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import streamlit as st
from PIL import Image
from predictor import predict_image
# 📌 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
# We initialize session state variables to manage app state
if "selected_image" not in st.session_state:
st.session_state["selected_image"] = None
if "prediction_placeholder" not in st.session_state:
st.session_state["prediction_placeholder"] = {"label": "A Dog", "score": 0.9558}
# 📌 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_image = 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="Animal",
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="✨",
)
# 📌 PREDICTION RESULTS
with col_results:
st.header("Results", divider=True)
# This message is shown before any image is processed
if st.session_state["selected_image"] is None and not classify_button:
st.info("Choose an image to get a prediction.")
# If the button is clicked, run the prediction logic
if classify_button:
# Check if an image is selected before running prediction
if uploaded_image is not None:
# st.session_state["selected_image"] = uploaded_image
# Use Image.open() to convert the UploadedFile object into a PIL.Image object
st.session_state["selected_image"] = Image.open(uploaded_image)
st.session_state["uploaded_file"] = uploaded_image
elif selected_example:
# Load the selected example image
try:
img_path = f"./assets/{selected_example.lower()}.jpg"
st.session_state["selected_image"] = Image.open(img_path)
except FileNotFoundError:
st.error(
f"Error: The example image '{selected_example.lower()}.jpg' was not found."
)
st.stop()
if st.session_state["selected_image"] is not None:
st.image(
st.session_state["selected_image"],
caption="Image to be classified",
)
# Call the prediction function and display results
with st.spinner("Analyzing image..."):
# Call our modularized prediction function!
try:
predicted_label, predicted_score = predict_image(
st.session_state["selected_image"]
)
st.metric(
label="Prediction",
value=f"{predicted_label.replace('_', ' ').title()}",
delta=f"{predicted_score * 100:.2f}%",
help="The predicted category and its confidence score.",
delta_color="normal",
)
st.balloons()
except Exception as e:
st.error(f"An error occurred during prediction: {e}")
else:
st.error("Please upload an image or select an example to classify.")
# 📌 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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