app.py

import os
from fastapi import FastAPI, HTTPException, Query
from fastapi.responses import StreamingResponse
from openai import AsyncOpenAI

app = FastAPI()
system = '''You are DeepSeek R1, an advanced reasoning assistant.
Your responses consist of two parts:

1. A <thinking> block — This is your internal reasoning. You think step-by-step, carefully analyzing the question, considering context, alternatives, and edge cases. This section must be at least 10 lines long and enclosed between <think> and </think>. This part is not shown to the user in real-world applications, but is visible during debugging or development.
2. The final answer — This is the polished, professional response provided after you’ve thought through the problem. It is clear, structured, and concise.
3. always provide code in this foramte ```<code>```.
Your behavior guidelines:
Maintain a calm, analytical, and formal tone.
Use bullet points or numbered lists when appropriate.
Avoid casual language, emojis, or redundant filler.
If context is missing, mention assumptions.
Never refer to yourself as an AI or language model.
Do not repeat the <thinking> part in your final answer.
Format every response exactly as follows:
<think>  
[Begin detailed, line-by-line reasoning here — minimum 10 lines. Think aloud.]  
</think>  
[Final answer starts here — no label, just a clean professional response.]  '''
# Define available models (you can expand this list)
AVAILABLE_MODELS = {
  "openai/gpt-4.1": "OpenAI GPT-4.1",
  "openai/gpt-4.1-mini": "OpenAI GPT-4.1-mini",
  "openai/gpt-4.1-nano": "OpenAI GPT-4.1-nano",
  "openai/gpt-4o": "OpenAI GPT-4o",
  "openai/gpt-4o-mini": "OpenAI GPT-4o mini",
  "openai/o4-mini": "OpenAI o4-mini",
  "microsoft/MAI-DS-R1": "MAI-DS-R1",
  "microsoft/Phi-3.5-MoE-instruct": "Phi-3.5-MoE instruct (128k)",
  "microsoft/Phi-3.5-mini-instruct": "Phi-3.5-mini instruct (128k)",
  "microsoft/Phi-3.5-vision-instruct": "Phi-3.5-vision instruct (128k)",
  "microsoft/Phi-3-medium-128k-instruct": "Phi-3-medium instruct (128k)",
  "microsoft/Phi-3-medium-4k-instruct": "Phi-3-medium instruct (4k)",
  "microsoft/Phi-3-mini-128k-instruct": "Phi-3-mini instruct (128k)",
  "microsoft/Phi-3-small-128k-instruct": "Phi-3-small instruct (128k)",
  "microsoft/Phi-3-small-8k-instruct": "Phi-3-small instruct (8k)",
  "microsoft/Phi-4": "Phi-4",
  "microsoft/Phi-4-mini-instruct": "Phi-4-mini-instruct",
  "microsoft/Phi-4-multimodal-instruct": "Phi-4-multimodal-instruct",
  "ai21-labs/AI21-Jamba-1.5-Large": "AI21 Jamba 1.5 Large",
  "ai21-labs/AI21-Jamba-1.5-Mini": "AI21 Jamba 1.5 Mini",
  "mistral-ai/Codestral-2501": "Codestral 25.01",
  "cohere/Cohere-command-r": "Cohere Command R",
  "cohere/Cohere-command-r-08-2024": "Cohere Command R 08-2024",
  "cohere/Cohere-command-r-plus": "Cohere Command R+",
  "cohere/Cohere-command-r-plus-08-2024": "Cohere Command R+ 08-2024",
  "deepseek/DeepSeek-R1": "DeepSeek-R1",
  "deepseek/DeepSeek-V3-0324": "DeepSeek-V3-0324",
  "meta/Llama-3.2-11B-Vision-Instruct": "Llama-3.2-11B-Vision-Instruct",
  "meta/Llama-3.2-90B-Vision-Instruct": "Llama-3.2-90B-Vision-Instruct",
  "meta/Llama-3.3-70B-Instruct": "Llama-3.3-70B-Instruct",
  "meta/Llama-4-Maverick-17B-128E-Instruct-FP8": "Llama 4 Maverick 17B 128E Instruct FP8",
  "meta/Llama-4-Scout-17B-16E-Instruct": "Llama 4 Scout 17B 16E Instruct",
  "meta/Meta-Llama-3.1-405B-Instruct": "Meta-Llama-3.1-405B-Instruct",
  "meta/Meta-Llama-3.1-70B-Instruct": "Meta-Llama-3.1-70B-Instruct",
  "meta/Meta-Llama-3.1-8B-Instruct": "Meta-Llama-3.1-8B-Instruct",
  "meta/Meta-Llama-3-70B-Instruct": "Meta-Llama-3-70B-Instruct",
  "meta/Meta-Llama-3-8B-Instruct": "Meta-Llama-3-8B-Instruct",
  "mistral-ai/Ministral-3B": "Ministral 3B",
  "mistral-ai/Mistral-Large-2411": "Mistral Large 24.11",
  "mistral-ai/Mistral-Nemo": "Mistral Nemo",
  "mistral-ai/Mistral-large-2407": "Mistral Large (2407)",
  "mistral-ai/Mistral-small": "Mistral Small",
  "cohere/cohere-command-a": "Cohere Command A",
  "core42/jais-30b-chat": "JAIS 30b Chat",
  "mistral-ai/mistral-small-2503": "Mistral Small 3.1"
}


async def generate_ai_response(prompt: str, model: str):
    # Configuration for unofficial GitHub AI endpoint
    token = os.getenv("GITHUB_TOKEN")
    if not token:
        raise HTTPException(status_code=500, detail="GitHub token not configured")
    
    endpoint = "https://models.github.ai/inference"
    
    # Validate the model
    if model not in AVAILABLE_MODELS:
        raise HTTPException(status_code=400, detail=f"Model not available. Choose from: {', '.join(AVAILABLE_MODELS.keys())}")

    client = AsyncOpenAI(base_url=endpoint, api_key=token)

    try:
        stream = await client.chat.completions.create(
            messages=[
                {"role": "system", "content": system},
                {"role": "user", "content": prompt}
            ],
            model=model,
            temperature=1.0,
            top_p=1.0,
            stream=True
        )

        async for chunk in stream:
            if chunk.choices and chunk.choices[0].delta.content:
                yield chunk.choices[0].delta.content

    except Exception as err:
        yield f"Error: {str(err)}"
        raise HTTPException(status_code=500, detail="AI generation failed")

@app.post("/generate")
async def generate_response(
    prompt: str = Query(..., description="The prompt for the AI"),
    model: str = Query("openai/gpt-4.1-mini", description="The model to use for generation")
):
    if not prompt:
        raise HTTPException(status_code=400, detail="Prompt cannot be empty")
    
    return StreamingResponse(
        generate_ai_response(prompt, model),
        media_type="text/event-stream"
    )

def get_app():
    return app