チュートリアル：GPT-5 のマルチタスク横断評価


import requests
from io import BytesIO
from PIL import Image
import base64
from openai import OpenAI
import weave; weave.init("gptV_gen_and_desc")


OPENAI_API_KEY = ""
client = OpenAI(api_key=OPENAI_API_KEY)

@weave.op
def gptV_describe_image_with_url(
    pil_img: Image.Image,
    img_url: str,
    prompt: str = "Describe what is in this image."
) -> str:
    """
    Describe an image using both its URL and base64 encoding for the model, 
    and logs PIL image in Weave.
    """
    # Prepare base64-encoded image for OpenAI input
    inp = {
        "role": "user",
        "content": [
            {"type": "input_text", "text": "Describe what is in this image."},
            {"type": "input_image", "image_url": img_url}
        ]
    }
    resp = client.responses.create(
        model="gpt-5",
        input=[inp]
    )
    return resp.output_text


@weave.op
def gpt_generate_image(
    prompt: str,
    size: str = "1024x1024"
) -> Image.Image:
    """
    Generate an image from a prompt using OpenAI DALL-E (PIL image output).
    """


    prompt = f"Generate an image given the following description: {prompt}"
    print(f"[DEBUG] Generating image with prompt: {prompt}")

    try:
        response = client.responses.create(
            model="gpt-5",
            input=prompt,
            tools=[{"type": "image_generation"}],  # no tool_choice
        )
        print(f"[DEBUG] Raw response received: {response}")
    except Exception as e:
        print(f"[ERROR] Failed to create response: {e}")
        return None

    try:
        image_data = [
            output.result
            for output in response.output
            if output.type == "image_generation_call"
        ]
        print(f"[DEBUG] Extracted image data: {image_data}")
    except Exception as e:
        print(f"[ERROR] Failed to extract image data: {e}")
        return None

    if image_data:
        try:
            image_base64 = image_data[0]
            filename = "generated_image.png"
            with open(filename, "wb") as f:
                f.write(base64.b64decode(image_base64))
            print(f"[DEBUG] Image saved to {filename}")
            
            pil_img = Image.open(BytesIO(base64.b64decode(image_base64)))
            return pil_img
        
        
        except Exception as e:
            print(f"[ERROR] Failed to save image: {e}")
            return None



# --- Main Example usage ---
if __name__ == "__main__":
    img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3f/Fronalpstock_big.jpg/800px-Fronalpstock_big.jpg"
    headers = {"User-Agent": "Mozilla/5.0 (compatible; GeminiScript/1.0)"}
    response = requests.get(img_url, headers=headers)
    response.raise_for_status()
    pil_img = Image.open(BytesIO(response.content))

    # 1. DESCRIBE
    desc = gptV_describe_image_with_url(
        pil_img=pil_img,
        img_url=img_url,
        prompt="Describe what is in this image."
    )
    print("\nGPT-V description:")
    print(desc)

    # 2. GENERATE NEW IMAGE FROM DESCRIPTION
    gen_img = gpt_generate_image(desc)
    gen_img.save("gptV_generated.png")
    print("\nGenerated image saved as gptV_generated.png")

import os
import sys
import time
import subprocess
from datasets import load_dataset
from litellm import completion as oai_completion
from anthropic import Anthropic
from google import genai
from openai import OpenAI
import weave
import random
import numpy as np

from weave.flow.eval_imperative import EvaluationLogger
from google import genai
from google.genai import types
from litellm import completion as oai_completion
import re
from litellm import completion as oai_completion
import requests
import json




weave.init("codecontests_eval")


# API keys
# os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "sk-...")
CLAUDE_KEY = os.getenv("CLAUDE_API_KEY", "")
GENAI_KEY = os.getenv("GOOGLE_API_KEY", "")
OPENROUTER_KEY = os.getenv("OPENROUTER_API_KEY", "")


# Clients
anthropic_client = Anthropic(api_key=CLAUDE_KEY)
gemini_client = genai.Client(api_key=GENAI_KEY)
openrouter_client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENROUTER_KEY,
)
client = OpenAI(api_key="")



def clean_llm_code_block(text):
    import re


    cleaned_text = text.replace("```python", "").replace("```", "").strip()
    code_blocks = re.findall(r"(def solve\(.*?)(?=^def |\Z)", cleaned_text, re.DOTALL | re.MULTILINE)
    source_text = code_blocks[-1] if code_blocks else cleaned_text


    prompt = (
        "Given the following response from a language model, extract ONLY the valid Python code for the function. "
        "Do not include any explanations, text, or formatting fences. Only the code.\n\n"
        f"Response:\n{source_text}\n\n"
        "Return ONLY the Python code, including any necessary imports:"
    )


    response = oai_completion(
        model="openai/gpt-4o-2024-08-06",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.0,
    )


    gpt4o_code = response["choices"][0]["message"]["content"]
    gpt4o_code = gpt4o_code.replace("```python", "").replace("```", "").strip()
    return gpt4o_code


