git init

.env.example

@@ -0,0 +1,2 @@
+OPENROUTER_API_KEY=sk-00000000000000000000000000000000
+OPENAI_API_KEY=sk-00000000000000000000000000000000

.gitignore

@@ -1,9 +1,7 @@
-# ---> Python
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
-
 # C extensions
 *.so
 
@@ -11,7 +9,7 @@ __pycache__/
 .Python
 build/
 develop-eggs/
-dist/
+#dist/
 downloads/
 eggs/
 .eggs/
@@ -57,9 +55,9 @@ cover/
 *.pot
 
 # Django stuff:
-*.log
 local_settings.py
-db.sqlite3
+*.sqlite
+*.sqlite3
 db.sqlite3-journal
 
 # Flask stuff:
@@ -158,5 +156,160 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+.pnpm-debug.log*
+
+# Diagnostic reports (https://nodejs.org/api/report.html)
+report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+*.lcov
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# Bower dependency directory (https://bower.io/)
+bower_components
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (https://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules/
+jspm_packages/
+
+# Snowpack dependency directory (https://snowpack.dev/)
+web_modules/
+
+# TypeScript cache
+*.tsbuildinfo
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Optional stylelint cache
+.stylelintcache
+
+# Microbundle cache
+.rpt2_cache/
+.rts2_cache_cjs/
+.rts2_cache_es/
+.rts2_cache_umd/
+
+# Optional REPL history
+.node_repl_history
+
+# Output of 'npm pack'
+*.tgz
+
+# Yarn Integrity file
+.yarn-integrity
+
+# dotenv environment variable files
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# parcel-bundler cache (https://parceljs.org/)
+.parcel-cache
+
+# Next.js build output
+.next
+out
+
+# Nuxt.js build / generate output
+.nuxt
+dist
+
+# Gatsby files
+.cache/
+# Comment in the public line in if your project uses Gatsby and not Next.js
+# https://nextjs.org/blog/next-9-1#public-directory-support
+# public
+
+# vuepress build output
+.vuepress/dist
+
+# vuepress v2.x temp and cache directory
+.temp
+
+# vitepress build output
+**/.vitepress/dist
+
+# vitepress cache directory
+**/.vitepress/cache
+
+# Docusaurus cache and generated files
+.docusaurus
+
+# Serverless directories
+.serverless/
+
+# FuseBox cache
+.fusebox/
+
+# DynamoDB Local files
+.dynamodb/
+
+# TernJS port file
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+.vscode-test
+
+# yarn v2
+.yarn/cache
+.yarn/unplugged
+.yarn/build-state.yml
+.yarn/install-state.gz
+.pnp.*
+
+
+*.pyc
+
+# Packages
+#/dist/*
+# Unit test / coverage reports
+
+.pytest_cache
+
+.DS_Store
+.idea/*
+.python-version
+.vscode/*
+
+/docs/site/*
+.mypy_cache
+
+
+/poetry.toml
+
+*.js.map

