Delete anycoder_app/models.py

anycoder_app/models.py

@@ -1,358 +0,0 @@
-"""
-Model inference and client management for AnyCoder.
-Handles different model providers and inference clients.
-"""
-import os
-from typing import Dict, List, Optional, Tuple
-import re
-from http import HTTPStatus
-
-from huggingface_hub import InferenceClient
-from openai import OpenAI
-from mistralai import Mistral
-import dashscope
-from google import genai
-from google.genai import types
-
-from .config import HF_TOKEN, AVAILABLE_MODELS
-
-# Type definitions
-History = List[Dict[str, str]]
-Messages = List[Dict[str, str]]
-
-def get_inference_client(model_id, provider="auto"):
-    """Return an InferenceClient with provider based on model_id and user selection."""
-    if model_id == "gemini-3.0-pro":
-        # Use Poe (OpenAI-compatible) client for Gemini 3.0 Pro
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "qwen3-30b-a3b-instruct-2507":
-        # Use DashScope OpenAI client
-        return OpenAI(
-            api_key=os.getenv("DASHSCOPE_API_KEY"),
-            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-        )
-    elif model_id == "qwen3-30b-a3b-thinking-2507":
-        # Use DashScope OpenAI client for Thinking model
-        return OpenAI(
-            api_key=os.getenv("DASHSCOPE_API_KEY"),
-            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-        )
-    elif model_id == "qwen3-coder-30b-a3b-instruct":
-        # Use DashScope OpenAI client for Coder model
-        return OpenAI(
-            api_key=os.getenv("DASHSCOPE_API_KEY"),
-            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-        )
-    elif model_id == "gpt-5":
-        # Use Poe (OpenAI-compatible) client for GPT-5 model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "gpt-5.1":
-        # Use Poe (OpenAI-compatible) client for GPT-5.1 model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "gpt-5.1-instant":
-        # Use Poe (OpenAI-compatible) client for GPT-5.1 Instant model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "gpt-5.1-codex":
-        # Use Poe (OpenAI-compatible) client for GPT-5.1 Codex model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "gpt-5.1-codex-mini":
-        # Use Poe (OpenAI-compatible) client for GPT-5.1 Codex Mini model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "grok-4":
-        # Use Poe (OpenAI-compatible) client for Grok-4 model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "Grok-Code-Fast-1":
-        # Use Poe (OpenAI-compatible) client for Grok-Code-Fast-1 model
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "claude-opus-4.1":
-        # Use Poe (OpenAI-compatible) client for Claude-Opus-4.1
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "claude-sonnet-4.5":
-        # Use Poe (OpenAI-compatible) client for Claude-Sonnet-4.5
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "claude-haiku-4.5":
-        # Use Poe (OpenAI-compatible) client for Claude-Haiku-4.5
-        return OpenAI(
-            api_key=os.getenv("POE_API_KEY"),
-            base_url="https://api.poe.com/v1"
-        )
-    elif model_id == "qwen3-max-preview":
-        # Use DashScope International OpenAI client for Qwen3 Max Preview
-        return OpenAI(
-            api_key=os.getenv("DASHSCOPE_API_KEY"),
-            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-        )
-    elif model_id == "openrouter/sonoma-dusk-alpha":
-        # Use OpenRouter client for Sonoma Dusk Alpha model
-        return OpenAI(
-            api_key=os.getenv("OPENROUTER_API_KEY"),
-            base_url="https://openrouter.ai/api/v1",
-        )
-    elif model_id == "openrouter/sonoma-sky-alpha":
-        # Use OpenRouter client for Sonoma Sky Alpha model
-        return OpenAI(
-            api_key=os.getenv("OPENROUTER_API_KEY"),
-            base_url="https://openrouter.ai/api/v1",
-        )
-    elif model_id == "openrouter/sherlock-dash-alpha":
-        # Use OpenRouter client for Sherlock Dash Alpha model
-        return OpenAI(
-            api_key=os.getenv("OPENROUTER_API_KEY"),
-            base_url="https://openrouter.ai/api/v1",
-        )
-    elif model_id == "openrouter/sherlock-think-alpha":
-        # Use OpenRouter client for Sherlock Think Alpha model
-        return OpenAI(
-            api_key=os.getenv("OPENROUTER_API_KEY"),
-            base_url="https://openrouter.ai/api/v1",
-        )
-    elif model_id == "MiniMaxAI/MiniMax-M2":
-        # Use HuggingFace InferenceClient with Novita provider for MiniMax M2 model
-        provider = "novita"
-    elif model_id == "step-3":
-        # Use StepFun API client for Step-3 model
-        return OpenAI(
-            api_key=os.getenv("STEP_API_KEY"),
-            base_url="https://api.stepfun.com/v1"
-        )
-    elif model_id == "codestral-2508" or model_id == "mistral-medium-2508":
-        # Use Mistral client for Mistral models
-        return Mistral(api_key=os.getenv("MISTRAL_API_KEY"))
-    elif model_id == "gemini-2.5-flash":
