Update app.py

app.py

@@ -1,133 +1,79 @@
 from PIL import Image, ImageOps
 import numpy as np
-from collections import OrderedDict
 import seaborn as sns
 import matplotlib.pyplot as plt
 import pandas as pd
 from keras.models import load_model
 import gradio as gr
 
+# Load the model and class names outside the prediction function
+model = load_model('keras_model.h5', compile=False)
+class_names = [line.strip() for line in open('labels.txt', 'r')]
 
 def create_plot(data):
-  sns.set_theme(style="whitegrid")
+    sns.set_theme(style="whitegrid")
 
-  f, ax = plt.subplots(figsize=(5, 5))
+    f, ax = plt.subplots(figsize=(5, 5))
 
-  sns.set_color_codes("pastel")
-  sns.barplot(x="Total", y="Labels", data=data,label="Total", color="b")
+    sns.set_color_codes("pastel")
+    sns.barplot(x="Total", y="Labels", data=data, label="Total", color="b")
 
-  sns.set_color_codes("muted")
-  sns.barplot(x="Confidence Score", y="Labels", data=data,label="Conficence Score", color="b")
-
-  ax.legend(ncol=2, loc="lower right", frameon=True)
-  sns.despine(left=True, bottom=True)
-  return f
+    sns.set_color_codes("muted")
+    sns.barplot(x="Confidence Score", y="Labels", data=data, label="Conficence Score", color="b")
 
+    ax.legend(ncol=2, loc="lower right", frameon=True)
+    sns.despine(left=True, bottom=True)
+    return f
 
 def predict_tumor(img):
-  np.set_printoptions(suppress=True)
-  model = load_model('keras_model.h5', compile=False)
-  class_names = open('labels.txt', 'r').readlines()
-  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-  
-  # image = Image.open(img).convert('RGB')
-  image = img
-  size = (224, 224)
-  image_PIL = Image.fromarray(image)
-  image = ImageOps.fit(image_PIL, size, Image.LANCZOS)
-  image_array = np.asarray(image)
-  normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-  data[0] = normalized_image_array
-  prediction = model.predict(data)
-  index = np.argmax(prediction)
-  class_name = class_names[index]
-  confidence_score = prediction[0][index]
-  
-  c_name = (class_name[2:])[:-1]
-  if c_name == "Yes":
-    tumor_prediction = "Model detected Tumor"
-    other_class = "No"
-  else:
-    other_class = "Yes"
-    tumor_prediction = "Model did not detect Tumor"
- 
-  res = {"Labels":[c_name,other_class], "Confidence Score":[(confidence_score*100),(1-confidence_score)*100],"Total":100}
-  data_for_plot = pd.DataFrame.from_dict(res)
-
-  tumor_conf_plt = create_plot(data_for_plot)
-  return tumor_prediction,tumor_conf_plt
+    np.set_printoptions(suppress=True)
+    
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 
-css = """
-footer {display:none !important}
-.output-markdown{display:none !important}
-footer {visibility: hidden}
-.hover\:bg-orange-50:hover {
-    --tw-bg-opacity: 1 !important;
-    background-color: rgb(229,225,255) !important;
-}
+    # Resize and preprocess the input image
+    size = (224, 224)
+    image_PIL = Image.fromarray(img)
+    image = ImageOps.fit(image_PIL, size, Image.LANCZOS)
+    image_array = np.asarray(image)
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+    data[0] = normalized_image_array
+    
+    # Make a prediction
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+    
+    c_name = class_name.strip()
+    tumor_prediction = f"Model {'detected' if c_name == 'Yes' else 'did not detect'} Tumor"
+    other_class = 'No' if c_name == 'Yes' else 'Yes'
+    
+    # Prepare data for plotting
+    res = {"Labels": [c_name, other_class], "Confidence Score": [(confidence_score * 100), (1 - confidence_score) * 100], "Total": 100}
+    data_for_plot = pd.DataFrame.from_dict(res)
 
-img.gr-sample-image:hover, video.gr-sample-video:hover {
-    --tw-border-opacity: 1;
-    border-color: rgb(37, 56, 133) !important;
-}
+    tumor_conf_plt = create_plot(data_for_plot)
+    
+    return tumor_prediction, tumor_conf_plt
 
-.gr-button-lg {
-    z-index: 14;
-    width: 113px;
-    height: 30px;
-    left: 0px;
-    top: 0px;
-    padding: 0px;
-    cursor: pointer !important; 
-    background: none rgb(17, 20, 45) !important;
-    border: none !important;
-    text-align: center !important;
-    font-size: 14px !important;
-    font-weight: 500 !important;
-    color: rgb(255, 255, 255) !important;
-    line-height: 1 !important;
-    border-radius: 6px !important;
-    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
-    box-shadow: none !important;
-}
-.gr-button-lg:hover{
-    z-index: 14;
-    width: 113px;
-    height: 30px;
-    left: 0px;
-    top: 0px;
-    padding: 0px;
-    cursor: pointer !important; 
-    background: none rgb(66, 133, 244) !important;
-    border: none !important;
-    text-align: center !important;
-    font-size: 14px !important;
-    font-weight: 500 !important;
-    color: rgb(255, 255, 255) !important;
-    line-height: 1 !important;
-    border-radius: 6px !important;
-    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
-    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
-}
 
-"""
-  
-with gr.Blocks(title="Brain Tumor Detection | Data Science Dojo", css = css) as demo:
-  with gr.Row():
-    with gr.Column(scale=4):
-      with gr.Row():
-          imgInput = gr.Image()
-    with gr.Column(scale=1):
-      tumor = gr.Textbox(label='Presence of Tumor')
-      plot = gr.Plot(label="Plot")
-      
-  submit_button = gr.Button(value="Submit")
-  submit_button.click(fn=predict_tumor, inputs=[imgInput], outputs=[tumor,plot])
+# Gradio Interface
+with gr.Blocks(title="Brain Tumor Detection | Data Science Dojo", css="styles.css") as demo:
+    with gr.Row():
+        with gr.Column(scale=4):
+            with gr.Row():
+                imgInput = gr.Image()
+        with gr.Column(scale=1):
+            tumor = gr.Textbox(label='Presence of Tumor')
+            plot = gr.Plot(label="Plot")
+            
+    submit_button = gr.Button(value="Submit")
+    submit_button.click(fn=predict_tumor, inputs=[imgInput], outputs=[tumor, plot])
 
-  gr.Examples(
-        examples=["pred2.jpg","pred3.jpg"],
+    gr.Examples(
+        examples=["pred2.jpg", "pred3.jpg"],
         inputs=imgInput,
-        outputs=[tumor,plot],
+        outputs=[tumor, plot],
         fn=predict_tumor,
         cache_examples=True,
     )