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README.md

@@ -1,12 +1,6 @@
 ---
 title: BCancer
-emoji: 😻
-colorFrom: red
-colorTo: blue
+app_file: app.py
 sdk: gradio
 sdk_version: 3.35.2
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+
+
+
+
+
+# Initial parameters for pretrained model
+IMG_SIZE = 300
+
+labelsBreast = {'Benign':0,
+                'Malignant':1,
+                'Normal':2}
+
+# Load the model from the H5 file
+model = tf.keras.models.load_model('model/BreastCancer.h5')
+
+# Define the prediction function
+def predict(img):
+    img_height = 224
+    img_width = 224
+
+    # Convert the NumPy array to a PIL Image object
+    pil_img = Image.fromarray(img)
+
+    # Resize the image using the PIL Image object
+    pil_img = pil_img.resize((img_height, img_width))
+
+    # Convert the PIL Image object to a NumPy array
+    x = tf.keras.preprocessing.image.img_to_array(pil_img)
+
+    x = x.reshape(1, img_height, img_width, 3)
+    np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
+
+
+    predi = model.predict(x)
+    accuracy_of_class = '{:.1f}'.format(predi[0][np.argmax(predi)] * 100) + "%"
+    classes = list(labelsBreast.keys())[np.argmax(predi)]
+    context = {
+        'predictedLabel': classes,
+        # 'y_class': y_class,
+        # 'z_class': z_class,
+        'accuracy_of_class': accuracy_of_class
+    }
+
+
+   
+    return context
+
+
+
+demo = gr.Interface(fn=predict, inputs="image", outputs="text" , examples=[["b1.png"],["b2.png"]],)
+
+demo.launch()
+

model/BreastCancer.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9a2d31cc5eed3d9ad783f825e5233ad0c983d033de76d28581bb510d3d2d67ff
+size 177263680

requirements.txt

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+tensorflow
+numpy
+pillow