@weave.op()
def generate_completion(model: str, prompt: str, effort: str="low") -> str:
    # if model.startswith("openai/"):
    #     response = oai_completion(
    #         model=model,
    #         messages=[{"role": "user", "content": prompt}],
    #         reasoning_effort="low",
    #     )
    #     return response["choices"][0]["message"]["content"].strip()
    if model.startswith("openai/"):
        response = client.responses.create(
            model=model.replace("openai/", ""),
            reasoning={"effort": effort},
            input=[
                {"role": "user", "content": prompt}
            ]
        )
        return response.output_text.strip()


    elif model.startswith("anthropic/"):
        response = anthropic_client.messages.create(
            model=model.replace("anthropic/", ""),
            max_tokens=8000,
            thinking={"type": "enabled", "budget_tokens": 4000},
            messages=[{"role": "user", "content": prompt}],
        )
        for block in response.content:
            if block.type == "text":
                return block.text.strip()
        return "[No Claude response]"


    elif model.startswith("gemini/"):
        result = gemini_client.models.generate_content(
            model=model.replace("gemini/", ""),
            config=types.GenerateContentConfig(
                thinking_config=types.ThinkingConfig(thinking_budget=4000)
            ),
            contents=[prompt]
        )
        return result.text.strip() if result.text else "[No Gemini response]"


    elif model.startswith("openrouter/"):

        url = "https://openrouter.ai/api/v1/chat/completions"
        headers = {
            "Authorization": f"Bearer {OPENROUTER_KEY}",
            "Content-Type": "application/json"
        }
        payload = {
            "model": model.replace("openrouter/", ""),
            "messages": [
                {"role": "system", "content": "Reasoning: high"},
                {"role": "user", "content": prompt}
            ],
        }
        response = requests.post(url, headers=headers, data=json.dumps(payload))
        resp_json = response.json()
        if 'choices' in resp_json and resp_json['choices']:
            # To get the reasoning, use: resp_json['choices'][0]['message'].get('reasoning')
            return resp_json['choices'][0]['message'].get('content', '[No answer found]')
        else:
            return "[No choices found in OSS response]"

    else:
        raise ValueError(f"Unsupported model: {model}")
    




def ask_llm_for_function_implementation(description: str, model: str, effort: str | None = None) -> str:
    prompt = (
        f"Write a Python3 function named `solve` with typed input arguments for this problem -- eg the solve function should take arguments to handle different test cases:\n\n"
        f"{description.strip()}\n\n"
        "Return only a valid Python function -- no special packages that arent commonly used and NO MAIN function, no  if __name__ == __main__....., JUST write the function --  that returns the result. No comments, no explanations."
        f"HOWEVER, you still need to include necessary imports for libraries"
        f"IF you do not include the right imports, the code will not be executable, and your response will be judged as incorrect!"
    )
    # Pass effort only to OpenAI via generate_completion when provided
    if effort is not None and model.startswith("openai/"):
        return clean_llm_code_block(generate_completion(model, prompt, effort=effort))
    else:
        return clean_llm_code_block(generate_completion(model, prompt))




@weave.op
def ask_llm_for_function_call(code: str, raw_input: str, model: str) -> str:


    prompt = (
        "You're given a Python function and a single input string. "
        "Format it into a valid Python function call using only standard types.\n\n"
        f"Function:\n{code}\n\n"
        f"Input:\n{raw_input.strip()}\n\n"
        "Return ONLY a valid function call (e.g., solve(3, 5)) WITH NO 'def' "
    )


    # Always use GPT-4o for this inference, regardless of the `model` argument.
    response = oai_completion(
        model="openai/gpt-4o-2024-08-06",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.0,
    )
    # The LLM may return markdown code blocks; strip them just in case.
    content = response["choices"][0]["message"]["content"]
    content = content.replace("```python", "").replace("```", "").strip()
    return content


def compare_output_with_llm(expected: str, actual: str, model: str) -> bool:
    prompt = (
        f"Expected output: {expected.strip()}\n"
        f"Actual output: {actual.strip()}\n\n"
        "Are these outputs equivalent? Eg ignore minor formatting errors etc, we are just looking for overall correctness in the output Reply YES or NO."
    )
    # response = generate_completion(model, prompt)


    response = oai_completion(
        model="openai/gpt-4o-2024-08-06",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.0,
    )
    # The LLM may return markdown code blocks; strip them just in case.
    res = 'YES' in str(response["choices"][0]["message"]["content"]).upper()
    return res 



