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import os
import streamlit as st
from PIL import Image
from predictor import predict_image
APP_DIR = os.path.dirname(os.path.abspath(__file__))
ASSETS_DIR = os.path.join(APP_DIR, "assets")
# 📌 PAGE SETUP
st.set_page_config(page_title="Image Classifier App", page_icon="🤖", layout="centered")
st.html("""
<style>
.stMainBlockContainer {
max-width: 70rem;
}
</style>
""")
# 📌 INITIALIZE SESSION STATE
if "uploaded_image" not in st.session_state:
st.session_state["uploaded_image"] = None
if "example_selected" not in st.session_state:
st.session_state["example_selected"] = False
if "prediction_result" not in st.session_state:
st.session_state["prediction_result"] = None
# 📌 MAIN APP LAYOUT
with st.container():
st.title(
body="🖼️ Image Classifier with CNN",
help="An interactive application to classify images into over 1000 categories.",
)
st.html("<br>")
# Use tabs for different sections of the app
tab_app, tab_description = st.tabs(["**App**", "**Description**"])
# 📌 APP TAB
with tab_app:
# Create a two-column layout for the app interface
col_upload, col_results = st.columns(2, gap="large")
# 📌 IMAGE UPLOAD & EXAMPLE SELECTION
with col_upload:
st.header("Upload an Image", divider=True)
# File uploader widget
uploaded_file = st.file_uploader(
label="Drag and drop an image here or click to browse",
type=["jpg", "jpeg", "png"],
help="Maximum file size is 200MB",
key="image_uploader",
)
st.html("<br>")
st.subheader("Or Try an Example", divider=True)
# Segmented control for selecting example images
selected_example = st.segmented_control(
label="Categories",
options=["Animal", "Vehicle", "Object", "Building"],
default=None,
help="Select one of the pre-loaded examples",
)
st.html("<br>")
# --- THE SINGLE CLASSIFY BUTTON ---
classify_button = st.button(
label="Classify Image",
key="classify_btn",
type="primary",
icon="✨",
)
# --- LOGIC FOR IMAGE SELECTION & PREDICTION ---
# Clear the previous prediction result if a new input is selected
if uploaded_file or selected_example:
st.session_state.prediction_result = None
image_to_process = None
if uploaded_file:
image_to_process = Image.open(uploaded_file)
elif selected_example:
try:
img_path = os.path.join(
APP_DIR, "assets", f"{selected_example.lower()}.jpg"
)
image_to_process = Image.open(img_path)
except FileNotFoundError:
st.error(
f"Error: The example image '{selected_example.lower()}.jpg' was not found."
)
st.stop()
# 📌 PREDICTION RESULTS
with col_results:
st.header("Results", divider=True)
# Display a "get started" message if no image is selected
if not image_to_process and not st.session_state.prediction_result:
st.info("Choose an image or an example to get a prediction.")
# Display the image if one is selected
if image_to_process:
st.image(image_to_process, caption="Image to be classified")
# If the button is clicked, run the prediction logic
if classify_button and image_to_process:
with st.spinner("Analyzing image..."):
try:
from predictor import predict_image
predicted_label, predicted_score = predict_image(
image_to_process
)
st.session_state.prediction_result = {
"label": predicted_label.replace("_", " ").title(),
"score": predicted_score,
}
except Exception as e:
st.error(f"An error occurred during prediction: {e}")
# Display the prediction result if available in session state
if st.session_state.prediction_result:
st.metric(
label="Prediction",
value=st.session_state.prediction_result["label"],
delta=f"{st.session_state.prediction_result['score'] * 100:.2f}%",
help="The predicted category and its confidence score.",
delta_color="normal",
)
st.balloons()
elif image_to_process:
st.info("Click 'Classify Image' to see the prediction.")
# 📌 DESCRIPTION TAB
with tab_description:
st.header("About This Project", divider=True)
st.markdown(
"""
This project showcases a Convolutional Neural Network (CNN) model that automatically
classifies images into over 1000 different categories.
### Original Architecture
The original project was built as a multi-service architecture, featuring:
* **Streamlit:** For the web user interface.
* **FastAPI:** As a RESTful API to handle image processing and model serving.
* **Redis:** A message broker for communication between the services.
### Portfolio Adaptation
For a live and cost-effective demo, this application has been adapted into a single-service
solution. The core logic of the FastAPI backend has been integrated directly into
the Streamlit app. This demonstrates the ability to adapt a solution for
specific deployment and resource constraints.
### Technologies Used
* **Streamlit:** For the interactive web interface.
* **TensorFlow:** For loading and running the pre-trained CNN model.
* **Pre-trained Model:** ResNet50 with weights trained on the ImageNet dataset.
"""
)
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