Delete claude_analysis.py

claude_analysis.py

@@ -1,164 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import os
-import sys
-import json
-from pathlib import Path
-
-try:
-    import anthropic
-except ImportError:
-    print("\nERROR: anthropic library is not installed")
-    print("To install, run: pip install anthropic")
-    print("or: python -m pip install anthropic\n")
-    print(json.dumps({"error": "Missing anthropic library. Please run: pip install anthropic"}))
-    sys.exit(1)
-
-# Проверяем версию anthropic
-try:
-    anthropic_version = anthropic.__version__
-    print(f"\nDEBUG - Using anthropic version: {anthropic_version}")
-except:
-    print("\nWARNING - Could not determine anthropic version")
-
-api_key = os.environ.get("ANTHROPIC_API_KEY", "")
-# api_key = "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"
-if not api_key:
-    print(json.dumps({"error": "No ANTHROPIC_API_KEY provided"}))
-    sys.exit(0)
-
-def get_txt_contents(local_path=None):
-    """
-    Получает содержимое .txt файлов либо из локальной папки, либо из аргументов командной строки
-    """
-    txt_contents = []
-    
-    # Пробуем использовать локальную папку
-    if local_path:
-        try:
-            path = Path(local_path)
-            if path.exists():
-                print(f"\nDEBUG - Reading files from local path: {path}")
-                for txt_file in path.glob("*.txt"):
-                    try:
-                        with open(txt_file, 'r', encoding='utf-8') as f:
-                            txt_contents.append(f.read())
-                            print(f"DEBUG - Read file: {txt_file.name}")
-                    except Exception as e:
-                        print(f"DEBUG - Error reading {txt_file}: {e}")
-                return txt_contents
-        except Exception as e:
-            print(f"\nDEBUG - Error accessing local path: {e}")
-    
-    # Если локальная папка недоступна, используем аргументы командной строки
-    txt_file_list = sys.argv[1:]
-    if not txt_file_list:
-        return txt_contents
-        
-    for f in txt_file_list:
-        try:
-            with open(f, 'r', encoding='utf-8') as ff:
-                txt_contents.append(ff.read())
-        except Exception as e:
-            print(f"DEBUG - Error reading {f}: {e}")
-            
-    return txt_contents
-
-# -----------------------------------------------------------------------------
-# 1. Собираем список файлов
-# -----------------------------------------------------------------------------
-local_path = r"G:\My Drive\Kohya_SS\Flux\SoloBand\IconsGray"
-txt_contents = get_txt_contents(local_path)
-
-if not txt_contents:
-    print("\nDEBUG - No text files found in local path or arguments")
-
-combined_text = "\n---\n".join(txt_contents)
-
-# -----------------------------------------------------------------------------
-# 2. Формируем промпт для Claude
-# -----------------------------------------------------------------------------
-prompt_content = f"""You are a highly creative AI art director. Your task is to generate an imaginative and specific JSON response. Never use "Unknown" values - instead, create interesting and unique artistic directions even without input files.
-
-You MUST ALWAYS return ONLY a valid JSON object in this exact format, with no additional text:
-
-{{
-  "token": "Choose exactly one: FD_AI (Fantasy & Dragons), SB_AI (Storybook), CM_AI (Comic), AN_AI (Anime), RT_AI (Realistic)",
-  "art_type": "Create a specific description like: 'Mystical Forest Portal', 'Neon Cyberpunk City', 'Cozy Cottage Interior', 'Ancient Dragon Lair', 'Futuristic Space Station'",
-  "style_name": "Define clear style like: 'Vibrant watercolor fantasy', 'Detailed pencil illustration', 'Dark gothic oil painting', 'Cute kawaii digital art', 'Cinematic 3D render'",
-  "model_name": "Will be auto-generated as {{token}}_{{art_type}}_V1",
-  "prompts": [
-    "Generate 6 detailed prompts that perfectly match the art_type and style_name",
-    "Each prompt should be unique and vivid",
-    "Include specific details about composition, lighting, mood, colors",
-    "Keep each prompt to 1-2 impactful sentences",
-    "Focus on visual elements rather than story",
-    "Maintain consistent style across all prompts"
-  ]
-}}
-
-Even with no input files, you must generate a creative and complete response using the available tokens and styles. Never return "Unknown" values.
-
-Input content to analyze (if any):
----
-{combined_text}
-"""
-
-# Добавляем отладочный вывод промпта
-print("\nDEBUG - Full prompt being sent to Claude:")
-print("="*80)
-print(prompt_content)
-print("="*80)
-
-# -----------------------------------------------------------------------------
-# 3. Обращаемся к Anthropic (Claude) с указанной моделью
-# -----------------------------------------------------------------------------
-try:
-    client = anthropic.Anthropic(api_key=api_key)
-    message = client.messages.create(
-        model="claude-3-sonnet-20240229",
-        max_tokens=1024,
-        messages=[
-            {
-                "role": "user",
-                "content": prompt_content
-            }
-        ],
-        temperature=0.7,
-    )
-    raw_reply = message.content[0].text
-except Exception as e:
-    print(json.dumps({"error": f"Request to Claude failed: {str(e)}"}))
-    sys.exit(0)
-
-# -----------------------------------------------------------------------------
-# 4. Пытаемся интерпретировать ответ как JSON
-# -----------------------------------------------------------------------------
-try:
-    data = json.loads(raw_reply)
-except:
-    # Если парсить напрямую не получается, отправим ошибку
-    print(json.dumps({"error": "Claude response is not valid JSON", "raw_reply": raw_reply}))
-    sys.exit(0)
-
-# -----------------------------------------------------------------------------
-# 5. (опционально) Проверяем, что в ответе есть нужные поля
-# -----------------------------------------------------------------------------
-token = data.get("token", "SB_AI")
-art_type = data.get("art_type", "UnknownArtType")
-style_name = data.get("style_name", "UnknownStyle")
-model_name = data.get("model_name", f"{token}_{art_type}_V1")
-prompts = data.get("prompts", [])
-
-# -----------------------------------------------------------------------------
-# 6. Выводим финальный JSON в stdout
-# -----------------------------------------------------------------------------
-out = {
-  "token": token,
-  "art_type": art_type,
-  "style_name": style_name,
-  "model_name": model_name,
-  "prompts": prompts
-}
-print(json.dumps(out, ensure_ascii=False))