Update app.py

app.py

@@ -1,23 +1,20 @@
 import os
 import json
-import logging
-import boto3
 from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
+from pydantic import BaseModel
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import asyncio
-import concurrent.futures
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+import boto3
+import logging
+from huggingface_hub import hf_hub_download
 
+# Configuraciones de AWS y Hugging Face
 AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
 AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
 AWS_REGION = os.getenv("AWS_REGION")
 S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME")
+HUGGINGFACE_HUB_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
 
-MAX_TOKENS = 1024  # Limite de tokens por fragmento
-
+# Cliente de S3
 s3_client = boto3.client(
     's3',
     aws_access_key_id=AWS_ACCESS_KEY_ID,
@@ -27,16 +24,10 @@ s3_client = boto3.client(
 
 app = FastAPI()
 
-PIPELINE_MAP = {
-    "text-generation": "text-generation",
-    "sentiment-analysis": "sentiment-analysis",
-    "translation": "translation",
-    "fill-mask": "fill-mask",
-    "question-answering": "question-answering",
-    "text-to-speech": "text-to-speech",
-    "text-to-video": "text-to-video",
-    "text-to-image": "text-to-image"
-}
+class GenerateRequest(BaseModel):
+    model_name: str
+    input_text: str
+    task_type: str
 
 class S3DirectStream:
     def __init__(self, bucket_name):
@@ -48,140 +39,130 @@ class S3DirectStream:
         )
         self.bucket_name = bucket_name
 
-    async def stream_from_s3(self, key):
-        loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(None, self._stream_from_s3, key)
-
-    def _stream_from_s3(self, key):
+    async def download_and_upload_to_s3(self, model_name):
         try:
-            response = self.s3_client.get_object(Bucket=self.bucket_name, Key=key)
-            return response['Body']
-        except self.s3_client.exceptions.NoSuchKey:
-            raise HTTPException(status_code=404, detail=f"El archivo {key} no existe en el bucket S3.")
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Error al descargar {key} desde S3: {str(e)}")
+            model_name = model_name.replace("/", "-").lower()
 
-    async def get_model_file_parts(self, model_name):
-        loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(None, self._get_model_file_parts, model_name)
+            # Descargar el archivo config.json desde Hugging Face
+            config_file = hf_hub_download(repo_id=model_name, filename="config.json", token=HUGGINGFACE_HUB_TOKEN)
+            tokenizer_file = hf_hub_download(repo_id=model_name, filename="tokenizer.json", token=HUGGINGFACE_HUB_TOKEN)
+
+            # Verificar si el archivo ya existe en S3
+            if not await self.file_exists_in_s3(f"{model_name}/config.json"):
+                with open(config_file, "rb") as file:
+                    self.s3_client.put_object(Bucket=self.bucket_name, Key=f"{model_name}/config.json", Body=file)
+
+            if not await self.file_exists_in_s3(f"{model_name}/tokenizer.json"):
+                with open(tokenizer_file, "rb") as file:
+                    self.s3_client.put_object(Bucket=self.bucket_name, Key=f"{model_name}/tokenizer.json", Body=file)
 
-    def _get_model_file_parts(self, model_name):
-        try:
-            model_prefix = model_name.lower()
-            files = self.s3_client.list_objects_v2(Bucket=self.bucket_name, Prefix=model_prefix)
-            model_files = [obj['Key'] for obj in files.get('Contents', []) if model_prefix in obj['Key']]
-            return model_files
         except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Error al obtener archivos del modelo {model_name} desde S3: {e}")
+            logging.error(f"Error al cargar el modelo desde Hugging Face a S3: {e}")
+            raise HTTPException(status_code=500, detail=f"Error al cargar el modelo: {str(e)}")
 
-    async def load_model_from_s3(self, model_name):
+    async def file_exists_in_s3(self, s3_key):
         try:
-            profile, model = model_name.split("/", 1) if "/" in model_name else ("", model_name)
+            self.s3_client.head_object(Bucket=self.bucket_name, Key=s3_key)
+            return True
+        except self.s3_client.exceptions.ClientError:
+            return False
 
