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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-04-28T20:21:26.291595Z","iopub.execute_input":"2023-04-28T20:21:26.292023Z","iopub.status.idle":"2023-04-28T20:21:26.317278Z","shell.execute_reply.started":"2023-04-28T20:21:26.291985Z","shell.execute_reply":"2023-04-28T20:21:26.315920Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"/kaggle/input/classi/ClassRF 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install gradio==3.14.0","metadata":{"execution":{"iopub.status.busy":"2023-04-28T20:21:32.692713Z","iopub.execute_input":"2023-04-28T20:21:32.693170Z","iopub.status.idle":"2023-04-28T20:21:52.069848Z","shell.execute_reply.started":"2023-04-28T20:21:32.693126Z","shell.execute_reply":"2023-04-28T20:21:52.067992Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Collecting gradio==3.14.0\n Downloading gradio-3.14.0-py3-none-any.whl (13.8 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting ffmpy\n Downloading ffmpy-0.3.0.tar.gz (4.8 kB)\n Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: markdown-it-py[linkify,plugins] in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (2.1.0)\nCollecting httpx\n Downloading httpx-0.24.0-py3-none-any.whl (75 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websockets>=10.0 in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (11.0)\nCollecting python-multipart\n Downloading python_multipart-0.0.6-py3-none-any.whl (45 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: pydantic in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (1.10.4)\nRequirement already satisfied: orjson in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (3.8.5)\nRequirement already satisfied: uvicorn in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (0.20.0)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (3.1.2)\nRequirement already satisfied: aiohttp in /opt/conda/lib/python3.7/site-packages (from gradio==3.14.0) (3.8.3)\nRequirement already satisfied: pydub in /opt/conda/lib/python3.7/site-packages (from 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jsonschema>=3.0->altair->gradio==3.14.0) (5.10.2)\nRequirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema>=3.0->altair->gradio==3.14.0) (0.19.3)\nRequirement already satisfied: pkgutil-resolve-name>=1.3.10 in /opt/conda/lib/python3.7/site-packages (from jsonschema>=3.0->altair->gradio==3.14.0) (1.3.10)\nCollecting uc-micro-py\n Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->gradio==3.14.0) (1.16.0)\nRequirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.7/site-packages (from importlib-resources>=1.4.0->jsonschema>=3.0->altair->gradio==3.14.0) (3.11.0)\nBuilding wheels for collected packages: ffmpy\n Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4711 sha256=92ce06a3d5c39ef58497e3878c9ddbe018e646d1110a1e7af66a0440f631746d\n Stored in directory: /root/.cache/pip/wheels/c0/96/80/1aeaaf27265398a7393b296714b84e51a7d00101f8a665a25a\nSuccessfully built ffmpy\nInstalling collected packages: ffmpy, uc-micro-py, python-multipart, linkify-it-py, httpcore, httpx, gradio\nSuccessfully installed ffmpy-0.3.0 gradio-3.14.0 httpcore-0.17.0 httpx-0.24.0 linkify-it-py-1.0.3 python-multipart-0.0.6 uc-micro-py-1.0.1\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import gradio as gr\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport pickle\nfrom tensorflow.keras.models import load_model\n","metadata":{"execution":{"iopub.status.busy":"2023-04-28T20:22:05.210684Z","iopub.execute_input":"2023-04-28T20:22:05.211495Z","iopub.status.idle":"2023-04-28T20:22:05.220560Z","shell.execute_reply.started":"2023-04-28T20:22:05.211413Z","shell.execute_reply":"2023-04-28T20:22:05.218904Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Load the RGB to hyperspectral conversion model\nconverion_model = load_model('/kaggle/input/convmo/Conversion_model.h5')\n\n# Load the cancer classification model\n#cancer_model = pickle.load(open(\"/kaggle/input/classi/ClassRF (1).pkl\", \"rb\"))\n\ncancer_model = pickle.load(open(\"/kaggle/input/logistic/LRclass.pkl\", \"rb\"))","metadata":{"execution":{"iopub.status.busy":"2023-04-28T20:24:35.081173Z","iopub.execute_input":"2023-04-28T20:24:35.081743Z","iopub.status.idle":"2023-04-28T20:24:36.467983Z","shell.execute_reply.started":"2023-04-28T20:24:35.081694Z","shell.execute_reply":"2023-04-28T20:24:36.465793Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"def classify(rgb_image):\n img = Image.fromarray(rgb_image.astype('uint8'), 'RGB')\n img = img.resize((272, 512))\n arr = np.array(img).astype('float32') / 255.0\n new_size = (272, 512)\n resized_rgb_img = tf.image.resize(arr, new_size)\n resized_rgb_img = tf.reshape(resized_rgb_img, (272, 512, 3))\n resized_rgb_img = np.expand_dims(resized_rgb_img , axis=0)\n \n # Convert the RGB image to hyperspectral using your model\n hyperspectral_image = converion_model(resized_rgb_img)\n hyperspectral_image = tf.image.resize(hyperspectral_image, new_size)\n hyperspectral_image = tf.reshape(hyperspectral_image, (272, 512, 16))\n imgplot = hyperspectral_image.numpy().astype(np.float32)\n imgplot= imgplot.reshape(-1, 272*512*16)\n prediction = cancer_model.predict(imgplot)\n if np.argmax(prediction) == 0:\n x= \"cancer\"\n else:\n x=\"not a cancer\"\n return x ","metadata":{"execution":{"iopub.status.busy":"2023-04-28T20:24:50.438626Z","iopub.execute_input":"2023-04-28T20:24:50.440038Z","iopub.status.idle":"2023-04-28T20:24:50.449870Z","shell.execute_reply.started":"2023-04-28T20:24:50.439980Z","shell.execute_reply":"2023-04-28T20:24:50.448539Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"# Define the Gradio interface\n#image_input = gr.inputs.Image()\noutput_label = gr.components.Label()\n#output_label=[\"text\"]\nimage_input = gr.components.Image()\ngr.Interface(\n classify, \n image_input, \n output_label, \n title=\"RGB to Hyperspectral Conversion and Cancer Classification\", \n description=\"Upload an RGB image and get a prediction of whether you have skin cancer or not.\"\n).launch(share=True)","metadata":{"execution":{"iopub.status.busy":"2023-04-28T20:24:54.496632Z","iopub.execute_input":"2023-04-28T20:24:54.497067Z","iopub.status.idle":"2023-04-28T20:25:03.058479Z","shell.execute_reply.started":"2023-04-28T20:24:54.497029Z","shell.execute_reply":"2023-04-28T20:25:03.056997Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Running on local URL: http://127.0.0.1:7860\n\nSetting up a public link... we have recently upgraded the way public links are generated. If you encounter any problems, please report the issue and downgrade to gradio version 3.13.0\n.\nRunning on public URL: https://3d8a5098-9d6d-46ab.gradio.live\n\nThis share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co./spaces\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<div><iframe src=\"https://3d8a5098-9d6d-46ab.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"},"metadata":{}},{"execution_count":11,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]}]} |