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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
from io import BytesIO
import requests
import plotly.express as px
# Set page configuration
st.set_page_config(page_title="Scene Classifier", layout="wide")
# Load the model
@st.cache_resource
def load_model():
try:
return tf.keras.models.load_model('epoch_26.h5')
except:
st.error("Model file not found. Please make sure 'model_epoch_11.h5' is in the same directory as this script.")
return None
model = load_model()
# Define class names based on your dataset
class_names = ['buildings', 'forest', 'glacier', 'mountain','human', 'sea', 'street']
def preprocess_image(img):
"""Preprocess image for prediction"""
img = img.convert('RGB')
img = img.resize((224, 224))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
img_array = img_array / 255.0 # Normalize the image
return img_array
def predict_and_display(img):
"""Make prediction and display results with confidence scores"""
# Preprocess the image
img_array = preprocess_image(img)
# Make prediction
predictions = model.predict(img_array)[0] # Get the first (and only) prediction
# Get predicted class and confidence
predicted_index = np.argmax(predictions)
predicted_class = class_names[predicted_index]
confidence_score = predictions[predicted_index] * 100 # Convert to percentage
# Display predicted label and confidence
st.markdown(f"### **Predicted:** {predicted_class} ({confidence_score:.2f}%)")
st.markdown(f"🧠 Model is **{confidence_score:.2f}%** confident that the image is a **{predicted_class}**.")
# Create a dataframe for plotting
confidence_data = {"Category": class_names, "Confidence (%)": [score * 100 for score in predictions]}
# Create a bar chart
fig = px.bar(
confidence_data,
x="Confidence (%)",
y="Category",
orientation="h",
text_auto=".2f",
title="Confidence Scores",
color="Category",
color_discrete_sequence=px.colors.qualitative.Set1
)
fig.update_traces(textposition="outside") # Show confidence values outside bars
fig.update_layout(
yaxis={"categoryorder": "total ascending"}, # Sort by confidence
height=600, # Increase chart height
width=700, # Increase chart width
margin=dict(l=30, r=30, t=50, b=50) # Adjust margins for better spacing
)
# Display image & graph side by side
col1, col2 = st.columns([1, 1])
with col1:
img = img.resize((512, 512))
st.image(img)
with col2:
st.plotly_chart(fig, use_container_width=True)
# Streamlit app UI
st.title("🌄 Scene Classifier")
st.markdown("""
This app classifies images into one of the following categories:
- 🏢 Buildings 🌲 Forest ❄️ Glacier ⛰️ Mountain 🌊 Sea 🛣️ Street 🚹 Human
""")
# Create tabs for URL input and file upload
tab1, tab2 = st.tabs(["Upload Image", "Enter URL"])
with tab1:
uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
try:
img = Image.open(uploaded_file)
if st.button("Classify Uploaded Image"):
predict_and_display(img)
except Exception as e:
st.error(f"Error processing the image: {e}")
with tab2:
image_url = st.text_input("Enter Image URL:")
if st.button("Classify from URL") and image_url:
try:
response = requests.get(image_url)
if response.status_code == 200:
img = Image.open(BytesIO(response.content))
predict_and_display(img)
else:
st.error(f"Error fetching the image. Status code: {response.status_code}")
except Exception as e:
st.error(f"Error processing the URL: {e}") |