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Create main.py

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  1. main.py +53 -0
main.py ADDED
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+ from fastapi import FastAPI, HTTPException
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+ from pydantic import BaseModel
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+ import requests
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+ import json
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+
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+ app = FastAPI()
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+
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+ # Model and API Key validation
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+ valid_api_keys = ['PARTH-SADARIA-NI-API-CHAWI', 'HEET-NI-CHEESEWADI-API-KEY']
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+ model_aliases = {
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+ 'Llama-3.1-Nemotron-70B-Instruct': 'nvidia/Llama-3.1-Nemotron-70B-Instruct',
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+ 'Meta-Llama-3.1-8B-Instruct': 'meta-llama/Meta-Llama-3.1-8B-Instruct',
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+ 'Meta-Llama-3.1-70B-Instruct': 'meta-llama/Meta-Llama-3.1-70B-Instruct',
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+ 'Meta-Llama-3.1-70B-Instruct-Turbo': 'meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo',
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+ 'Meta-Llama-3.1-405B-Instruct': 'meta-llama/Meta-Llama-3.1-405B-Instruct',
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+ 'Llama-3.2-11B-Vision-Instruct': 'meta-llama/Llama-3.2-11B-Vision-Instruct',
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+ 'Meta-Llama-3-8B-Instruct': 'meta-llama/Meta-Llama-3-8B-Instruct',
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+ }
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+
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+ class Payload(BaseModel):
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+ model: str
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+ messages: list
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+
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+ @app.post("/api/v1/chat/completions")
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+ async def get_completion(payload: Payload):
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+ api_key = payload.headers.get("Authorization")
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+
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+ # API Key validation
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+ if api_key not in valid_api_keys:
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+ raise HTTPException(status_code=403, detail="Forbidden: Invalid API key")
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+
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+ # Model alias resolution
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+ user_model = payload.model
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+ full_model_name = model_aliases.get(user_model, user_model)
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+
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+ is_deepinfra_model = full_model_name in model_aliases.values()
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+
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+ # Determine the URL to send the request to
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+ url = "https://api.deepinfra.com/v1/openai/chat/completions" if is_deepinfra_model else "https://gpt.tiptopuni.com/api/openai/v1/chat/completions"
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+
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+ # Send the API request to the appropriate service
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+ response = requests.post(url, json={**payload.dict(), "model": full_model_name})
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+
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+ if response.status_code != 200:
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+ raise HTTPException(status_code=response.status_code, detail="Failed to fetch data")
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+
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+ # Return the response from the model API
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+ return response.json()
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+
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+ # Run the server with Uvicorn using the 'main' module
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+ if __name__ == "__main__":
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+ import uvicorn
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+ uvicorn.run(app, host="0.0.0.0", port=8000)