import gradio as gr
import tensorflow as tf
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": {"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
  img = img.resize((128, 128))
  img = np.array(img) / 255.0  # Normalize pixel values
  img = img.reshape((1, 128, 128, 1))  # Add channel dimension
  return img

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 > 0.68 else "Female"
  list = "{:.2f}".format(age_pred),gender,df
  return list



# Gradio Interface with separate outputs
text_age = gr.components.Textbox(label="Predicted Age")
text_gender = gr.components.Textbox(label="Predicted Gender")

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()