requirements.txt

@@ -0,0 +1,5 @@
+Pillow
+tqdm
+openai
+python-dotenv
+pydantic

src/etl.py

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+import shutil
+from pathlib import Path
+from PIL import Image
+from tqdm import tqdm
+
+
+def process_image(source_path: Path, dest_dir: Path, max_width: int):
+    """
+    Обрабатывает одно изображение: изменяет его размер, если ширина превышает max_width,
+    иначе просто копирует. Сохраняет результат в dest_dir.
+    """
+    try:
+        with Image.open(source_path) as img:
+            dest_path = dest_dir / source_path.name
+
+            if img.width > max_width:
+                # Рассчитываем новую высоту, сохраняя пропорции
+                ratio = max_width / float(img.width)
+                new_height = int(float(img.height) * ratio)
+
+                # Изменяем размер и сохраняем
+                resized_img = img.resize((max_width, new_height), Image.Resampling.LANCZOS)
+                resized_img.save(dest_path)
+            else:
+                # Если изображение уже подходит, просто копируем его
+                shutil.copy2(source_path, dest_path)
+
+    except Exception as e:
+        tqdm.write(f"Ошибка при обработке файла {source_path.name}: {e}")
+
+
+def main():
+    """
+    Основная функция для создания обработанной версии датасета PlantWild.
+    """
+    # --- НАСТРОЙКИ ---
+    dataset_plantwild: Path = Path(r'E:\Project\OLIMP\PlantWild\plantwild')
+    dataset_processed: Path = Path(r'E:\Project\OLIMP\Benchmark_LLM_PlantWild\dataset')
+    num_images_per_class: int = 4
+    max_width: int = 640
+
+    # --- ЛОГИКА СКРИПТА ---
+    source_images_dir = dataset_plantwild / 'images'
+
+    if not source_images_dir.is_dir():
+        print(f"Ошибка: Исходная директория не найдена по пути '{source_images_dir}'")
+        return
+
+    dataset_processed.mkdir(parents=True, exist_ok=True)
+    print(f"Целевая директория: '{dataset_processed}'")
+
+    class_dirs = [d for d in source_images_dir.iterdir() if d.is_dir()]
+
+    print("Начинаем обработку классов...")
+    for class_dir in tqdm(class_dirs, desc="Обработка классов"):
+        class_name = class_dir.name
+        new_class_dir = dataset_processed / class_name
+        new_class_dir.mkdir(exist_ok=True)
+
+        image_paths = list(class_dir.glob('*.[jJ][pP][gG]')) + \
+                      list(class_dir.glob('*.[jJ][pP][eE][gG]')) + \
+                      list(class_dir.glob('*.[pP][nN][gG]'))
+
+        if not image_paths:
+            tqdm.write(f"Предупреждение: Изображения для класса '{class_name}' не найдены.")
+            continue
+
+        selected_images = image_paths[:num_images_per_class]
+
+        for image_path in selected_images:
+            process_image(source_path=image_path, dest_dir=new_class_dir, max_width=max_width)
+
+    print("\nОбработка датасета завершена.")
+
+
+if __name__ == "__main__":
+    main()

