Upload 4 files
Browse files- README.md +39 -0
- app.py +62 -0
- requirements.txt +86 -0
- yolov5s.pt +3 -0
README.md
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# cocoa_beans_interfaces
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Requisitos previos
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Python 3.8 o superior.
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Git para clonar el repositorio.
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1. Clonar el repositorio
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Primero, clona este repositorio:
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git clone https://github.com/kebincontreras/cocoa_beans_interfaces.git
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cd cocoa_beans_interfaces
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2. Crear un entorno virtual
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Para mantener las dependencias aisladas, es recomendable crear un entorno virtual.
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En Linux / macOS:
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python3 -m venv interfas_beans_cocoa
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source interfas_beans_cocoa/bin/activate
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En Windows:
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python -m venv interfas_beans_cocoa
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interfas_beans_cocoa\Scripts\activate
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3. Instalar dependencias
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Con el entorno virtual activado, instala las dependencias necesarias para el proyecto:
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pip install -r requirements.txt
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Este comando instalará todas las librerías especificadas en el archivo requirements.txt, que incluye Gradio, Torch, OpenCV y otras necesarias para la clasificación.
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4. Ejecutar la aplicación
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Con el entorno configurado y las dependencias instaladas, ejecuta la aplicación:
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python app.py
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Esto iniciará un servidor de Gradio que mostrará la interfaz para subir una imagen de granos de cacao y realizar la clasificación de niveles de fermentación.
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app.py
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import gradio as gr
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import cv2
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import torch
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from PIL import Image
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import numpy as np
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# Cargar el modelo YOLO (usando YOLOv5 como ejemplo)
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # Puedes cambiar 'yolov5s' por cualquier otro modelo
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# Función para realizar detección de objetos
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def detect_objects(image):
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# Convertir la imagen a un formato compatible con OpenCV
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image = np.array(image)
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# Hacer la detección con YOLO
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results = model(image)
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# Renderizar los resultados (dibujar las cajas de detección)
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results_image = results.render()[0]
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return Image.fromarray(results_image)
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# Interfaz de Gradio para cargar una imagen
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def gradio_interface():
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with gr.Blocks() as demo:
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# Título centrado
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gr.Markdown(
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"""
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<center>
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<h2>Fermentation Level Classification for Cocoa Beans</h2>
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</center>
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"""
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)
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# Botón GitHub centrado justo debajo del título
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gr.Markdown(
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"""
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<center>
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<a href="https://github.com/kebincontreras/cocoa_beans_interfaces" target="_blank" style="text-decoration: none;">
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<button style="background-color: #007bff; color: white; padding: 10px 20px; border: none; border-radius: 5px; font-size: 16px;">GitHub</button>
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</a>
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</center>
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"""
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)
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# Organizar imágenes en la misma fila
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with gr.Row():
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img_input = gr.Image(label="Upload Image")
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img_output = gr.Image(label="Image with Detected Objects")
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# Botón para aplicar la detección de objetos a la imagen subida
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btn_detect_upload = gr.Button("Classify Fermentation Level")
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# Conectar el botón con la función de detección de objetos
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btn_detect_upload.click(detect_objects, inputs=img_input, outputs=img_output)
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return demo
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# Ejecutar la aplicación
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if __name__ == "__main__":
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demo = gradio_interface()
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demo.launch()
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requirements.txt
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aiofiles==23.2.1
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annotated-types==0.7.0
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anyio==4.6.0
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certifi==2024.8.30
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charset-normalizer==3.3.2
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click==8.1.7
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contourpy==1.3.0
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cycler==0.12.1
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fastapi==0.115.0
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ffmpy==0.4.0
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filelock==3.16.1
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fonttools==4.54.1
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fsspec==2024.9.0
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gitdb==4.0.11
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GitPython==3.1.43
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gradio==4.44.0
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gradio_client==1.3.0
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h11==0.14.0
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httpcore==1.0.5
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httpx==0.27.2
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huggingface-hub==0.25.1
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idna==3.10
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importlib_resources==6.4.5
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Jinja2==3.1.4
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kiwisolver==1.4.7
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markdown-it-py==3.0.0
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MarkupSafe==2.1.5
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matplotlib==3.9.2
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mdurl==0.1.2
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mpmath==1.3.0
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networkx==3.3
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numpy==1.26.4
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nvidia-cublas-cu12==12.1.3.1
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nvidia-cuda-cupti-cu12==12.1.105
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nvidia-cuda-nvrtc-cu12==12.1.105
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nvidia-cuda-runtime-cu12==12.1.105
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nvidia-cudnn-cu12==9.1.0.70
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nvidia-cufft-cu12==11.0.2.54
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nvidia-curand-cu12==10.3.2.106
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nvidia-cusolver-cu12==11.4.5.107
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nvidia-cusparse-cu12==12.1.0.106
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nvidia-nccl-cu12==2.20.5
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nvidia-nvjitlink-cu12==12.6.68
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nvidia-nvtx-cu12==12.1.105
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opencv-python==4.10.0.84
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orjson==3.10.7
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packaging==24.1
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pandas==2.2.3
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pillow==10.4.0
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psutil==6.0.0
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py-cpuinfo==9.0.0
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pydantic==2.9.2
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pydantic_core==2.23.4
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pydub==0.25.1
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Pygments==2.18.0
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pyparsing==3.1.4
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python-dateutil==2.9.0.post0
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python-multipart==0.0.11
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pytz==2024.2
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PyYAML==6.0.2
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requests==2.32.3
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rich==13.8.1
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ruff==0.6.8
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scipy==1.14.1
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seaborn==0.13.2
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semantic-version==2.10.0
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setuptools==75.1.0
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shellingham==1.5.4
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six==1.16.0
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smmap==5.0.1
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sniffio==1.3.1
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starlette==0.38.6
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sympy==1.13.3
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tomlkit==0.12.0
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torch==2.4.1
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torchvision==0.19.1
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tqdm==4.66.5
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triton==3.0.0
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typer==0.12.5
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typing_extensions==4.12.2
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tzdata==2024.2
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ultralytics==8.2.102
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ultralytics-thop==2.0.8
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urllib3==2.2.3
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uvicorn==0.31.0
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websockets==12.0
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yolov5s.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee
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size 14808437
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