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src/streamlit_app.py

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+import streamlit as st
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import yaml
+import os
+from pathlib import Path
+from model_downloader import ModelDownloader
+
+# Import models
+from .deeplabv3plus_model import LandslideModel as DeepLabV3PlusModel
+from .vgg16_model import LandslideModel as VGG16Model
+from .resnet34_model import LandslideModel as ResNet34Model
+from .efficientnetb0_model import LandslideModel as EfficientNetB0Model
+from .mitb1_model import LandslideModel as MiTB1Model
+from .inceptionv4_model import LandslideModel as InceptionV4Model
+from .densenet121_model import LandslideModel as DenseNet121Model
+from .resnext50_32x4d_model import LandslideModel as ResNeXt50_32X4DModel
+from .se_resnet50_model import LandslideModel as SEResNet50Model
+from .se_resnext50_32x4d_model import LandslideModel as SEResNeXt50_32X4DModel
+from .segformer_model import LandslideModel as SegFormerB2Model
+from .inceptionresnetv2_model import LandslideModel as InceptionResNetV2Model
+
+# Initialize model downloader
+model_downloader = ModelDownloader()
+
+# Model descriptions
+model_descriptions = {
+    "MobileNetV2": {"type": "mobilenet_v2", "description": "MobileNetV2 is a lightweight deep learning model for image classification and segmentation."},
+    "VGG16": {"type": "vgg16", "description": "VGG16 is a popular deep learning model known for its simplicity and depth."},
+    "ResNet34": {"type": "resnet34", "description": "ResNet34 is a deep residual network that helps in training very deep networks."},
+    "EfficientNetB0": {"type": "efficientnet_b0", "description": "EfficientNetB0 is part of the EfficientNet family, known for its efficiency and performance."},
+    "MiT-B1": {"type": "mit_b1", "description": "MiT-B1 is a transformer-based model designed for segmentation tasks."},
+    "InceptionV4": {"type": "inceptionv4", "description": "InceptionV4 is a convolutional neural network known for its inception modules."},
+    "DeepLabV3+": {"type": "deeplabv3plus", "description": "DeepLabV3+ is an advanced model for semantic image segmentation."},
+    "DenseNet121": {"type": "densenet121", "description": "DenseNet121 is a densely connected convolutional network for image classification and segmentation."},
+    "ResNeXt50_32X4D": {"type": "resnext50_32x4d", "description": "ResNeXt50_32X4D is a highly modularized network aimed at improving accuracy."},
+    "SEResNet50": {"type": "se_resnet50", "description": "SEResNet50 is a ResNet model with squeeze-and-excitation blocks for better feature recalibration."},
+    "SEResNeXt50_32X4D": {"type": "se_resnext50_32x4d", "description": "SEResNeXt50_32X4D combines ResNeXt and SE blocks for improved performance."},
+    "SegFormerB2": {"type": "segformer", "description": "SegFormerB2 is a transformer-based model for semantic segmentation."},
+    "InceptionResNetV2": {"type": "inceptionresnetv2", "description": "InceptionResNetV2 is a hybrid model combining Inception and ResNet architectures."},
+}
+
+# Streamlit app
+st.set_page_config(page_title="Landslide Detection", layout="wide")
+
+st.title("Landslide Detection")
+st.markdown("""
+## Instructions
+1. Select a model from the sidebar.
+2. Upload one or more `.h5` files.
+3. The app will process the files and display the input image, prediction, and overlay.
+4. You can download the prediction results.
+""")
+
+# Sidebar for model selection
+st.sidebar.title("Model Selection")
+model_type = st.sidebar.selectbox("Select Model", list(model_descriptions.keys()))
+
+# Get model details
+config = {
+    'model_config': {
+        'model_type': model_descriptions[model_type]['type'],
+        'in_channels': 14,
+        'num_classes': 1
+    }
+}
+
+# Show model description
+st.sidebar.markdown(f"**Model Type:** {model_descriptions[model_type]['type']}")
+st.sidebar.markdown(f"**Description:** {model_descriptions[model_type]['description']}")
+
+try:
+    # Get the appropriate model class
+    if model_type == "DeepLabV3+":
+        model_class = DeepLabV3PlusModel
+    else:
+        model_class = locals()[model_type.replace("-", "") + "Model"]
+    
+    # Get model path from downloader
+    model_name = model_descriptions[model_type]['type'].replace("+", "plus").lower()
+    model_path = model_downloader.get_model_path(model_name)
+    st.success(f"Model {model_type} loaded successfully!")
+
+    # File uploader
+    uploaded_files = st.file_uploader("Upload H5 files", type=['h5'], accept_multiple_files=True)
+    
+    if uploaded_files:
+        # Process each uploaded file
+        for uploaded_file in uploaded_files:
+            st.write(f"Processing {uploaded_file.name}...")
+            # Add your file processing logic here
+            
+except FileNotFoundError as e:
+    st.error(f"Model file not found: {str(e)}")
+    st.error("Please ensure all model files are present in the models directory")
+    st.stop()
+except Exception as e:
+    st.error(f"Error: {str(e)}")
+    st.stop()