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app.py

@@ -1,24 +1,27 @@
 import gradio as gr
 import tensorflow as tf
-from PIL import Image
 import numpy as np
 import os
 import pandas as pd
 
-
 model_gender = tf.keras.models.load_model('model_gender.h5')
 model_age = tf.keras.models.load_model('model_age.h5')
 
 actual_data = {
-    "000000.png": {"img": 1,"age": 85.0, "gender": "female"},
-    "000001.png": {"img": 2,"age": 72.0, "gender": "female"},
-    "000002.png": {"img": 3,"age": 45.0, "gender": "male"},
-    "000003.png": {"img": 4,"age": 59.0, "gender": "male"},
-    "000004.png": {"img": 5,"age": 37.0, "gender": "male"}
+    "000000.png": {"Image": 1,"age": 61.0, "gender": "Female"},
+    "000001.png": {"Image": 2,"age": 63.0, "gender": "Male"},
+    "000002.png": {"Image": 3,"age": 45.0, "gender": "Male"},
+    "000003.png": {"Image": 4,"age": 59.0, "gender": "Female"},
+    "000004.png": {"Image": 5,"age": 37.0, "gender": "Female"}
     }
 
 df = pd.DataFrame(actual_data).T
 
+data = {'Name': ['Accuracy', 'Precision', 'Recall', 'F1-score'],
+        'Value': [96.11 , 0.9368, 0.9731, 0.9546]}
+
+df1 = pd.DataFrame(data)
+
 def preprocess_image(image):
   # Assuming image is a PIL Image object from Gradio
   img = image.convert('L')  # Convert to grayscale
@@ -31,7 +34,7 @@ def predict(image):
   preprocessed_image = preprocess_image(image)
   gender_pred = model_gender.predict(preprocessed_image)[0][0]
   age_pred = model_age.predict(preprocessed_image)[0][0]
-  gender = "Male" if gender_pred > 0.7 else "Female"
+  gender = "Male" if gender_pred > 0.68 else "Female"
   list = "{:.2f}".format(age_pred),gender,df
   return list
 
@@ -41,15 +44,73 @@ def predict(image):
 text_age = gr.components.Textbox(label="Predicted Age")
 text_gender = gr.components.Textbox(label="Predicted Gender")
 
-interface = gr.Interface(predict, gr.components.Image(height=440,width=1000,label="Upload Image", type="pil"),
-                          outputs=[text_age, text_gender, gr.DataFrame(value=df)],
-                          examples=[
-                            os.path.join(os.path.dirname(__file__),"00000.png"),
-                            os.path.join(os.path.dirname(__file__),"00001.png"),
-                            os.path.join(os.path.dirname(__file__),"00002.png"),
-                            os.path.join(os.path.dirname(__file__),"00003.png"),
-                            os.path.join(os.path.dirname(__file__),"00004.png")],
-
-                          allow_flagging='never', 
-                          theme=gr.themes.Soft(),
-                          title="Age and Gender Prediction").launch()
+def predictor_tab():
+    interface = gr.Interface(predict, gr.components.Image(height=440,width=1000,label="Upload Image", type="pil"),
+                              outputs=[text_age, text_gender, gr.DataFrame(value=df)],
+                              examples=[
+                                os.path.join(os.path.dirname(__file__),"00000.png"),
+                                os.path.join(os.path.dirname(__file__),"00001.png"),
+                                os.path.join(os.path.dirname(__file__),"00002.png"),
+                                os.path.join(os.path.dirname(__file__),"00003.png"),
+                                os.path.join(os.path.dirname(__file__),"00004.png")],
+
+                              allow_flagging='never')
+                    
+    return interface
+
+def about_tab():
+    with gr.Blocks() as about:
+        # Title and Introduction
+        gr.Markdown("# Age and Gender Prediction with Deep Learning!")
+        gr.Markdown("This awesome app uses deep learning magic ✨ to predict someone's age and gender based on a x-ray image! Just upload a photo, and our clever models will do their best detective work to unveil the mystery.")
+        
+        # Dataset Section
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("**DATASET 🗃**")
+                gr.Markdown(
+                    """
+                    The lung scans used in this project come from a publicly available dataset. 
+                    It contains approximately 10,700 scans for training and 11,700 scans for testing. 
+                    This dataset was part of a competition held by The Radiology and Diagnostic Imaging Society of São Paulo (SPR) with Amazon Web Services.
+                    """
+                )
+                gr.Text("https://www.kaggle.com/datasets/felipekitamura/spr-x-ray-age-and-gender-dataset",label="link")
+
+        # Model Performance Section
+        gr.Markdown("**Model Performance 💡**")
+        table = gr.DataFrame(value=df1)
+        gr.Markdown("⚜ **Model Accuracy for Genders**")
+        gender_img = 'gender.png'
+        gr.Image(value=gender_img, width=500, height=450)
+        gr.Markdown("⚜ **Model Accuracy for Age**")
+        age_img = 'age.png'
+        gr.Image(value=age_img, width=500, height=450)
+        gr.Markdown("⚜ **Confusion matrix**")
+        matrix_img = 'matrix.png'
+        gr.Image(value=matrix_img, width=500, height=450)
+        
+        
+        
+         # Wrap the table in a Block for better formatting
+        # Creator Information Section
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("**Created by 🤓**")
+                gr.Markdown("Uday Jawheri")
+                with gr.Row():
+                    gr.Text("https://www.linkedin.com/in/uday-jawheri/", label="LinkedIn")
+                    gr.Text("https://xudayx.github.io/Portfolio/", label="Website")
+
+    return about
+
+
+with gr.Blocks(theme=gr.themes.Soft(), title="Age and Gender Prediction") as app:  # Consistent variable name 'app'
+    with gr.Tab("Predictor"):
+        predictor_tab()
+
+    with gr.Tab("About"):
+        about_tab()
+
+# Launch the Gradio app
+app.launch()