new release

app.py

@@ -3,6 +3,8 @@ import os
 import csv
 import json
 import torch
+import requests
+
 import numpy as np
 import pandas as pd
 import gradio as gr
@@ -21,10 +23,10 @@ from funcs.dataloader import BaseDataset2, read_json_files
 
 DEVICE = torch.device("cpu")
 reducer10d = PHATEAE(epochs=30, n_components=10, lr=.0001, batch_size=128, t='auto', knn=8, relax=True, metric='euclidean')
-reducer10d.load('models/r10d_6.pth')
+reducer10d.load('models/r10d_3.pth')
 
 cluster_som = ClusterSOM()
-cluster_som.load("models/cluster_som6.pkl")
+cluster_som.load("models/cluster_som3.pkl")
 
 def score(self, data, midpoints=None, threshold_radius=4):
     """
@@ -223,7 +225,16 @@ def get_som_mp4_v2(csv_file_box, slice_size_slider, sample_rate, window_size_sli
         csv_writer.writerow(header)
         csv_writer.writerows(processed_data)
     
-    os.system('curl -X POST -F "csv_file=@animation_table.csv" https://metric-space.ngrok.io/generate --output animation.mp4')
+    # os.system('curl -X POST -F "csv_file=@animation_table.csv" https://metric-space.ngrok.io/generate --output animation.mp4')
+    #with hhtp requests
+    url = "https://metric-space.ngrok.io/generate"
+    file = {'csv_file': open('animation_table.csv', 'rb')}
+    response = requests.post(url, files=file)
+
+    # The response will contain the binary data of the MP4 file. 
+    # You can write it to a file like this:
+    with open('animation.mp4', 'wb') as f:
+        f.write(response.content)
 
     # prediction = cluster_som.predict(embedding10d)
     som_video = cluster.plot_activation(embedding10d)

funcs/processor.py

@@ -74,8 +74,8 @@ def process_data(input_file, slice_size=64, min_slice_size=10, sample_rate=20, w
     # Save the resulting DataFrame to a new file
     data.to_csv('output.csv', sep=";", na_rep="NaN", float_format="%.0f")
 
-    # file, len_ = slice_csv_to_json('output.csv', slice_size, min_slice_size, sample_rate, window_size=window_size)
-    file, len_ = slice_csv_to_json_v2('output.csv', slice_size, min_slice_size, sample_rate)
+    file, len_ = slice_csv_to_json('output.csv', slice_size, min_slice_size, sample_rate, window_size=window_size)
+    # file, len_ = slice_csv_to_json_v2('output.csv', slice_size, min_slice_size, sample_rate)
     # get the plot automatically
     sensor_fig, slice_fig, get_all_slice, slice_json, overlay_fig = plot_sensor_data_from_json(file, "GZ1")
     # overlay_fig = plot_overlay_data_from_json(file, ["GZ1", "GZ2", "GZ3", "GZ4"])

funcs/som.py

@@ -185,8 +185,12 @@ class ClusterSOM:
             images.append(img)
             plt.close()
         
+        # Set default frame duration if `times` is not provided.
+        if times is None:
+            times = [500 for _ in range(len(images))]
+
         # Create the video using moviepy and save it as a mp4 file
-        video = ImageSequenceClip(images, fps=1) 
+        video = ImageSequenceClip(images, fps=30) 
 
         return video