mimic

app.py

@@ -11,30 +11,9 @@ from fastapi.openapi.docs import get_swagger_ui_html
 import os
 import requests
 from modules.audio import convert, get_audio_duration
-from modules.r2 import upload_to_s3, upload_image_to_s3
+from modules.r2 import upload_to_s3
 import threading
 import queue
-from diffusers import DiffusionPipeline
-import torch
-from datetime import datetime
-import random
-import numpy as np
-
-SAVE_DIR = "saved_images"  
-if not os.path.exists(SAVE_DIR):
-    os.makedirs(SAVE_DIR, exist_ok=True)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-repo_id = "black-forest-labs/FLUX.1-dev"
-adapter_id = "guardiancc/lora"
-
-pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
-pipeline.load_lora_weights(adapter_id)
-pipeline.enable_sequential_cpu_offload()
-pipeline = pipeline.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
 
 vpv_webhook = os.environ.get("VPV_WEBHOOK")
 
@@ -47,34 +26,6 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-
-def save_generated_image(image):
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    unique_id = str(uuid.uuid4())[:8]
-    filename = f"{timestamp}_{unique_id}.png"
-    filepath = os.path.join(SAVE_DIR, filename)
-    image.save(filepath)
-    
-    return filepath
-
-def inference_image(prompt):
-    seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-    image = pipeline(
-        prompt=prompt,
-        guidance_scale=3.5,
-        num_inference_steps=20,
-        width=512,
-        height=512,
-        generator=generator,
-        joint_attention_kwargs={"scale": 0.8},
-    ).images[0]
-    
-    filepath = save_generated_image(image, prompt)
-    url = upload_image_to_s3(filepath, os.path.basename(filepath), "png")
-    os.unlink(filepath)
-    return url
-    
 def download_file(url: str) -> str:
     """
     Baixa um arquivo da URL fornecida e o salva no diretório 'downloads/'.
@@ -109,7 +60,6 @@ async def openapi():
     with open("swagger.json") as f:
         return json.load(f)
 
-
 class ProcessRequest(BaseModel):
     key: str
     text: str
@@ -121,15 +71,9 @@ class ProcessRequest(BaseModel):
     format: str = "wav"
     speed: float = 0.8
     crossfade: float = 0.1
-    
-class ProcessImage(BaseModel):
-    prompt: str
-    id: str
-    receiver: str
-    webhook: str
+
 
 q = queue.Queue()
-image_queue = queue.Queue()
 
 def process_queue(q):
     while True:
@@ -154,32 +98,9 @@ def process_queue(q):
             print(e)
         finally:
             q.task_done()
-            
-def process_image(q):
-    while True:
-        try:
-            prompt, id, receiver, webhook = q.get(timeout=5)  
-            image = inference_image(prompt)
-                
-            payload = {
-                    "id": id,
-                    "receiver": receiver,
-                    "url": image,
-                    "type": "image"
-                }
-                
-            requests.post(webhook, json=payload)
-        except Exception as e:
-            print(e)
-        finally:
-            q.task_done()
-
         
 worker_thread = threading.Thread(target=process_queue, args=(q,))
 worker_thread.start()
-
-imagge_worker = threading.Thread(target=process_queue, args=(q,))
-imagge_worker.start()
         
 @app.post("/process")
 def process_audio(payload: ProcessRequest):
@@ -227,47 +148,6 @@ def process_audio(payload: ProcessRequest):
         requests.post(dc_callback, headers=headers, data=json.dumps(data))
         raise HTTPException(status_code=500, detail=str(e))
 
-@app.post("/image")
-def process_image(payload: ProcessImage):
-    prompt = payload.prompt
-    id = payload.id
-    receiver = payload.receiver
-    webhook = payload.webhook
-    
-    if len(prompt) <= 5:
-        raise HTTPException(status_code=500, detail=str(e))
-
-    try:
-        image_queue.put(( prompt, id, receiver, webhook))
-        return {"success": True, "err": ""}
-        
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-    
-    except Exception as e:
-        error_trace = traceback.format_exc() 
-        dc_callback = "https://discord.com/api/webhooks/1285586984898662511/QNVvY2rtoKICamlXsC1BreBaYjS9341jz9ANCDBzayXt4C7v-vTFzKfUtKQkwW7BwpfP"
-        
-        data = {
-            "content": "",
-            "tts": False,
-            "embeds": [
-                {
-                    "type": "rich",
-                    "title": f"Erro aconteceu na IA - MIMIC - 2 ia",
-                    "description": f"Erro: {str(e)}\n\nDetalhes do erro:\n```{error_trace}```"
-                }
-            ]
-        }
-        
-        headers = {
-            "Content-Type": "application/json",
-            "Accept": "application/json",
-        }
-        requests.post(dc_callback, headers=headers, data=json.dumps(data))
-        raise HTTPException(status_code=500, detail=str(e))
-
-
 class TrainRequest(BaseModel):
     audio: HttpUrl 
     key: str