def run_code_and_call_function(code: str, function_call: str, timeout=10):
    full_code = code + f"\n\nprint({function_call})"
    try:
        start = time.time()
        result = subprocess.run(
            [sys.executable, "-c", full_code],
            capture_output=True,
            text=True,
            timeout=timeout
        )
        latency = time.time() - start
        return result.stdout.strip(), result.stderr.strip(), latency
    except subprocess.TimeoutExpired:
        return "", "Execution timed out.", timeout
    except Exception as e:
        return "", str(e), 0.0




def ask_model_for_pip_command(error_msg):
    prompt = (
        "Given this Python error:\n\n"
        + error_msg +
        "\n\nWrite the pip install command needed to fix it. Only return the command, e.g.:\n"
        "pip install requests"
    )
    return generate_completion("openai/gpt-4o-2024-08-06", prompt)



def run_pip_install(pip_command):
    print(f"Running: {pip_command}")
    try:
        result = subprocess.run(
            pip_command.split(),
            capture_output=True,
            text=True,
            timeout=180
        )
        print(result.stdout.strip())
        if result.stderr:
            print(result.stderr.strip())
    except Exception as e:
        print(f"pip install failed: {e}")



def evaluate_model_on_code_contests(model_name: str, reasoning_effort: str | None = None):
    print(f"\n\nRunning evaluation for model: {model_name}\n")

    random.seed(42)
    np.random.seed(42)
    ds = load_dataset("deepmind/code_contests", split="test", streaming=True)
    ds = list(ds.take(31))


    # Build sanitized model identifier for Weave, including reasoning effort if provided
    model_id = model_name.replace("-", "_").replace("/", "_").replace(".", "_")
    if reasoning_effort:
        effort_id = str(reasoning_effort).replace("-", "_").replace("/", "_").replace(".", "_")
        model_id = f"{model_id}__{effort_id}"

    eval_logger = EvaluationLogger(
        model=model_id,
        dataset="code_contests_test"
    )
    all_latencies = []


    for i in range(30):
        row = ds[i]
        description = row["description"]
        raw_inputs = row["public_tests"]["input"]
        expected_outputs = row["public_tests"]["output"]


        try:
            # Forward reasoning_effort only to OpenAI generate_completion
            code = ask_llm_for_function_implementation(
                description,
                model=model_name,
                effort=reasoning_effort if (reasoning_effort and model_name.startswith("openai/")) else None,
            )
            print(f"\n=== Task {row['name']} ===", flush=True)
            # print("Generated code:\n", code)


            all_passed = True
            task_latencies = []
            results_lst, expected_lst = [], []


            for j, raw_input in enumerate(raw_inputs):
                expected = expected_outputs[j] if j < len(expected_outputs) else ""


                try:
                    function_call = ask_llm_for_function_call(code, raw_input, model=model_name)
                    result, error, latency = run_code_and_call_function(code, function_call)
                    if latency < 99:
                        task_latencies.append(latency)


                
                    if error:
                        print(f"[{j}] Runtime error: {error}")
                        if "ModuleNotFoundError" in error:
                            pip_cmd = ask_model_for_pip_command(error)
                            run_pip_install(pip_cmd)
                            # Re-run once after pip install
                            result, error, latency = run_code_and_call_function(code, function_call)
                            
                            task_latencies.append(latency)
                            if error:
                                print(f"[{j}] Retry failed: {error}")
                                all_passed = False
                                continue
                        else:
                            all_passed = False
                            continue


                    is_correct = compare_output_with_llm(expected, result, model="openai/gpt-4o-2024-08-06")###### 
                    results_lst.append(result)
                    expected_lst.append(expected)
                    if not is_correct:
                        all_passed = False
                    print(f"[{j}] input: {raw_input.strip()} → output: {result} | expected: {expected.strip()} | PASS: {is_correct} | latency: {latency:.2f}s")


                except Exception as inner:
                    print(f"[{j}] Inner error: {repr(inner)}")
                    all_passed = False
            
            task_avg_latency = sum(task_latencies) / len(task_latencies) if len(task_latencies) > 0 else 0.0
            all_latencies.extend(task_latencies)


            prediction_log = eval_logger.log_prediction(
                inputs={"description": description},
                output={'code': code, 'execution_result': results_lst, 'expected_execution_result': expected_lst}
            )
            prediction_log.log_score("correctness", all_passed)
            prediction_log.log_score("code_latency", task_avg_latency)
            prediction_log.finish()


        except Exception as e:
            print(f"[{i}] Top-level failure: {repr(e)}")
            prediction_log = eval_logger.log_prediction(
                inputs={"description": description},
                output=str(e)
            )
            prediction_log.log_score("correctness", False)
            prediction_log.finish()


    avg_latency = sum(all_latencies) / len(all_latencies) if all_latencies else 0.0
    eval_logger.log_summary({"avg_code_latency": avg_latency})
    print(f"Evaluation complete for {model_name}. View in Weave UI.")




