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app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from keras.preprocessing.image import img_to_array
+
+# Load the pre-trained model
+model = tf.keras.models.load_model("student.h5")
+
+# Define the class names
+class_names = ["Diger", "MuhammetAliSimsek", "MuserrefSelcukOzdemir", "ZekeriyyaKoroglu"]
+
+# Function to preprocess the image for model prediction
+def preprocess_image(image_path):
+    img = Image.open(image_path).convert("RGB")
+    img = img.resize((224, 224))  # Ensure the image size matches the model input size
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array  # Normalize the pixel values
+
+# Streamlit App
+st.title("Student Recognition App")
+
+# Upload image through Streamlit
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
+
+    # Preprocess the uploaded image
+    input_image = preprocess_image(uploaded_file)
+
+    # Make prediction using the model
+    predictions = model.predict(input_image)
+
+    # Get the predicted class
+    predicted_class_index = np.argmax(predictions)
+    predicted_class = class_names[predicted_class_index]
+
+    # Display the prediction result
+    st.write("Prediction Result:")
+    st.write(f"The person in the image is predicted as: {predicted_class}")

requiremenets.txt

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+streamlit==1.23.1
+pandas==1.5.3
+plotly==5.0.0
+tensorflow==2.12.0
+matplotlib==3.7.0

student.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ea60f8dab75295cec5a58ffc9b139146b860cd9f58f9ce38c025c18832b4aa8
+size 23218904