-        # Use Google Gemini (OpenAI-compatible) client
-        return OpenAI(
-            api_key=os.getenv("GEMINI_API_KEY"),
-            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
-        )
-    elif model_id == "gemini-2.5-pro":
-        # Use Google Gemini Pro (OpenAI-compatible) client
-        return OpenAI(
-            api_key=os.getenv("GEMINI_API_KEY"),
-            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
-        )
-    elif model_id == "gemini-flash-latest":
-        # Use Google Gemini Flash Latest (OpenAI-compatible) client
-        return OpenAI(
-            api_key=os.getenv("GEMINI_API_KEY"),
-            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
-        )
-    elif model_id == "gemini-flash-lite-latest":
-        # Use Google Gemini Flash Lite Latest (OpenAI-compatible) client
-        return OpenAI(
-            api_key=os.getenv("GEMINI_API_KEY"),
-            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
-        )
-    elif model_id == "kimi-k2-turbo-preview":
-        # Use Moonshot AI (OpenAI-compatible) client for Kimi K2 Turbo (Preview)
-        return OpenAI(
-            api_key=os.getenv("MOONSHOT_API_KEY"),
-            base_url="https://api.moonshot.ai/v1",
-        )
-    elif model_id == "moonshotai/Kimi-K2-Thinking":
-        # Use HuggingFace InferenceClient with Novita provider for Kimi K2 Thinking
-        provider = "novita"
-    elif model_id == "stealth-model-1":
-        # Use stealth model with generic configuration
-        api_key = os.getenv("STEALTH_MODEL_1_API_KEY")
-        if not api_key:
-            raise ValueError("STEALTH_MODEL_1_API_KEY environment variable is required for Carrot model")
-        
-        base_url = os.getenv("STEALTH_MODEL_1_BASE_URL")
-        if not base_url:
-            raise ValueError("STEALTH_MODEL_1_BASE_URL environment variable is required for Carrot model")
-        
-        return OpenAI(
-            api_key=api_key,
-            base_url=base_url,
-        )
-    elif model_id == "moonshotai/Kimi-K2-Instruct":
-        provider = "groq"
-    elif model_id == "deepseek-ai/DeepSeek-V3.1":
-        provider = "novita"
-    elif model_id == "deepseek-ai/DeepSeek-V3.1-Terminus":
-        provider = "novita"
-    elif model_id == "deepseek-ai/DeepSeek-V3.2-Exp":
-        provider = "novita"
-    elif model_id == "zai-org/GLM-4.5":
-        provider = "fireworks-ai"
-    elif model_id == "zai-org/GLM-4.6":
-        # Use auto provider for GLM-4.6, HuggingFace will select best available
-        provider = "auto"
-    return InferenceClient(
-        provider=provider,
-        api_key=HF_TOKEN,
-        bill_to="huggingface"
-    )
-
-# Helper function to get real model ID for stealth models and special cases
-def get_real_model_id(model_id: str) -> str:
-    """Get the real model ID, checking environment variables for stealth models and handling special model formats"""
-    if model_id == "stealth-model-1":
-        # Get the real model ID from environment variable
-        real_model_id = os.getenv("STEALTH_MODEL_1_ID")
-        if not real_model_id:
-            raise ValueError("STEALTH_MODEL_1_ID environment variable is required for Carrot model")
-        
-        return real_model_id
-    elif model_id == "zai-org/GLM-4.6":
-        # GLM-4.6 requires provider suffix in model string for API calls
-        return "zai-org/GLM-4.6:zai-org"
-    return model_id
-
-# Type definitions
-History = List[Tuple[str, str]]
-Messages = List[Dict[str, str]]
-
-def history_to_messages(history: History, system: str) -> Messages:
-    messages = [{'role': 'system', 'content': system}]
-    for h in history:
-        # Handle multimodal content in history
-        user_content = h[0]
-        if isinstance(user_content, list):
-            # Extract text from multimodal content
-            text_content = ""
-            for item in user_content:
-                if isinstance(item, dict) and item.get("type") == "text":
-                    text_content += item.get("text", "")
-            user_content = text_content if text_content else str(user_content)
-        
-        messages.append({'role': 'user', 'content': user_content})
-        messages.append({'role': 'assistant', 'content': h[1]})
-    return messages
-
-def history_to_chatbot_messages(history: History) -> List[Dict[str, str]]:
-    """Convert history tuples to chatbot message format"""
-    messages = []
-    for user_msg, assistant_msg in history:
-        # Handle multimodal content
-        if isinstance(user_msg, list):
-            text_content = ""
-            for item in user_msg:
-                if isinstance(item, dict) and item.get("type") == "text":
-                    text_content += item.get("text", "")
-            user_msg = text_content if text_content else str(user_msg)
-        
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": assistant_msg})
-    return messages
-
-def strip_tool_call_markers(text):
-    """Remove TOOL_CALL markers that some LLMs (like Qwen) add to their output."""
