app.py

import os
import json
import gradio as gr
from llama_cpp import Llama
from radon.complexity import cc_visit
import lizard

# ======================
# Model Path
# ======================
MODEL_PATH = "models/deepseek/deepseek-coder-6.7b-instruct.Q4_K_M.gguf"

# ======================
# Load LLM (runs locally)
# ======================
llm = Llama(
    model_path=MODEL_PATH,
    n_ctx=4096,
    n_threads=os.cpu_count(),
    verbose=False
)

# ======================
# Static analysis (fast + reliable)
# ======================
def static_complexity(code: str):
    try:
        analysis = lizard.analyze_file.analyze_source_code("code.py", code)
        funcs = analysis.function_list

        if not funcs:
            return "O(1)", "O(1)"

        max_cc = max(f.cyclomatic_complexity for f in funcs)

        if max_cc <= 2:
            return "O(1)", "O(1)"
        elif max_cc <= 5:
            return "O(n)", "O(1)"
        elif max_cc <= 10:
            return "O(n^2)", "O(n)"
        else:
            return "O(n^3)", "O(n)"

    except Exception:
        return None, None


# ======================
# LLM fallback
# ======================
def llm_complexity(code: str):
    prompt = f"""
You are an expert algorithm analyst.

Analyze the following Python code.

Rules:
- Output ONLY JSON
- No explanation

Format:
{{
  "time_complexity": "O(?)",
  "space_complexity": "O(?)"
}}

Code:
{code}
"""

    output = llm(
        prompt,
        max_tokens=120,
        temperature=0.0,
    )

    return output["choices"][0]["text"].strip()


# ======================
# Main predictor
# ======================
def predict_big_o(code: str):
    if not code.strip():
        return '{"time_complexity":"O(1)","space_complexity":"O(1)"}'
    time_c, space_c = static_complexity(code)

    # if time_c is not None:
    #     return json.dumps({
    #         "time_complexity": time_c,
    #         "space_complexity": space_c
    #     })
    return llm_complexity(code)


# ======================
# Gradio UI
# ======================
iface = gr.Interface(
    fn=predict_big_o,
    inputs=gr.Code(language="python", label="Paste your code"),
    outputs=gr.Textbox(label="Big-O Prediction"),
    title="Big-O Complexity Predictor (Offline)",
    description="Predicts Time and Space Complexity locally using DeepSeek GGUF"
)

if __name__ == "__main__":
    iface.launch()