# Run for all models


evaluate_model_on_code_contests("openrouter/openai/gpt-oss-120b")
evaluate_model_on_code_contests("anthropic/claude-opus-4-1-20250805")
evaluate_model_on_code_contests("openai/gpt-5", reasoning_effort='low')


import os
import sys
import re
import requests
import tempfile
import webbrowser
import html
import asyncio
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from playwright.async_api import async_playwright
from PIL import Image
import pytesseract

from openai import OpenAI
import weave; weave.init("gpt5_agent")


# -------- CONFIG --------
LOGFILE = sys.argv[1] if len(sys.argv) > 1 else "/tmp/agentpython-stderr.log"
OUTPUT_DIR = "github_screenshots"
PARALLEL_PAGE_LOADS = 6   # how many pages to screenshot at once
OCR_WORKERS = min(8, (os.cpu_count() or 4))
# ------------------------

def verbose_print(msg):
    print(f"\033[95m[LOG] {msg}\033[0m", flush=True)

def read_log(logfile):
    verbose_print(f"Reading from log file: {logfile}")
    if not os.path.exists(logfile) or os.path.getsize(logfile) == 0:
        print("[LOG] Log file empty or not found. No action needed.", flush=True)
        sys.exit(0)
    with open(logfile) as f:
        content = f.read()
    print(f"\n--- Log Content ---\n{content}\n{'-'*40}", flush=True)
    return content

def is_python_error(txt):
    if "Traceback (most recent call last):" in txt or "Exception" in txt or "Error" in txt:
        verbose_print("Looks like a Python error.")
        return True
    verbose_print("Not detected as Python error (using fallback toolchain).")
    return False

@weave.op 
def generate_search_query_openai(error_str):
    verbose_print("Generating generalized search query using gpt-5 (OpenAI)...")
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    gpt_response = client.responses.create(
        model="gpt-5",
        reasoning={"effort": "low"},
        input=[
            {
                "role": "user",
                "content": (
                    "You are generating a GitHub search query from an error message. "
                    "Your goal is to create a generic query that will return relevant results from GitHub issues across many repositories. "
                    "Do NOT include overly specific details that would narrow results too much, such as:\n"
                    "- File paths\n"
                    "- Line numbers\n"
                    "- Exact tensor shapes, array sizes, or specific numeric values in parentheses\n"
                    "- Memory addresses\n"
                    "- Random seeds or run-specific values\n\n"
                    "Instead:\n"
                    "- Keep only the key error type and descriptive text\n"
                    "- Include the relevant library name if obvious (e.g., torch, numpy, pandas)\n"
                    "- Use quotes for the core error message if helpful\n\n"
                    "Output only the final search query string. No explanation, no extra words.\n\n"
                    f"Error:\n{error_str}"
                )
            }
        ]
    )
    query = (gpt_response.output_text or "").strip()
    print("Generated search query:", repr(query), flush=True)
    return query

async def _screenshot_one(page, url, path):
    try:
        await page.goto(url, timeout=20000)
        await page.set_viewport_size({"width": 1920, "height": 1080})
        await page.screenshot(path=path, full_page=True)
        verbose_print(f"[+] Screenshot saved: {path}")
        return True
    except Exception as e:
        verbose_print(f"[!] Failed screenshot for {url}: {e}")
        return False

async def capture_screenshots_parallel(urls, out_dir, concurrency=6):
    os.makedirs(out_dir, exist_ok=True)
    results = [None] * len(urls)

    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)
        context = await browser.new_context()

        sem = asyncio.Semaphore(concurrency)
        async def worker(i, url):
            path = os.path.join(out_dir, f"issue_{i+1}.png")
            async with sem:
                page = await context.new_page()
                ok = await _screenshot_one(page, url, path)
                await page.close()
                results[i] = path if ok else None

        tasks = [asyncio.create_task(worker(i, url)) for i, url in enumerate(urls)]
        await asyncio.gather(*tasks)
        await browser.close()

    return results  # list of file paths (or None)

def run_ocr(image_path):
    if not image_path or not os.path.exists(image_path):
        return ""
    try:
        img = Image.open(image_path)
        text = pytesseract.image_to_string(img)
        # save alongside
        txt_path = image_path.rsplit(".", 1)[0] + ".txt"
        with open(txt_path, "w", encoding="utf-8") as f:
            f.write(text)
        return text
    except Exception as e:
        verbose_print(f"[!] OCR failed for {image_path}: {e}")
        return ""
@weave.op 
def summarize_with_gpt5(error_text, github_text):
    if not github_text.strip():
        return "[No OCR text to summarize]"
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    resp = client.responses.create(
        model="gpt-5",
        reasoning={"effort": "low"},
        input=[
            {
                "role": "user",
                "content": (
                    "You are assisting in debugging. The following is a Python error message, "
                    "and then OCR-extracted text from a GitHub issue discussing it. "
                    "Summarize the most likely cause and solution in a few sentences. "
                    "Only include relevant fix instructions. Be concise.\n\n"
                    f"Error:\n{error_text}\n\nGitHub Issue Content:\n{github_text}"
                )
            }
        ]
    )
    return (resp.output_text or "").strip()


