.5-Coder-7B-Instruct** · ZeroGPU"
+        )
+
+        chatbot = gr.Chatbot(
+            label="Conversation",
+            placeholder="Ask me anything about code...",
+            render_markdown=True,
+            show_copy_button=True,
+            height=500,
+        )
+
+        with gr.Row():
+            msg = gr.Textbox(
+                label="Your message",
+                placeholder="Write a Python async function that downloads a URL...",
+                scale=8,
+                container=False,
+            )
+            submit_btn = gr.Button("Send", variant="primary", scale=1, min_width=80)
+            clear_btn = gr.Button("Clear", scale=1, min_width=80)
+
+        with gr.Accordion("⚙️ Settings", open=False):
+            with gr.Row():
+                system_prompt = gr.Textbox(
+                    label="System Prompt",
+                    value=DEFAULT_SYSTEM,
+                    lines=2,
+                    scale=3,
+                )
+                with gr.Column(scale=1):
+                    temperature = gr.Slider(
+                        label="Temperature", minimum=0.0, maximum=1.5,
+                        value=0.3, step=0.05,
+                    )
+            with gr.Row():
+                top_p = gr.Slider(
+                    label="Top-P", minimum=0.6, maximum=1.0,
+                    value=0.9, step=0.05,
+                )
+                max_tokens = gr.Slider(
+                    label="Max Tokens", minimum=128, maximum=4096,
+                    value=2048, step=128,
+                )
+
+        gr.Examples(
+            examples=build_examples(),
+            inputs=[msg],
+            label="Try these prompts",
+        )
+
+        # -- State: chat history --
+        history_state = gr.State([])
+
+        # -- Event wiring --
+        def respond(message, history, system, temp, top_p_val, max_tok):
+            if not message.strip():
+                return "", history, history
+            history = history + [(message, None)]
+            yield "", history, []
+            for partial in chat_fn(message, history[:-1], system, temp, top_p_val, max_tok):
+                history[-1] = (message, partial)
+                yield "", history, []
+            yield "", history, [message]
+
+        # Wire submit via message box
+        msg.submit(
+            respond,
+            inputs=[msg, history_state, system_prompt, temperature, top_p, max_tokens],
+            outputs=[msg, chatbot, history_state],
+            concurrency_limit=8,
+        )
+        submit_btn.click(
+            respond,
+            inputs=[msg, history_state, system_prompt, temperature, top_p, max_tokens],
+            outputs=[msg, chatbot, history_state],
+            concurrency_limit=8,
+        )
+
+        # Clear conversation
+        def clear_conversation():
+            return [], "", []
+
+        clear_btn.click(
+            clear_conversation,
+            outputs=[history_state, chatbot, msg],
+            concurrency_limit=8,
+        )
+
+        # -- API endpoint exposure (auto by Gradio, but re-binding as top-level fn) --
+        gr.Markdown(
+            """
+            ### 📡 API
+
+            This Space exposes a REST API at `/gradio_api/call/predict`.
+            See the [Gradio docs](https://www.gradio.app/guides/sharing-your-app#api) for usage.
+            """
+        )
+
+        return demo
+
+
+if __name__ == "__main__":
+    demo = create_ui()
+    demo.queue(default_concurrency_limit=8)
+    demo.launch()

requirements.txt

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+torch>=2.8.0
+transformers>=4.50.0
+accelerate>=1.5.0
+bitsandbytes>=0.45.0
+gradio>=5.23.0
+sentencepiece>=0.2.0
+protobuf>=5.28.0