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app.py
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app.py
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import tensorflow as tf
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import streamlit as st
# Load the Keras model
model = load_model("keras_model.h5")
# Load the labels from the text file
with open("labels.txt", "r") as file:
class_names = file.readlines()
class_names = [name.strip() for name in class_names]
# Function to preprocess the image
def preprocess_image(image_path, target_size=(224, 224)):
image = Image.open(image_path)
image = image.resize(target_size)
image = np.array(image)
image = image / 255.0 # Normalize the image data
image = np.expand_dims(image, axis=0) # Add batch dimension
return image
# Get the image filename from the user
image_name = st.text_input("Enter Image name", "sushanth-style.jpg")
# Preprocess the image
image_data = preprocess_image(image_name)
# Make prediction
prediction = model.predict(image_data)
predicted_class_index = np.argmax(prediction)
predicted_class_name = class_names[predicted_class_index]
# Map the predicted class name to 'human' or 'other'
if predicted_class_name in ['male', 'female']:
predicted_class_name = 'human'
else:
predicted_class_name = 'other'
# Display the image
st.image(image_name, caption="Input Image", use_column_width=True)
# Display the predicted class
st.write("Predicted Class:", predicted_class_name)