File size: 8,225 Bytes
a94ab76 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
import gc
import torch
import os
from typing import Optional
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
app = FastAPI()
# CORS ayarları - React uygulamanızın çalıştığı port'a izin verin
app.add_middleware(
CORSMiddleware,
allow_origins=["http://localhost:5173", "http://localhost:3000"], # Vite ve CRA portları
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global variables
current_model = None
current_pipe = None
current_model_name = None
class GenerateRequest(BaseModel):
model_name: str
prompt: str
system_prompt: str
max_tokens: int = 512
temperature: float = 0.75
top_p: float = 0.95
top_k: int = 64
image: Optional[str] = None # Base64 encoded image
class GenerateResponse(BaseModel):
generated_text: str
model_used: str
def load_model(model_path: str):
global current_model, current_pipe, current_model_name
# Return existing pipeline if the model is already loaded
if current_pipe is not None and current_model_name == model_path:
return current_pipe
print(f"Unloading previous model to load: {model_path}")
# Cleanup previous model
if current_model is not None:
del current_model
if current_pipe is not None:
del current_pipe
gc.collect()
torch.cuda.empty_cache()
try:
if "Gemma3-1B" in model_path:
print("Loading Gemma 3 1B with PEFT...")
base_model_name = "unsloth/gemma-3-1b-it"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map="auto",
dtype="auto"
)
model = PeftModel.from_pretrained(base_model, model_path)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
current_model = model
elif "Gemma3-12B" in model_path:
print("Loading Gemma 3 12B with PEFT (Image Support)...")
base_model_name = "unsloth/gemma-3-12b-it"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map="auto",
dtype="auto"
)
model = PeftModel.from_pretrained(base_model, model_path)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
current_model = model
elif "Qwen2.5-3B" in model_path:
print("Loading Qwen 2.5 3B...")
pipe = pipeline("text-generation", model=model_path, device=0)
current_model = pipe.model # Keep reference for simple consistency
elif "Llama3.1-8B" in model_path:
print("Loading Llama 3.1 8B...")
pipe = pipeline("text-generation", model=model_path, device=0)
current_model = pipe.model
else:
print(f"Unknown model pattern for {model_path}, trying default pipeline loading...")
pipe = pipeline("text-generation", model=model_path, device=0)
current_model = pipe.model
current_pipe = pipe
current_model_name = model_path
return pipe
except Exception as e:
print(f"Error loading model {model_path}: {str(e)}")
raise HTTPException(status_code=500, detail=f"Model loading failed: {str(e)}")
# Initialize with default 1B model
default_model = "Chan-Y/TurkishReasoner-Gemma3-1B"
try:
load_model(default_model)
except Exception as e:
print(f"Initial model loading failed (might be expected in dev env): {e}")
@app.get("/")
def read_root():
return {"message": "Turkish AI Backend API is running"}
@app.get("/models")
def get_models():
"""Return available models"""
return {
"models": [
{
"name": "Gemma 3 1B Turkish Reasoning",
"path": "Chan-Y/TurkishReasoner-Gemma3-1B",
"supportsImages": False
},
{
"name": "Gemma 3 12B Turkish (Supports Images)",
"path": "Chan-Y/TurkishReasoner-Gemma3-12B",
"supportsImages": True
},
{
"name": "Qwen 2.5 3B Turkish Reasoning",
"path": "Chan-Y/TurkishReasoner-Qwen2.5-3B",
"supportsImages": False
},
{
"name": "Llama 3.1 8B Turkish Reasoning",
"path": "Chan-Y/TurkishReasoner-Llama3.1-8B",
"supportsImages": False
}
]
}
@app.post("/generate", response_model=GenerateResponse)
async def generate_text(request: GenerateRequest):
"""Generate text using the model"""
global current_pipe
try:
# Load requested model if different
pipe = load_model(request.model_name)
# Prepare message content
user_content = [{"type": "text", "text": request.prompt}]
# Add image if provided and supported
if request.image and "Gemma3-12B" in request.model_name:
# Assuming the image string is a data:image/jpeg;base64,... URI
# Pipeline might expect a PIL image or a URL or strictly formatted dict
# Standard transformers pipeline behavior for image:
# {"type": "image", "image": "base64_string_or_url"}
user_content.insert(0, {"type": "image", "image": request.image})
messages = [
{
"role": "system",
"content": [{"type": "text", "text": request.system_prompt}]
},
{
"role": "user",
"content": user_content
},
]
# Clean up system prompt if empty or not supported by some models?
# Standard chat templates usually handle system prompts.
print(f"Generating with {request.model_name}, temp={request.temperature}")
output = pipe(
messages,
max_new_tokens=request.max_tokens,
temperature=request.temperature,
top_p=request.top_p,
top_k=request.top_k
)
generated_text = output[0]["generated_text"][-1]["content"]
return GenerateResponse(
generated_text=generated_text,
model_used=request.model_name
)
except Exception as e:
print(f"Error during generation: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/generate/stream")
async def generate_text_stream(request: GenerateRequest):
"""
Streaming endpoint for real-time generation
(Not implemented in this version - would use Server-Sent Events)
"""
raise HTTPException(status_code=501, detail="Streaming not yet implemented")
# --- Static Files Serving (for Deployment) ---
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
# Check if static directory exists (it will in Docker)
static_dir = "static"
if os.path.exists(static_dir):
app.mount("/assets", StaticFiles(directory=f"{static_dir}/assets"), name="assets")
# Catch-all for SPA (serve index.html)
@app.get("/{full_path:path}")
async def serve_spa(full_path: str):
# Allow API routes to pass through (though they match specifically defined routes first)
if full_path.startswith("api") or full_path.startswith("generate") or full_path.startswith("models"):
raise HTTPException(status_code=404, detail="Not found")
# Serve index.html for everything else
return FileResponse(f"{static_dir}/index.html")
else:
print("Static directory not found. Running in API-only mode.")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |