Upload 3 files

app.py

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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}")

epoch_26.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d91b274f183a50abecef91d624f3124125e4fde244ce24be497a4d099bfc10d8
+size 130887608

requirements.txt

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+streamlit
+tensorflow==2.18.0
+opencv-python
+numpy
+pillow
+requests
+plotly