Create app.py

app.py

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+import gradio as gr
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# 1. Download GGUF file from your Repo
+model_path = hf_hub_download(
+    repo_id="SALEETAI/coding-agent-qwen-sft-v2-GGUF",
+    filename="coding-agent-qwen-sft-v2-GGUF.q4_k_m.gguf"
+)
+
+# 2. Initialize the model
+# We set n_ctx to 2048 for good performance on free CPU
+llm = Llama(model_path=model_path, n_ctx=2048, verbose=False)
+
+def generate_code(prompt):
+    # Professional inference parameters
+    output = llm(
+        f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n",
+        max_tokens=1024,
+        stop=["<|im_end|>"],
+        echo=False,
+        repeat_penalty=1.2,
+        temperature=0.4
+    )
+    return output['choices'][0]['text']
+
+# 3. Create Gradio UI
+demo = gr.Interface(
+    fn=generate_code,
+    inputs=gr.Textbox(lines=5, placeholder="Enter a coding prompt..."),
+    outputs=gr.Code(language="python"),
+    title="🚀 Coding Agent Qwen-SFT v2 (GGUF Demo)",
+    description="Running on Free CPU using llama-cpp-python."
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)