src/main.py

@@ -0,0 +1,171 @@
+import os
+import csv
+import json
+import base64
+from pathlib import Path
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+from openai import OpenAI, APIError
+from pydantic import BaseModel, Field, ValidationError
+from dotenv import load_dotenv
+from tqdm import tqdm
+
+
+# --- Pydantic-модель для структурированного ответа ---
+class PlantDiseasePrediction(BaseModel):
+    """Определяет структуру ответа от LLM."""
+    predicted_class: str = Field(..., description="The predicted disease class for the plant image.")
+
+
+# --- АБСТРАКЦИЯ ПРОВАЙДЕРОВ ---
+
+class ImageClassifierProvider(ABC):
+    @abstractmethod
+    def get_prediction(self, image_path: Path, prompt: str, classes: list[str]) -> Tuple[str, str]:
+        pass
+
+
+class BaseProvider(ImageClassifierProvider):
+    """Общий класс для провайдеров, использующих JSON mode."""
+
+    def __init__(self, model_name: str, api_key: str, base_url: str):
+        self.model_name = model_name
+        self.client = OpenAI(base_url=base_url, api_key=api_key)
+
+    def _encode_image_to_base64(self, image_path: Path) -> str:
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode('utf-8')
+
+    def get_prediction(self, image_path: Path, prompt: str, classes: list[str]) -> Tuple[str, str]:
+        base64_image = self._encode_image_to_base64(image_path)
+        raw_response_str = ""
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model_name,
+                response_format={"type": "json_object"},
+                messages=[
+                    {"role": "system", "content": prompt},
+                    {"role": "user", "content": [
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
+                    ]}
+                ]
+            )
+            raw_response_str = response.model_dump_json(indent=2)
+            message_content = response.choices[0].message.content
+
+            prediction_data = json.loads(message_content)
+            prediction_obj = PlantDiseasePrediction(**prediction_data)
+
+            if prediction_obj.predicted_class in classes:
+                return prediction_obj.predicted_class, raw_response_str
+            else:
+                # Модель сгенерировала класс, которого нет в списке
+                return "INVALID_CLASS_ERROR", raw_response_str
+
+        except (json.JSONDecodeError, ValidationError) as e:
+            error_msg = f"Ошибка парсинга или валидации JSON: {e}"
+            tqdm.write(f"{error_msg} для {image_path.name}")
+            return "PARSING_ERROR", raw_response_str or error_msg
+        except APIError as e:
+            error_msg = f"Ошибка API: {e}"
+            tqdm.write(f"{error_msg} для {image_path.name}")
+            return "API_ERROR", raw_response_str or str(e)
+        except Exception as e:
+            error_msg = f"Неизвестная ошибка: {e}"
+            tqdm.write(f"{error_msg} для {image_path.name}")
+            return "UNKNOWN_ERROR", raw_response_str or str(e)
+
+
+class OpenAIProvider(BaseProvider):
+    def __init__(self, model_name: str, api_key: str):
+        super().__init__(model_name, api_key, base_url="https://api.proxyapi.ru/openai/v1")
+
+
+class OpenRouterProvider(BaseProvider):
+    def __init__(self, model_name: str, api_key: str):
+        super().__init__(model_name, api_key, base_url="https://openai.api.proxyapi.ru/v1")
+
+
+def get_provider(provider_name: str, model_name: str, api_key: str) -> ImageClassifierProvider:
+    if provider_name.lower() == 'openrouter':
+        return OpenRouterProvider(model_name=model_name, api_key=api_key)
+    elif provider_name.lower() == 'openai':
+        return OpenAIProvider(model_name=model_name, api_key=api_key)
+    else:
+        raise ValueError(f"Провайдер '{provider_name}' не поддерживается.")
+
+
+def main():
+    load_dotenv()
+    PROVIDER = "openai"
+    MODEL_NAME = "gpt-5-chat-latest"
+    processed_dataset_path = Path(r'E:\Project\OLIMP\Benchmark_LLM_PlantWild\dataset')
+
+    api_key_env_var = "OPENAI_API_KEY" if PROVIDER.lower() == 'openai' else "OPENROUTER_API_KEY"
+    api_key = os.getenv(api_key_env_var)
+    if not api_key:
+        print(f"Ошибка: API ключ {api_key_env_var} не найден в .env файле.")
+        return
+
+    provider = get_provider(PROVIDER, MODEL_NAME, api_key)
+
+    classes_file = processed_dataset_path / 'classes.txt'
+    if not classes_file.exists():
+        print(f"Ошибка: Файл с классами не найден: {classes_file}")
+        return
+
+    with open(classes_file, 'r', encoding='utf-8') as f:
+        class_names = [line.strip().split(' ', 1)[1] for line in f if line.strip()]
+
+    # Промпт адаптирован для JSON mode
+    schema_json = json.dumps(PlantDiseasePrediction.model_json_schema(), indent=2)
+    prompt = (
+        f"You are an expert system for plant disease recognition. "
+        f"Analyze the user's image and respond ONLY with a valid JSON object that adheres to the following JSON Schema:\n"
+        f"```json\n{schema_json}\n```\n"
+        f"The 'predicted_class' must be one of the following valid classes: {', '.join(class_names)}. "
+        f"Do not include any other text, explanations, or markdown formatting."
+    )
+
+    safe_model_name = MODEL_NAME.replace('/', '_')
+    results_filename = f"results_{PROVIDER}_{safe_model_name}.csv"
+    error_log_filename = f"error_log_{PROVIDER}_{safe_model_name}.txt"
+
+    if os.path.exists(error_log_filename):
+        os.remove(error_log_filename)
+
+    with open(results_filename, 'w', newline='', encoding='utf-8') as csvfile:
+        csv_writer = csv.writer(csvfile, delimiter=';')
+        csv_writer.writerow(["image_name", "correct_class", "predicted_class"])
+        image_files = sorted(list(processed_dataset_path.rglob('*.[jJ][pP]*[gG]')))
+
+        print(f"Найдено {len(image_files)} изображений. Начинаем тестирование...")
+        error_count = 0
+
+        # ----------------------------------------
+        print("УБРАТЬ! СДЕЛАНО ТОЛЬКО ДЛЯ ОТЛАДКИ!")
+        image_files = image_files[0:4]
+        # ----------------------------------------
+
+        for image_path in tqdm(image_files, desc="Тестирование"):
+            correct_class = image_path.parent.name
+            predicted_class, raw_response = provider.get_prediction(image_path, prompt, class_names)
+
+            csv_writer.writerow([image_path.name, correct_class, predicted_class])
+
+            error_keywords = ["ERROR", "REFUSAL"]
+            if any(keyword in predicted_class for keyword in error_keywords):
+                error_count += 1
+                with open(error_log_filename, 'a', encoding='utf-8') as log_file:
+                    log_file.write(f"--- ERROR FOR IMAGE: {image_path.name} ---\n")
+                    log_file.write(f"PREDICTED_STATUS: {predicted_class}\nRAW RESPONSE:\n{raw_response}\n\n")
+
+    print(f"\nТестирование завершено.")
+    print(f"✅ Результаты сохранены в файле: {results_filename}")
+    if error_count > 0:
+        print(f"⚠️ Записано {error_count} ошибок. Подробности в файле: {error_log_filename}")
+
+
+if __name__ == "__main__":
+    main()