Duplicate from blackphin/kidney-detection-model

Co-authored-by: Shivansh Goel <blackphin@users.noreply.huggingface.co>

.gitattributes

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+*h5 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.venv
+__pycache__
+*.docx

Model/Model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61102bc6b12bbb86693920bdca6962d66ba34b6ab3002fd5eb03cc2e1269be22
+size 135395432

README.md

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+---
+title: Kidney Detection Model
+emoji: 🌍
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: blackphin/kidney-detection-model
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import sys
+import os
+
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import cv2
+
+import tensorflow as tf
+from tensorflow.keras import backend as K
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.imagenet_utils import preprocess_input
+from tensorflow.keras.applications.imagenet_utils import preprocess_input
+
+from sklearn.metrics import f1_score
+
+
+import gradio as gr
+
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+sns.set_style('darkgrid')
+if not sys.warnoptions:
+    import warnings
+    warnings.simplefilter("ignore")
+pd.set_option('display.max_columns', None)  # or 1000
+pd.set_option('display.max_rows', None)  # or 1000
+pd.set_option('display.max_colwidth', None)  # or 199
+print('Modules loaded')
+
+
+def F1_score(y_true, y_pred):  # taken from old keras source code
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    recall = true_positives / (possible_positives + K.epsilon())
+    f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())
+    return f1_val
+
+
+model = load_model(r"Model\Model.h5",
+                   custom_objects={"F1_score": f1_score})
+
+
+def recog_model(img_path):
+    img = cv2.imread(img_path)
+    img = cv2.resize(img, (250, 224))
+    x = np.expand_dims(img, axis=0)
+    x = preprocess_input(x)
+
+    prediction = model.predict(x)
+
+    # convert the prediction to a class label
+    classes = ['Tumor', 'Cyst', 'Normal', 'Stone']
+    predicted_class = classes[np.argmax(prediction[0])]
+    confidence = str(100 * (np.max(prediction[0])))
+    return (str(predicted_class+" detected with a confidence of "+confidence+"%"))
+
+
+demo = gr.Interface(fn=recog_model, inputs=gr.Image(image_mode="L", type="filepath", label="Input Image"),
+                    outputs=gr.Label(label="Model Prediction"), allow_flagging="never", examples=[r"demo\Cyst.jpg", r"demo\Normal.jpg", r"demo\Stone.jpg", r"demo\Tumor.jpg"])
+
+demo.launch()