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app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the saved ResNet model
+model_resnet = []#tf.keras.models.load_model("path_to_your_saved_model/resnet_model.h5")
+
+# Set page configuration and layout
+st.set_page_config(
+    page_title="Image Classification App",
+    page_icon=":camera:",
+    layout="wide",
+)
+
+# Header logo
+st.image("RCAIoT_logo.png", use_column_width=False)
+
+# Main content
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    st.header("Upload Image")
+    uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"], key="upload_image")
+    if uploaded_image is not None:
+        # Display the uploaded image with fixed initial height and width using HTML/CSS
+        st.image(uploaded_image, caption="Uploaded Image", use_column_width=False, width=300)
+        st.markdown(
+            """
+            <style>
+            img {
+                max-height: 300px;
+            }
+            </style>
+            """,
+            unsafe_allow_html=True,
+        )
+
+with col2:
+    st.header("Results")
+
+    # Stylish Analyze and Reset buttons in a horizontal row
+    st.markdown(
+        """
+        <style>
+        .analyze-reset-buttons {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+        .analyze-reset-buttons button {
+            flex: 1;
+            margin: 10px;
+            padding: 10px;
+            background-color: #3366ff;
+            color: white;
+            font-size: 16px;
+            text-align: center;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            transition: background-color 0.3s ease;
+        }
+        .analyze-reset-buttons button:hover {
+            background-color: #2353b4;
+        }
+        .results-text {
+            font-size: 24px;
+            font-weight: bold;
+            margin-top: 20px;
+            color: #000;
+        }
+        .results-values {
+            font-size: 18px;
+            font-weight: bold;
+            margin-top: 10px;
+            color: blue;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    st.markdown("<div class='analyze-reset-buttons'>", unsafe_allow_html=True)
+    if st.button("Analyze", key="analyze_button"):
+        if uploaded_image is not None:
+            # Preprocess the uploaded image
+            img = Image.open(uploaded_image)
+            img = img.resize((64, 64))  # Resize the image to match the model input size
+            img = np.array(img)
+            img = tf.keras.applications.resnet50.preprocess_input(img)
+            img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+            # Make predictions using the loaded model
+            predictions = model_resnet.predict(img)
+
+            # Get the class label with the highest probability
+            class_label = np.argmax(predictions)
+            confidence = predictions[0][class_label]
+
+            # Display prediction and confidence
+            st.markdown(
+                f"<div class='results-text'>Predicted Class: <span class='results-values'>{class_label}</span></div>",
+                unsafe_allow_html=True,
+            )
+            st.markdown(
+                f"<div class='results-text'>Confidence Level: <span class='results-values'>{confidence:.2f}</span></div>",
+                unsafe_allow_html=True,
+            )
+        else:
+            st.warning("Please upload an image before clicking 'Analyze'.")
+    if st.button("Reset", key="reset_button"):
+        st.session_state.uploaded_image = None
+        uploaded_image = None
+        st.empty()  # Clear the results container
+    st.markdown("</div>", unsafe_allow_html=True)