proof of concept

app.py

@@ -1,7 +1,99 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+import gradio as gr
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+from tensorflow.keras.models import model_from_json
+from utils.utils import open_anchor_to_anchor, draw_heatmap, load_chr_ratio_matrix_from_sparse
+
+
+model_depths = ['1.5M', '2M', '2.4M', '4.88M', '5M', '6.29M', '8.5M', '12.5M', '16.5M', '25M', '32M', '50M', '100M', '150M']
+# Load the model
+model_weights = 'DeepLoop_models/CPGZ_trained/12.5M.h5'  # Replace with your model weights file
+model_architecture = 'DeepLoop_models/CPGZ_trained/12.5M.json'  # Replace with your model architecture file
+
+with open(model_architecture, 'r') as f:
+    model = model_from_json(f.read())
+model.load_weights(model_weights)
+
+# Define the anchor file path
+anchor_file = 'ref/hg19_DPNII_anchor_bed/chr22.bed'  # Replace with your anchor file
+
+# Define the tile size
+tile_size = 128
+
+# Load the input matrix
+# input_file = '../anchor_2_anchor.loop.chr22'
+# chr_name = get_chromosome_from_filename('../anchor_2_anchor.loop.chr22')
+# input_matrix = load_chr_ratio_matrix_from_sparse(os.path.dirname(input_file), os.path.basename(input_file),
+#                                                     os.path.dirname(anchor_file), force_symmetry=True)
+input_file = None
+input_matrix = None
+
+# Load the anchor list
+anchor_list = pd.read_csv(anchor_file, sep='\t', names=['chr', 'start', 'end', 'anchor'])
+
+def predict(depth_idx):
+    """Loads the input file, predicts the output, and visualizes the tile."""
+    selected_depth = model_depths[depth_idx]
+    model_weights = f'DeepLoop_models/CPGZ_trained/{selected_depth}.h5'  # Replace with your model weights file
+    model_architecture = f'DeepLoop_models/CPGZ_trained/{selected_depth}.json'  # Replace with your model architecture file
+
+    with open(model_architecture, 'r') as f:
+        model = model_from_json(f.read())
+    model.load_weights(model_weights)
+
+    # Get the tile
+    center_anchor = int(len(anchor_list) / 2)
+    i = max(0, center_anchor - int(tile_size / 2))
+    j = i + tile_size
+    tile = input_matrix[i:j, i:j].A
+    tile = np.expand_dims(tile, -1)
+    tile = np.expand_dims(tile, 0)
+
+    # Predict the output
+    denoised_tile = model.predict(tile).reshape((tile_size, tile_size))
+    denoised_tile[denoised_tile < 0] = 0
+
+    # Normalize the tiles
+    tile = tile[0, ..., 0]
+    denoised_tile = (denoised_tile + denoised_tile.T) / 2
+
+    # Visualize the tiles
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
+    draw_heatmap(tile, 0, ax=ax1)
+    draw_heatmap(denoised_tile, 0, ax=ax2)
+    ax1.set_title('Input Tile')
+    ax2.set_title(f'{selected_depth} model')
+    plt.tight_layout()
+    
+    # return as a numpy array
+    fig.canvas.draw()
+    data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close(fig)
+    return data
+
+def upload_file(file):
+    global input_file, input_matrix
+    print(file)
+    input_file = file
+    input_matrix = load_chr_ratio_matrix_from_sparse(os.path.dirname(input_file), os.path.basename(input_file),
+                                                        os.path.dirname(anchor_file), force_symmetry=True)
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        upload = gr.UploadButton("Upload a file", file_count="single")
+    with gr.Row():
+        slider = gr.Slider(minimum=0, maximum=len(model_depths) - 1, step=1, label='Model Depth', interactive=True)
+        heatmap = gr.Image(label='Visualization')
+    
+    upload.upload(upload_file, upload)
+    slider.change(predict, [slider], heatmap)
+
+
+
+if __name__ == "__main__":
+    demo.queue().launch()