@weave.op 
def search_github(query, github_token=None, owner=None, repo=None, error_text=None):
    verbose_print(f"Searching GitHub issues for: {query!r}")
    url = 'https://api.github.com/search/issues'
    headers = {'Accept': 'application/vnd.github.v3+json'}
    if github_token:
        headers['Authorization'] = f'token {github_token}'
    if owner and repo:
        gh_query = f'repo:{owner}/{repo} is:issue {query}'
    else:
        gh_query = query
    params = {'q': gh_query, 'per_page': 5}
    resp = requests.get(url, headers=headers, params=params)
    if resp.status_code != 200:
        print(f"[GitHub] Search failed: {resp.status_code} {resp.text}", flush=True)
        return []

    items = resp.json().get('items', [])
    if not items:
        print("[GitHub] No results found.", flush=True)
        return []

    issue_urls = [it.get('html_url', '') for it in items]
    # Parallel screenshots
    verbose_print("Capturing GitHub issues as screenshots in parallel...")
    screenshots = asyncio.run(capture_screenshots_parallel(issue_urls, OUTPUT_DIR, PARALLEL_PAGE_LOADS))

    # Parallel OCR
    verbose_print("Running OCR on screenshots in parallel...")
    ocr_texts = [""] * len(screenshots)
    with ThreadPoolExecutor(max_workers=OCR_WORKERS) as ex:
        futures = {ex.submit(run_ocr, path): i for i, path in enumerate(screenshots)}
        for fut in as_completed(futures):
            i = futures[fut]
            try:
                ocr_texts[i] = fut.result() or ""
            except Exception as e:
                verbose_print(f"[!] OCR worker error for index {i}: {e}")
                ocr_texts[i] = ""

    # Summarize in parallel
    gh_results = []
    summaries = [""] * len(items)

    def _summarize_idx(i: int) -> str:
        return summarize_with_gpt5(error_text or query, ocr_texts[i])

    max_workers = min(8, len(items)) if items else 0
    if max_workers > 0:
        with ThreadPoolExecutor(max_workers=max_workers) as ex:
            future_map = {ex.submit(_summarize_idx, i): i for i in range(len(items))}
            for fut in as_completed(future_map):
                i = future_map[fut]
                try:
                    summaries[i] = fut.result() or ""
                except Exception as e:
                    summaries[i] = f"[summarize error: {e}]"

    for idx, item in enumerate(items):
        summary = summaries[idx]
        issue_info = {
            "number": item.get("number", "?"),
            "title": item.get("title", ""),
            "url": item.get("html_url", ""),
            "body": (item.get("body", "") or "")[:600] + ("..." if item.get("body") and len(item["body"]) > 600 else ""),
            "ocr_summary": summary,
            "screenshot": screenshots[idx] or ""
        }
        gh_results.append(issue_info)
        print("=" * 60, flush=True)
        print(f"Issue #{issue_info['number']}: {issue_info['title']}", flush=True)
        print(f"URL: {issue_info['url']}", flush=True)
        print(f"Screenshot: {issue_info['screenshot']}", flush=True)
        print(f"Solution Summary: {summary}", flush=True)
    print("=" * 60, flush=True)

    return gh_results


@weave.op 
def openai_web_search(query):
    verbose_print(f"Querying OpenAI gpt-5 web search for: {query!r}")
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    search_response = client.responses.create(
        model="gpt-5",
        tools=[{"type": "web_search_preview"}],
        reasoning={"effort": "low"},
        input=query
    )
    print("\n=== [OpenAI] Web Search AI Answer ===", flush=True)
    print(search_response.output_text, flush=True)
    links = re.findall(r'\[([^\]]+)\]\((https?://[^\)]+)\)', search_response.output_text or "")
    link_objs = []
    if links:
        for title, url in links:
            link_objs.append({'title': title, 'url': url})
    else:
        print("No citations found in output_text.", flush=True)
    return {'output_text': search_response.output_text, 'citations': link_objs}