-            model_prefix = f"{profile}/{model}".lower()
-            model_files = await self.get_model_file_parts(model_prefix)
+    async def load_model_from_s3(self, model_name):
+        try:
+            model_name = model_name.replace("/", "-").lower()
+            model_files = await self.get_model_file_parts(model_name)
 
             if not model_files:
-                raise HTTPException(status_code=404, detail=f"Archivos del modelo {model_name} no encontrados en S3.")
-
-            config_stream = await self.stream_from_s3(f"{model_prefix}/config.json")
-            config_data = config_stream.read()
+                await self.download_and_upload_to_s3(model_name)
 
-            if not config_data:
-                raise HTTPException(status_code=500, detail=f"El archivo de configuración {model_prefix}/config.json está vacío.")
+            # Cargar configuración del modelo desde S3
+            config_data = await self.stream_from_s3(f"{model_name}/config.json")
+            if isinstance(config_data, bytes):
+                config_data = config_data.decode("utf-8")
             
-            config_text = config_data.decode("utf-8")
-            config_json = json.loads(config_text)
+            config_json = json.loads(config_data)
 
-            model = AutoModelForCausalLM.from_pretrained(f"s3://{self.bucket_name}/{model_prefix}", config=config_json, from_tf=False)
+            # Cargar el modelo
+            model = AutoModelForCausalLM.from_pretrained(f"s3://{self.bucket_name}/{model_name}", config=config_json)
             return model
 
         except HTTPException as e:
             raise e
         except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Error al cargar el modelo desde S3: {e}")
+            logging.error(f"Error al cargar el modelo desde S3: {e}")
+            raise HTTPException(status_code=500, detail=f"Error al cargar el modelo desde S3: {str(e)}")
 
     async def load_tokenizer_from_s3(self, model_name):
         try:
-            profile, model = model_name.split("/", 1) if "/" in model_name else ("", model_name)
-
-            tokenizer_stream = await self.stream_from_s3(f"{profile}/{model}/tokenizer.json")
-            tokenizer_data = tokenizer_stream.read().decode("utf-8")
+            model_name = model_name.replace("/", "-").lower()
+            tokenizer_data = await self.stream_from_s3(f"{model_name}/tokenizer.json")
 
-            tokenizer = AutoTokenizer.from_pretrained(f"{profile}/{model}")
+            if isinstance(tokenizer_data, bytes):
+                tokenizer_data = tokenizer_data.decode("utf-8") 
+            
+            tokenizer = AutoTokenizer.from_pretrained(f"s3://{self.bucket_name}/{model_name}")
             return tokenizer
         except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Error al cargar el tokenizer desde S3: {e}")
+            logging.error(f"Error al cargar el tokenizer desde S3: {e}")
+            raise HTTPException(status_code=500, detail=f"Error al cargar el tokenizer desde S3: {str(e)}")
 
-    async def create_s3_folders(self, s3_key):
+    async def stream_from_s3(self, key):
         try:
-            folder_keys = s3_key.split('/')
-            for i in range(1, len(folder_keys)):
-                folder_key = '/'.join(folder_keys[:i]) + '/'
-                if not await self.file_exists_in_s3(folder_key):
-                    self.s3_client.put_object(Bucket=self.bucket_name, Key=folder_key, Body='')
-
+            response = self.s3_client.get_object(Bucket=self.bucket_name, Key=key)
+            return response['Body'].read()
+        except self.s3_client.exceptions.NoSuchKey:
+            raise HTTPException(status_code=404, detail=f"El archivo {key} no existe en el bucket S3.")
         except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Error al crear carpetas en S3: {e}")
+            raise HTTPException(status_code=500, detail=f"Error al descargar {key} desde S3: {str(e)}")
 