-    if not text:
-        return text
-    # Remove [TOOL_CALL] and [/TOOL_CALL] markers
-    text = re.sub(r'\[/?TOOL_CALL\]', '', text, flags=re.IGNORECASE)
-    # Remove standalone }} that appears with tool calls
-    # Only remove if it's on its own line or at the end
-    text = re.sub(r'^\s*\}\}\s*$', '', text, flags=re.MULTILINE)
-    return text.strip()
-
-def remove_code_block(text):
-    # First strip any tool call markers
-    text = strip_tool_call_markers(text)
-    
-    # Try to match code blocks with language markers
-    patterns = [
-        r'```(?:html|HTML)\n([\s\S]+?)\n```',  # Match ```html or ```HTML
-        r'```\n([\s\S]+?)\n```',               # Match code blocks without language markers
-        r'```([\s\S]+?)```'                      # Match code blocks without line breaks
-    ]
-    for pattern in patterns:
-        match = re.search(pattern, text, re.DOTALL)
-        if match:
-            extracted = match.group(1).strip()
-            # Remove a leading language marker line (e.g., 'python') if present
-            if extracted.split('\n', 1)[0].strip().lower() in ['python', 'html', 'css', 'javascript', 'json', 'c', 'cpp', 'markdown', 'latex', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-mssql', 'sql-mysql', 'sql-mariadb', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgsql', 'sql-gql', 'sql-gpsql', 'sql-sparksql', 'sql-esper']:
-                return extracted.split('\n', 1)[1] if '\n' in extracted else ''
-            # If HTML markup starts later in the block (e.g., Poe injected preface), trim to first HTML root
-            html_root_idx = None
-            for tag in ['<!DOCTYPE html', '<html']:
-                idx = extracted.find(tag)
-                if idx != -1:
-                    html_root_idx = idx if html_root_idx is None else min(html_root_idx, idx)
-            if html_root_idx is not None and html_root_idx > 0:
-                return extracted[html_root_idx:].strip()
-            return extracted
-    # If no code block is found, check if the entire text is HTML
-    stripped = text.strip()
-    if stripped.startswith('<!DOCTYPE html>') or stripped.startswith('<html') or stripped.startswith('<'):
-        # If HTML root appears later (e.g., Poe preface), trim to first HTML root
-        for tag in ['<!DOCTYPE html', '<html']:
-            idx = stripped.find(tag)
-            if idx > 0:
-                return stripped[idx:].strip()
-        return stripped
-    # Special handling for python: remove python marker
-    if text.strip().startswith('```python'):
-        return text.strip()[9:-3].strip()
-    # Remove a leading language marker line if present (fallback)
-    lines = text.strip().split('\n', 1)
-    if lines[0].strip().lower() in ['python', 'html', 'css', 'javascript', 'json', 'c', 'cpp', 'markdown', 'latex', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-mssql', 'sql-mysql', 'sql-mariadb', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgsql', 'sql-gql', 'sql-gpsql', 'sql-sparksql', 'sql-esper']:
-        return lines[1] if len(lines) > 1 else ''
-    return text.strip()
-
-## React CDN compatibility fixer removed per user preference
-
-def strip_thinking_tags(text: str) -> str:
-    """Strip <think> tags and [TOOL_CALL] markers from streaming output."""
-    if not text:
-        return text
-    # Remove <think> opening tags
-    text = re.sub(r'<think>', '', text, flags=re.IGNORECASE)
-    # Remove </think> closing tags
-    text = re.sub(r'</think>', '', text, flags=re.IGNORECASE)
-    # Remove [TOOL_CALL] markers
-    text = re.sub(r'\[/?TOOL_CALL\]', '', text, flags=re.IGNORECASE)
-    return text
-
-def strip_placeholder_thinking(text: str) -> str:
-    """Remove placeholder 'Thinking...' status lines from streamed text."""
-    if not text:
-        return text
-    # Matches lines like: "Thinking..." or "Thinking... (12s elapsed)"
-    return re.sub(r"(?mi)^[\t ]*Thinking\.\.\.(?:\s*\(\d+s elapsed\))?[\t ]*$\n?", "", text)
-
-def is_placeholder_thinking_only(text: str) -> bool:
-    """Return True if text contains only 'Thinking...' placeholder lines (with optional elapsed)."""
-    if not text:
-        return False
-    stripped = text.strip()
-    if not stripped:
-        return False
-    return re.fullmatch(r"(?s)(?:\s*Thinking\.\.\.(?:\s*\(\d+s elapsed\))?\s*)+", stripped) is not None
-
-def extract_last_thinking_line(text: str) -> str:
-    """Extract the last 'Thinking...' line to display as status."""
-    matches = list(re.finditer(r"Thinking\.\.\.(?:\s*\(\d+s elapsed\))?", text))
-    return matches[-1].group(0) if matches else "Thinking..."
-