@weave.op
def write_html_report(
    log,
    file_snippet,
    tools,
    gh_results,
    web_results,
    static_result=None,
    out_path=None
):
    """Write the HTML debug report and return both the path and the raw HTML."""
    verbose_print("Writing HTML report ...")
    out_path = out_path or os.path.join(tempfile.gettempdir(), 'dbg_report.html')
    css = """
    body { font-family: 'Segoe UI', sans-serif; background: #f5f7fa; color: #333; margin: 0; padding: 0; }
    header { background: #1e293b; color: white; padding: 20px; text-align: center; font-size: 1.5em; }
    section { padding: 20px; margin: 20px; background: white; border-radius: 8px; box-shadow: 0 2px 6px rgba(0,0,0,0.1); }
    h2 { border-bottom: 2px solid #e5e7eb; padding-bottom: 5px; margin-bottom: 10px; color: #1f2937; }
    pre { background: #0f172a; color: #e2e8f0; padding: 15px; border-radius: 6px; overflow-x: auto; font-size: 0.9em; }
    a { color: #2563eb; text-decoration: none; }
    a:hover { text-decoration: underline; }
    .gh-issue { border: 1px solid #e5e7eb; padding: 10px; border-radius: 6px; margin-bottom: 16px; background: #f9fafb; }
    .shot { margin: 8px 0; display: block; max-width: 100%; border: 1px solid #e5e7eb; border-radius: 6px; }
    .label { font-weight: 600; color: #111827; }
    """
    html_parts = []
    html_parts.append(f"<html><head><meta charset='utf-8'><title>Debug Results</title><style>{css}</style></head><body>\n")
    html_parts.append("<header>Debugging Session Report</header>\n")
    html_parts.append("<section><h2>Error Log</h2>")
    html_parts.append(f"<pre>{html.escape(log or 'None')}</pre></section>")
    if file_snippet:
        html_parts.append("<section><h2>Relevant Source Snippet</h2>")
        html_parts.append(f"<pre>{html.escape(file_snippet)}</pre></section>")
    if tools:
        html_parts.append("<section><h2>LLM Tool Recommendations</h2>")
        html_parts.append(f"<pre>{html.escape(str(tools))}</pre></section>")
    if static_result:
        html_parts.append("<section><h2>Static Analysis</h2>")
        diag = static_result.get("diagnosis", "")
        fixes = "\n".join(static_result.get("fixes", []) or [])
        patch = static_result.get("patch", "")
        test_snip = static_result.get("test_snippet", "")
        notes = static_result.get("notes", "")
        html_parts.append(f"<div class='label'>Diagnosis</div><pre>{html.escape(diag)}</pre>")
        if fixes:
            html_parts.append(f"<div class='label'>Proposed Fixes</div><pre>{html.escape(fixes)}</pre>")
        if patch:
            html_parts.append(f"<div class='label'>Proposed Patch</div><pre>{html.escape(patch)}</pre>")
        if test_snip:
            html_parts.append(f"<div class='label'>Quick Test</div><pre>{html.escape(test_snip)}</pre>")
        if notes:
            html_parts.append(f"<div class='label'>Notes</div><pre>{html.escape(notes)}</pre>")
        html_parts.append("</section>")
    if gh_results:
        html_parts.append("<section><h2>GitHub Related Issues</h2>")
        for res in gh_results:
            html_parts.append(f"<div class='gh-issue'><div class='label'>#{res['number']}: {html.escape(res['title'])}</div>")
            html_parts.append(f"<a href='{res['url']}'>{res['url']}</a><br>")
            html_parts.append(f"<div class='label'>Issue Preview</div><pre>{html.escape(res['body'])}</pre>")
            html_parts.append(f"<div class='label'>Solution Summary</div><pre>{html.escape(res.get('ocr_summary',''))}</pre></div>")
        html_parts.append("</section>")
    if web_results:
        html_parts.append("<section><h2>Web Search AI Answer</h2>")
        html_parts.append(f"<pre>{html.escape(web_results.get('output_text', ''))}</pre>")
        if web_results.get('citations'):
            html_parts.append("<ul>")
            for c in web_results['citations']:
                html_parts.append(f"<li><a href='{c['url']}'>{html.escape(c['title'])}</a></li>")
            html_parts.append("</ul>")
        html_parts.append("</section>")
    html_parts.append("</body></html>")
    raw_html = ''.join(html_parts)
    with open(out_path, "w", encoding="utf-8") as f:
        f.write(raw_html)
    verbose_print(f"HTML written at: {out_path}")
    return out_path, raw_html

def open_html_in_chrome(path):
    verbose_print(f"Opening HTML report in browser ...")
    url = Path(path).resolve().as_uri()
    if sys.platform == 'darwin':
        chrome = '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome'
        if os.path.exists(chrome):
            os.system(f'open -a "{chrome}" "{url}"')
        else:
            webbrowser.open(url)
    elif sys.platform == 'win32':
        import subprocess
        try:
            subprocess.Popen(['start', 'chrome', url], shell=True)
        except Exception:
            webbrowser.open(url)
    else:
        try:
            os.system(f'google-chrome "{url}"')
        except Exception:
            webbrowser.open(url)