-    async def file_exists_in_s3(self, s3_key):
+    async def get_model_file_parts(self, model_name):
         try:
-            self.s3_client.head_object(Bucket=self.bucket_name, Key=s3_key)
-            return True
-        except self.s3_client.exceptions.ClientError:
-            return False
+            model_name = model_name.replace("/", "-").lower()
+            files = self.s3_client.list_objects_v2(Bucket=self.bucket_name, Prefix=model_name)
+            model_files = [obj['Key'] for obj in files.get('Contents', []) if model_name in obj['Key']]
+            return model_files
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Error al obtener archivos del modelo {model_name} desde S3: {str(e)}")
 
-def split_text_by_tokens(text, tokenizer, max_tokens=MAX_TOKENS):
-    tokens = tokenizer.encode(text)
-    chunks = []
-    for i in range(0, len(tokens), max_tokens):
-        chunk = tokens[i:i+max_tokens]
-        chunks.append(tokenizer.decode(chunk))
-    return chunks
-
-def continue_generation(input_text, model, tokenizer, max_tokens=MAX_TOKENS):
-    generated_text = ""
-    while len(input_text) > 0:
-        tokens = tokenizer.encode(input_text)
-        input_text = tokenizer.decode(tokens[:max_tokens])
-        output = model.generate(input_ids=tokenizer.encode(input_text, return_tensors="pt").input_ids)
-        generated_text += tokenizer.decode(output[0], skip_special_tokens=True)
-        input_text = input_text[len(input_text):]  # Si la entrada se agotó, ya no hay más que procesar
-    return generated_text
-
-@app.post("/predict/")
-async def predict(model_request: dict):
+@app.post("/generate")
+async def generate(request: GenerateRequest):
     try:
-        model_name = model_request.get("model_name")
-        task = model_request.get("pipeline_task")
-        input_text = model_request.get("input_text")
-
-        if not model_name or not task or not input_text:
-            raise HTTPException(status_code=400, detail="Faltan parámetros en la solicitud.")
-
-        streamer = S3DirectStream(S3_BUCKET_NAME)
-        model = await streamer.load_model_from_s3(model_name)
-        tokenizer = await streamer.load_tokenizer_from_s3(model_name)
-
-        if task not in PIPELINE_MAP:
-            raise HTTPException(status_code=400, detail="Pipeline task no soportado")
-
-        nlp_pipeline = pipeline(PIPELINE_MAP[task], model=model, tokenizer=tokenizer)
-
-        result = await asyncio.to_thread(nlp_pipeline, input_text)
-
-        chunks = split_text_by_tokens(result, tokenizer)
+        model_name = request.model_name
+        input_text = request.input_text
+        task_type = request.task_type
+        
+        s3_direct_stream = S3DirectStream(S3_BUCKET_NAME)
+        
+        model = await s3_direct_stream.load_model_from_s3(model_name)
+        tokenizer = await s3_direct_stream.load_tokenizer_from_s3(model_name)
+        
+        if task_type == "text-to-text":
+            generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+            result = generator(input_text, max_length=1024, num_return_sequences=1)
+            return {"result": result[0]["generated_text"]}
+
+        elif task_type == "text-to-image":
+            generator = pipeline("text-to-image", model=model, tokenizer=tokenizer, device=0)
+            image = generator(input_text)
+            return {"result": image}
+
+        elif task_type == "text-to-speech":
+            generator = pipeline("text-to-speech", model=model, tokenizer=tokenizer, device=0)
+            audio = generator(input_text)
+            return {"result": audio}
+
+        elif task_type == "text-to-video":
+            generator = pipeline("text-to-video", model=model, tokenizer=tokenizer, device=0)
+            video = generator(input_text)
+            return {"result": video}
 
-        if len(chunks) > 1:
-            full_result = ""
-            for chunk in chunks:
-                full_result += continue_generation(chunk, model, tokenizer)
-            return JSONResponse(content={"result": full_result})
         else:
-            return JSONResponse(content={"result": result})
+            raise HTTPException(status_code=400, detail="Tipo de tarea no soportada")
 
+    except HTTPException as e:
+        raise e
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error al realizar la predicción: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
 
 if __name__ == "__main__":
     import uvicorn