first commit

Dockerfile

@@ -0,0 +1,28 @@
+FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu20.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   libgl1 \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+WORKDIR /code
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/server
+COPY --chown=user . $HOME/server
+
+CMD ["uvicorn", "api.main:api", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -6,5 +6,3 @@ colorTo: indigo
 sdk: docker
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

api/core/app.py

@@ -0,0 +1,70 @@
+class Demos:
+    def __init__(self):
+        from fastapi import FastAPI, HTTPException, Response
+        self.api = FastAPI
+        self.exception = HTTPException
+        self.api_response = Response
+    def validate_apikey(self,api_key)->bool:
+        __validation = True
+        return __validation
+    @staticmethod
+    def text_to_img(texto:str=None, model:str="PROMPTHERO")->bytes:
+        """Genera un BITARRAY de la imagen con el texto dado.
+
+        args:
+            texto           (str)   :   Texto para generar imagen
+        return:
+            _img            (str)   :   Imagen en BITARRAY
+        """
+        
+        from api.core.controllers.text2image import Generador
+        _imagen = str()
+        if "RUNWAY" in model.upper():
+            _imagen = Generador.using_runway_sd_15(prompt=texto)
+        elif "STABILITY" in model.upper():
+            _imagen = Generador.using_stability_sd_21(prompt=texto)
+        elif "REALISTIC" in model.upper():
+            _imagen = Generador.using_realistic_v14(prompt=texto)
+        elif "PROMPTHERO" in model.upper():
+            _imagen = Generador.using_prompthero_openjourney(prompt=texto)
+        else:
+            _imagen = bytes("error", 'utf-8')
+        return _imagen
+    @staticmethod
+    def text_to_video(texto:str=None)->str:
+        """Genera un BITARRAY del video con el texto dado.
+
+        args:
+            texto           (str)   :   Texto para generar video
+        return:
+            _video          (str)   :   Video en BITARRAY
+        """
+        _video = str()
+        return _video
+    @staticmethod
+    def text_to_speach(texto:str=None)->str:
+        """Genera un BITARRAY del audio con el texto dado.
+
+        args:
+            texto           (str)   :   Texto para generar audio
+        return:
+            _speach         (str)   :   Audio en BITARRAY
+        """
+        _speach = str()
+        return _speach
+    @staticmethod
+    def image_to_image(task:str="MSLD", image:str=None, mask:str=None,**kwargs)->str:
+        """Genera una imagen a partir de una imagen
+
+        args:
+            task            (str)   :   Modelo a utilizar: MSLD, DEEP, SCRIBLE, etc..
+            image           (str)   :   Input Image
+            mask            (str)   :   Mask Image
+            **kwargs        (str)   :   Argumentos adicionales: inference, strnght, guidance...
+        return:
+            _image          (str)   :   Imagen en BITARRAY
+        """
+        _image = str()
+        return _image
+    
+

api/core/controllers/text2image.py

@@ -0,0 +1,51 @@
+from diffusers import DiffusionPipeline as Pipe
+import torch
+
+class Generador:
+    def img_to_bytes(image) -> bytes:
+        import io
+        _imgByteArr = io.BytesIO()
+        image.save(_imgByteArr, format="png")
+        return _imgByteArr.getvalue()
+    def using_runway_sd_15(prompt:str)->bytes:
+        try:            
+            _generador = Pipe.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+            _generador.to("cuda")
+            _imagen = _generador(prompt).images[0]
+            _response = bytes(Generador.img_to_bytes(image=_imagen))
+        except Exception as e:
+            _response = bytes(str(e), 'utf-8')
+        finally:
+            return _response
+    def using_stability_sd_21(prompt:str)->bytes:
+        try:
+            _generador = Pipe.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
+            _generador.to("cuda")
+            _imagen = _generador(prompt).images[0]
+            _response = bytes(Generador.img_to_bytes(image=_imagen))
+        except Exception as e:
+            _response = bytes(str(e), 'utf-8')
+        finally:
+            return _response
+    def using_realistic_v14(prompt:str)->bytes:
+        try:
+            _generador = Pipe.from_pretrained("SG161222/Realistic_Vision_V1.4", torch_dtype=torch.float16)
+            _generador.to("cuda")
+            _imagen = _generador(prompt).images[0]
+            _response = bytes(Generador.img_to_bytes(image=_imagen))
+        except Exception as e:
+            _response = bytes(str(e), 'utf-8')
+        finally:
+            return _response
+    def using_prompthero_openjourney(prompt:str)->bytes:
+        try:
+            _generador = Pipe.from_pretrained("prompthero/openjourney", torch_dtype=torch.float16)
+            _generador.to("cuda")
+            _imagen = _generador(prompt).images[0]
+            _response = bytes(Generador.img_to_bytes(image=_imagen))
+        except Exception as e:
+            print(e)
+            _response = bytes(str(e), 'utf-8')
+        finally:
+            return _response
+    