def find_files_from_log_gpt(log_content):
    verbose_print("Invoking LLM to identify implicated files from the log...")
    user_prompt = (
        "Given this error message or traceback, list all file paths (and, if available, line numbers) "
        "involved in the error. Output one JSON per line, as:\n"
        '{"file": "path/to/file.py", "line": 123}\n'
        'If line is not found, use null.\n'
        f"\nError:\n{log_content}"
    )
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    llm_resp = client.responses.create(
        model="gpt-5",
        reasoning={"effort": "low"},
        input=[{"role": "user", "content": user_prompt}]
    )
    output = llm_resp.output_text or ""
    results = []
    for l in output.splitlines():
        l = l.strip()
        if not l:
            continue
        try:
            results.append(eval(l, {"null": None}))
        except Exception as exc:
            verbose_print(f"[File Extraction Skipped Line]: {l!r} ({exc})")
    verbose_print(f"LLM File Extraction Result: {results}")
    return results

def get_file_snippet(file_path, n_lines=20, line=None):
    if not os.path.exists(file_path):
        verbose_print(f"[WARN] File not found: {file_path}")
        return None
    code = []
    with open(file_path, "r") as f:
        lines = f.readlines()
        if line and 1 <= line <= len(lines):
            s = max(0, line-6)
            e = min(len(lines), line+5)
            code = lines[s:e]
        else:
            code = lines[:n_lines]
    return "".join(code)


@weave.op 
def suggest_tools(error_message, code_snippet):
    import ast, json
    verbose_print("Asking LLM: Based on the error and file, which tool to use next?")
    prompt = (
        "You are an AI debugging orchestrator. The following is a Python error message and a snippet of code "
        "from a file involved in the error. Based on this, choose which tools should be used next, and explain why. "
        "Possible tools: github_issue_search, web_search, static_analysis. "
        "Output a single python dictionary (not JSON, not explanation). Example: "
        "{'recommendations':['web_search', 'github_issue_search'], 'justification': 'Searching the web and GitHub can help resolve import errors quickly.'}\n"
        "Error:\n" + error_message +
        "\n\nFile snippet:\n" + code_snippet +
        "\n\nOutput only the dictionary. No preamble or explanation."
        "alwqays use the github tool man"
    )
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    resp = client.responses.create(
        model="gpt-5",
        reasoning={"effort": "low"},
        input=[{"role": "user", "content": prompt}]
    )
    output = (resp.output_text or "").strip()
    try:
        if output.startswith("```") and output.endswith("```"):
            output = output[3:-3].strip()
        obj = ast.literal_eval(output)
        if isinstance(obj, dict):
            verbose_print(f"LLM Tool Suggestion: {obj}")
            return obj
    except Exception:
        pass
    m = re.search(r'\{.*\}', output, re.DOTALL)
    if m:
        try:
            obj = ast.literal_eval(m.group(0))
            if isinstance(obj, dict):
                verbose_print(f"LLM Tool Suggestion: {obj}")
                return obj
        except Exception:
            pass
    verbose_print(f"LLM Suggestion RAW output (not parsable): {output!r}")
    return {"recommendations": [], "justification": 'Could not parse LLM response'}



@weave.op
def final_recommendation_with_gpt5(
    error_text: str,
    code_snippet: str | None,
    tool_suggestion: dict | None,
    gh: list | None,
    web: dict | None,
    query: str,
) -> str:
    """Synthesize a concise, actionable plan from all gathered signals."""
    from openai import OpenAI
    import json, os

    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    gh_brief = []
    if gh:
        for item in gh[:5]:
            gh_brief.append({
                "title": item.get("title", ""),
                "url": item.get("url", ""),
                "summary": item.get("ocr_summary", "")
            })

    web_brief = {
        "answer": (web or {}).get("output_text") if web else None,
        "citations": (web or {}).get("citations") if web else None
    }

    payload = {
        "error_text": error_text,
        "code_snippet": code_snippet,
        "tool_suggestion": tool_suggestion,
        "search_query": query,
        "github_findings": gh_brief,
        "web_findings": web_brief
    }

    prompt = (
        "You are a debugging assistant. Based on the following data, produce a short, actionable plan.\n"
        "Include:\n"
        "1. Likely root cause in one or two sentences.\n"
        "2. Concrete next steps that can be executed now.\n"
        "3. If shapes or types are mismatched, propose exact code edits.\n"
        "4. If library problems are implicated, propose install or version pin commands.\n"
        "5. If no external search is needed, say so and outline local static checks.\n\n"
        f"DATA:\n{json.dumps(payload, ensure_ascii=False, indent=2)}\n\n"
        "Return a concise plan. No preamble."
    )

    resp = client.responses.create(
        model="gpt-5",
        reasoning={"effort": "low"},
        input=[{"role": "user", "content": prompt}]
    )
    return (resp.output_text or "").strip()




