models/fetch.py

import random

class FetchModel:
    @staticmethod
    def all_models():
        models = [
            {
                "id": "llama-4-maverick-17b",
                "name": "LLaMA 4 Maverick 17B",
                "description": "Meta AI's 17B-parameter general-purpose model from the LLaMA 4 series, designed for high-quality text generation.",
                "type": "text"
            },
            {
                "id": "llama-4-scout-17b",
                "name": "LLaMA 4 Scout 17B",
                "description": "Instruction-tuned version of LLaMA 4 by Meta, tailored for alignment and structured task performance.",
                "type": "text"
            },
            {
                "id": "llama-3.1-8b",
                "name": "LLaMA 3.1 8B",
                "description": "A fast and lightweight 8B parameter model from Meta's LLaMA 3.1 line, optimized for low-latency inference.",
                "type": "text"
            },
            {
                "id": "llama-3.3-70b",
                "name": "LLaMA 3.3 70B",
                "description": "Meta's 70B parameter flagship model from LLaMA 3.3, designed for state-of-the-art language understanding and generation.",
                "type": "text"
            },
            {
                "id": "deepseek-r1",
                "name": "DeepSeek R1",
                "description": "DeepSeek AIs foundational model focused on reasoning, language understanding, and long-context comprehension.",
                "type": "text"
            },
            {
                "id": "deepseek-v3",
                "name": "DeepSeek V3",
                "description": "DeepSeek AIs third-generation model with enhanced reasoning and coding abilities.",
                "type": "text"
            },
            {
                "id": "qwen-2.5-72b",
                "name": "Qwen 2.5 72B",
                "description": "Large instruction-tuned language model from Qwen 2.5 family, optimized for complex NLP tasks.",
                "type": "text"
            },
            {
                "id": "gemma-2-27b",
                "name": "Gemma 2 27B",
                "description": "Googles instruction-tuned model with 27B parameters, capable of high-performance natural language understanding.",
                "type": "text"
            },
            {
                "id": "grok-3",
                "name": "Grok 3",
                "description": "xAI's general-purpose large language model designed for reasoning, conversation, and alignment.",
                "type": "text"
            },
            {
                "id": "grok-3-fast",
                "name": "Grok 3 (Fast)",
                "description": "A low-latency version of Grok 3 optimized for responsiveness and quick task execution.",
                "type": "text"
            },
            {
                "id": "grok-3-mini",
                "name": "Grok 3 Mini",
                "description": "A smaller variant of Grok 3 designed for lighter inference while maintaining core capabilities.",
                "type": "text"
            },
            {
                "id": "grok-3-mini-fast",
                "name": "Grok 3 Mini (Fast)",
                "description": "Fast and lightweight variant of Grok 3 Mini for extremely low-latency use cases.",
                "type": "text"
            },
            {
                "id": "grok-2-1212",
                "name": "Grok 2 1212",
                "description": "An earlier generation Grok model from xAI, optimized for general language tasks with improved efficiency.",
                "type": "text"
            },
            {
                "id": "fal-ai/fast-sdxl",
                "name": "Fast SDXL",
                "description": "A fast and efficient image generation model from the SDXL family, optimized for high-quality outputs.",
                "type": "image"
            }
        ]
        
        return models
    
    @staticmethod
    def select_model(id):
        if id == "llama-4-maverick-17b":
            options = ['meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8']
            model = random.choice(options)
            return model
        elif id == "llama-4-scout-17b":
            options = ['meta-llama/Llama-4-Scout-17B-16E-Instruct', 'meta-llama/llama-4-scout-17b-16e-instruct']
            model = random.choice(options)
            return model
        elif id == "llama-3.1-8b":
            options = ['llama-3.1-8b-instant']
            model = random.choice(options)
            return model
        elif id == "llama-3.3-70b":
            options = ['meta-llama/Llama-3.3-70B-Instruct-Turbo', 'llama-3.3-70b-versatile', 'meta-llama/Llama-3.3-70B-Instruct-Turbo']
            model = random.choice(options)
            return model
        elif id == "deepseek-r1":
            options = ['deepseek-ai/DeepSeek-R1', 'deepseek-r1-distill-llama-70b', 'deepseek-ai/DeepSeek-R1-Turbo', 'deepseek-ai/DeepSeek-R1-Distill-Llama-70B', 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B']
            model = random.choice(options)
            return model
        elif id == "deepseek-v3":
            options = ['deepseek-ai/DeepSeek-V3']
            model = random.choice(options)
            return model
        elif id == "qwen-2.5-72b":
            options = ['Qwen/Qwen2.5-VL-72B-Instruct', 'Qwen/Qwen2.5-72B-Instruct']
            model = random.choice(options)
            return model
        elif id == "gemma-2-27b":
            options = ['google/gemma-2-27b-it']
            model = random.choice(options)
            return model
        elif id == "grok-3":
            options = ['grok-3']
            model = random.choice(options)
            return model
        elif id == "grok-3-fast":
            options = ['grok-3-fast']
            model = random.choice(options)
            return model
        elif id == "grok-3-mini":
            options = ['grok-3-mini']
            model = random.choice(options)
            return model
        elif id == "grok-3-mini-fast":
            options = ['grok-3-mini-fast']
            model = random.choice(options)
            return model
        elif id == "grok-2-1212":
            options = ['grok-2-1212']
            model = random.choice(options)
            return model