File size: 3,103 Bytes
facad14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | """
Devrayog AI - Main API Server
FastAPI-based API for Devrayog AI models
"""
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional
import time
import os
app = FastAPI(
title="Devrayog AI API",
description="India's First Solo-Built AI - API Server",
version="0.1.0"
)
# CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Request/Response models
class ChatRequest(BaseModel):
message: str
model: str = "devrayog-agni"
max_tokens: int = 512
temperature: float = 0.7
system_prompt: Optional[str] = None
class ChatResponse(BaseModel):
response: str
model: str
tokens_used: int
time_taken: float
class ModelInfo(BaseModel):
name: str
description: str
parameters: str
status: str
# Routes
@app.get("/")
async def root():
return {
"name": "Devrayog AI",
"version": "0.1.0",
"status": "running",
"message": "Welcome to Devrayog AI - India's First Solo-Built AI",
"endpoints": {
"chat": "/api/v1/chat",
"models": "/api/v1/models",
"health": "/health"
}
}
@app.get("/health")
async def health():
return {"status": "healthy", "timestamp": time.time()}
@app.get("/api/v1/models")
async def list_models():
return {
"models": [
{
"id": "devrayog-agni",
"name": "Devrayog Agni",
"description": "Fast and sharp - optimized for quick responses",
"parameters": "3.8B",
"status": "coming_soon"
},
{
"id": "devrayog-vayu",
"name": "Devrayog Vayu",
"description": "Creative and flowing - optimized for storytelling",
"parameters": "9B",
"status": "coming_soon"
},
{
"id": "devrayog-akash",
"name": "Devrayog Akash",
"description": "Most powerful - optimized for complex reasoning",
"parameters": "8B",
"status": "coming_soon"
}
]
}
@app.post("/api/v1/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
start_time = time.time()
# For now, return a placeholder response
# Once models are deployed, this will call the actual model
response = f"[Devrayog AI - {request.model}] Thank you for your message: '{request.message}'. This is a placeholder response. The actual model will be deployed soon on Hugging Face."
time_taken = time.time() - start_time
return ChatResponse(
response=response,
model=request.model,
tokens_used=len(response.split()),
time_taken=round(time_taken, 3)
)
if __name__ == "__main__":
import uvicorn
port = int(os.environ.get("PORT", 8000))
uvicorn.run(app, host="0.0.0.0", port=port)
|