@weave.op
def static_analysis_gpt5(error_text: str, code_snippet: str | None) -> dict:
    """
    Pure GPT-5 static analysis. No web or GitHub.
    Returns a dict with fields: diagnosis, fixes, patch, test_snippet, notes.
    """
    from openai import OpenAI
    import os, json

    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    system = (
        "You are a Python static analyzer. Read the error and the code snippet. "
        "Find the root cause and propose concrete code edits. "
        "If there is a tensor shape mismatch, compute the exact shapes and provide the corrected operation. "
        "Return strict JSON with keys: diagnosis, fixes, patch, test_snippet, notes."
    )

    user = {
        "error_text": error_text,
        "code_snippet": code_snippet or ""
    }

    prompt = (
        "Analyze the following and return strict JSON only. "
        "Do not include commentary outside JSON.\n\n"
        f"{json.dumps(user, ensure_ascii=False, indent=2)}\n\n"
        "{ \"diagnosis\": \"...\", "
        "\"fixes\": [\"...\"], "
        "\"patch\": \"diff or edited code\", "
        "\"test_snippet\": \"python code to quickly sanity check\", "
        "\"notes\": \"short notes\" }"
    )

    resp = client.responses.create(
        model="gpt-5",
        reasoning={"effort": "low"},
        input=[
            {"role": "system", "content": system},
            {"role": "user", "content": prompt}
        ]
    )

    raw = (resp.output_text or "").strip()
    try:
        data = json.loads(raw)
    except Exception:
        data = {
            "diagnosis": "Could not parse JSON from model",
            "fixes": [],
            "patch": "",
            "test_snippet": "",
            "notes": raw[:500]
        }
    return data

@weave.op
def main(force_use_all_tools: bool = True):
    import os
    GITHUB_TOKEN = ""
    os.environ['OPENAI_API_KEY'] = ""  # set your key here


    error_content = read_log(LOGFILE)
    search_query = generate_search_query_openai(error_content) if is_python_error(error_content) \
                   else error_content.strip().replace("\n", " ")

    files_info, snippet, tools = None, None, None
    try:
        files_info = find_files_from_log_gpt(error_content)
        if files_info:
            file_to_examine, line_hint = files_info[0].get("file"), files_info[0].get("line")
            verbose_print(f"Selected file: {file_to_examine}, line: {line_hint}")
            snippet = get_file_snippet(file_to_examine, line=line_hint)
            if snippet:
                print("\n--- Snippet from implicated file ---\n", flush=True)
                print(snippet, flush=True)
                print("-" * 60, flush=True)
                tools = suggest_tools(error_content, snippet)
                print("\n[TOOL RECOMMENDATION]:", tools, flush=True)
            else:
                verbose_print(f"Could not get snippet from file {file_to_examine}")
        else:
            verbose_print("Did not find any file to examine in the error.")
    except Exception as e:
        verbose_print(f"[WARN] File inference failed: {e}")

    gh_results = []
    web_results = None
    static_result = None

    # run static analysis
    if force_use_all_tools or (tools and "static_analysis" in tools.get("recommendations", [])):
        static_result = static_analysis_gpt5(error_content, snippet)

    # run github search
    if force_use_all_tools or (tools and "github_issue_search" in tools.get("recommendations", [])):
        gh_results = search_github(
            search_query,
            github_token=GITHUB_TOKEN,
            error_text=error_content
        )

    # run web search
    if force_use_all_tools or (tools and "web_search" in tools.get("recommendations", [])):
        try:
            web_results = openai_web_search(search_query)
        except Exception as ex:
            print(f"[OpenAI] Search failed: {ex}", flush=True)

    final_plan = final_recommendation_with_gpt5(
        error_text=error_content,
        code_snippet=snippet,
        tool_suggestion=tools,
        gh=gh_results,
        web=web_results,
        query=search_query
    )
    print("\n=== FINAL RECOMMENDATION ===\n", final_plan, "\n", flush=True)

    html_path, raw_html = write_html_report(
        log=error_content,
        file_snippet=snippet,
        tools=tools,
        gh_results=gh_results,
        web_results=web_results,
        static_result=static_result   # pass it so the section renders
    )

    appended = raw_html.replace(
        "</body></html>",
        f"<section><h2>Final Recommendation</h2><pre>{html.escape(final_plan or '')}</pre></section></body></html>"
    )
    with open(html_path, "w", encoding="utf-8") as f:
        f.write(appended)

    open_html_in_chrome(html_path)
    verbose_print("Searches complete. Examine the HTML report in Chrome for summary and results.\n")



if __name__ == "__main__":
    # Fix for Windows event loop policy (Playwright + asyncio)
    if sys.platform.startswith("win"):
        try:
            asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())  # type: ignore[attr-defined]
        except Exception:
            pass
    main(force_use_all_tools=True)