api/core/controllers/text2text.py

@@ -0,0 +1,43 @@
+from transformers import pipeline as Pipe
+
+class Traductor:
+    def EN_ES(texto:str)->str:
+        try:
+            _traductor = Pipe("text2text-generation", model="Helsinki-NLP/opus-mt-en-es")
+            _traduccion = _traductor(texto)[0]
+            _response = _traduccion.get('generated_text')
+        except Exception as e:
+            _response = str(e)
+        finally:
+            return _response
+    
+    def ES_EN(texto:str)->str:
+        try:
+            _traductor = Pipe("text2text-generation", model="Helsinki-NLP/opus-mt-es-en")
+            _traduccion = _traductor(texto)[0]
+            _response = _traduccion.get('generated_text')
+        except Exception as e:
+            _response = str(e)
+        finally:
+            return _response
+    
+    def AR_ES(texto:str)->str:
+        try:
+            _traductor = Pipe("text2text-generation", model="Helsinki-NLP/opus-mt-ar-es")
+            _traduccion = _traductor(texto)[0]
+            _response = _traduccion.get('generated_text')
+        except Exception as e:
+            _response = str(e)
+        finally:
+            return _response
+
+class Abstractor:
+    def resumen(texto:str)->str:
+        try:
+            _abstractor = Pipe("text2text-generation", model="facebook/bart-large-cnn")
+            _resumen = _abstractor(texto)[0]
+            _response = _resumen.get('generated_text')
+        except Exception as e:
+            _response = str(e)
+        finally:
+            return _response

api/main.py

@@ -0,0 +1,57 @@
+from api.core.app import Demos
+__main = Demos()
+api = __main.api()
+
+@api.post("/texto_a_imagen/", status_code=201, responses = {201: {"content": {"image/png": {}}}} ,response_class=__main.api_response)
+def get_text2img(data:dict):
+    __response=dict({"request_data":data})
+    try:
+        if data and 'texto' in data and 'modelo' in data:
+            __response['original']= data.get('texto')
+            __image =  __main.text_to_img(texto=data.get('texto'), 
+                                          model=data.get('modelo'))
+        else:
+            raise __main.exception(status_code = 401, datail=f"Datos mal formados:\n{data}")
+    except Exception as e:
+        print(e)
+        #To-do ->agregar mas información en el error fecha, usuario, reqs
+        raise __main.exception(status_code = 403, datail=str(e))
+    finally:
+        return __main.api_response(content=__image, media_type="image/png")
+
+@api.post("/texto_a_video/", status_code=201)
+def get_text2video(data:dict) -> dict:
+    __response=dict({"request_data":data})
+    try:
+        if data:
+            __response['original']= data.get('texto')
+            __response['video']= __main.text_to_video(texto=data.get('texto'))
+        else:
+            raise __main.exception(status_code = 401, datail=f"Datos mal formados:\n{data}")
+    except Exception as e:
+        print(e)
+        #To-do ->agregar mas información en el error fecha, usuario, reqs
+        raise __main.exception(status_code = 403, datail=e)
+    finally:
+        return __response
+
+@api.post("/texto_a_audio/", status_code=201)
+def get_text2speach(data:dict) -> dict:
+    __response=dict({"request_data":data})
+    try:
+        if data:
+            __response['original']= data.get('texto')
+            __response['audio']= __main.text_to_speach(texto=data.get('texto'))
+        else:
+            raise __main.exception(status_code = 401, datail=f"Datos mal formados:\n{data}")
+    except Exception as e:
+        print(e)
+        #To-do ->agregar mas información en el error fecha, usuario, reqs
+        raise __main.exception(status_code = 403, datail=e)
+    finally:
+        return __response
+
+
+
+
+    

requirements.txt

@@ -0,0 +1,29 @@
+torch
+torchvision 
+torchaudio
+invisible_watermark
+datasets
+hf-doc-builder
+huggingface-hub
+Jinja2
+librosa
+numpy
+scipy
+tensorboard
+omegaconf
+pytorch-lightning
+xformers
+
+fastapi
+pydantic
+uvicorn
+typing
+requests
+bs4
+transformers
+transformers[sentencepiece]
+diffusers
+diffusers[torch]
+diffusers[flax]
+accelerate
+safetensors