added emojii

Gradio_app.ipynb

@@ -2,14 +2,29 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Onset_picker.load_from_checkpoint(\"./weights.ckpt\",\n",
+    "                                 picker=Updated_onset_picker(),\n",
+    "                                    learning_rate=3e-4)\n",
+    "model.eval()\n",
+    "model.freeze()\n",
+    "script = model.to_torchscript()\n",
+    "torch.jit.save(script, \"model.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7860\n",
+      "Running on local URL:  http://127.0.0.1:7871\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -17,7 +32,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7871/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -30,7 +45,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 1,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -41,7 +56,6 @@
     "import gradio as gr\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from phasehunter.model import Onset_picker, Updated_onset_picker\n",
     "from phasehunter.data_preparation import prepare_waveform\n",
     "import torch\n",
     "\n",
@@ -69,7 +83,7 @@
     "    processed_input = prepare_waveform(waveform)\n",
     "    \n",
     "    # Make prediction\n",
-    "    with torch.no_grad():\n",
+    "    with torch.inference_mode():\n",
     "        output = model(processed_input)\n",
     "\n",
     "    p_phase = output[:, 0]\n",
@@ -377,39 +391,40 @@
     "\n",
     "    return image, output_picks\n",
     "\n",
-    "\n",
-    "model = Onset_picker.load_from_checkpoint(\"./weights.ckpt\",\n",
-    "                                 picker=Updated_onset_picker(),\n",
-    "                                    learning_rate=3e-4)\n",
-    "model.eval()\n",
+    "model = torch.jit.load(\"model.pt\")\n",
     "\n",
     "with gr.Blocks() as demo:\n",
-    "    gr.HTML(\"\"\"<h1>PhaseHunter</h1>\n",
-    "<p>This app allows uses a new neural network called PhaseHunter to detect <span style=\"background-image: linear-gradient(to right, #ED213A, #93291E); \n",
-    "               -webkit-background-clip: text;\n",
-    "               -webkit-text-fill-color: transparent;\n",
-    "               background-clip: text;\n",
-    "               font-size: 24px;\">P</span> and \n",
-    "               <span style=\"background-image: linear-gradient(to right, #00B4DB, #0083B0); \n",
-    "               -webkit-background-clip: text;\n",
-    "               -webkit-text-fill-color: transparent;\n",
-    "               background-clip: text;\n",
-    "               font-size: 24px;\">S</span> seismic phases along with <span style=\"background-image: linear-gradient(to right, #f12711, #f5af19); \n",
-    "               -webkit-background-clip: text;\n",
-    "               -webkit-text-fill-color: transparent;\n",
-    "               background-clip: text;\n",
-    "               font-size: 24px;\">\n",
-    "     uncertainty\n",
-    "  </span> of the detection.</p>\n",
-    "<ol>\n",
-    "  <li>By selecting one of the sample waveforms.</li>\n",
-    "  <li>By uploading your own waveform.</li>\n",
-    "  <li>By selecting an earthquake from the global earthquake catalogue.</li>\n",
-    "</ol>\n",
-    "<p>Please upload your waveform in <code>.npy</code> (numpy) format.</p>\n",
-    "<p>Your waveform should be sampled at 100 samples per second and have 3 (Z, N, E) or 1 (Z) channels. \n",
-    "If your file is longer than 60 seconds, the app will only use the first 60 seconds of the waveform.</p>\n",
+    "    gr.HTML(\"\"\"\n",
+    "<div style=\"padding: 20px; border-radius: 10px;\">\n",
+    "    <h1 style=\"font-size: 30px; text-align: center; margin-bottom: 20px;\">PhaseHunter <span style=\"animation: arrow-anim 10s linear infinite; display: inline-block; transform: rotate(45deg) translateX(-20px);\">🏹</span>\n",
+    "\n",
+    "<style>\n",
+    "    @keyframes arrow-anim {\n",
+    "        0% { transform: translateX(-20px); }\n",
+    "        50% { transform: translateX(20px); }\n",
+    "        100% { transform: translateX(-20px); }\n",
+    "    }\n",
+    "</style></h1> \n",
+    "    \n",
+    "\n",
+    "    <p style=\"font-size: 16px; margin-bottom: 20px;\">Detect <span style=\"background-image: linear-gradient(to right, #ED213A, #93291E); \n",
+    "    -webkit-background-clip: text;\n",
+    "    -webkit-text-fill-color: transparent;\n",
+    "    background-clip: text;\">P</span> and <span style=\"background-image: linear-gradient(to right, #00B4DB, #0083B0); \n",
+    "    -webkit-background-clip: text;\n",
+    "    -webkit-text-fill-color: transparent;\n",
+    "    background-clip: text;\">S</span> seismic phases with <span style=\"background-image: linear-gradient(to right, #f12711, #f5af19); \n",
+    "    -webkit-background-clip: text;\n",
+    "    -webkit-text-fill-color: transparent;\n",
+    "    background-clip: text;\">uncertainty</span></p>\n",
+    "    <ul style=\"font-size: 16px; margin-bottom: 40px;\">\n",
+    "        <li>Detect seismic phases by selecting a sample waveform or uploading your own waveform in <code>.npy</code> format.</li>\n",
+    "        <li>Select an earthquake from the global earthquake catalogue and PhaseHunter will analyze seismic stations in the given radius.</li>\n",
+    "        <li>Waveforms should be sampled at 100 samples/sec and have 3 (Z, N, E) or 1 (Z) channels. PhaseHunter analyzes the first 6000 samples of your file.</li>\n",
+    "    </ul>\n",
+    "</div>\n",
     "\"\"\")\n",
+    "\n",
     "    with gr.Tab(\"Try on a single station\"):\n",
     "        with gr.Row(): \n",
     "            # Define the input and output types for Gradio\n",
@@ -510,6 +525,51 @@
     "demo.launch()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<span style=\"animation: arrow-anim 10s linear infinite; display: inline-block; transform: rotate(45deg) translateX(-20px);\">🏹</span>\n",
+       "\n",
+       "<style>\n",
+       "    @keyframes arrow-anim {\n",
+       "        0% { transform: translateX(-20px); }\n",
+       "        50% { transform: translateX(20px); }\n",
+       "        100% { transform: translateX(-20px); }\n",
+       "    }\n",
+       "</style>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%html\n",
+    "\n",
+    "<span style=\"animation: arrow-anim 10s linear infinite; display: inline-block; transform: rotate(45deg) translateX(-20px);\">🏹</span>\n",
+    "\n",
+    "<style>\n",
+    "    @keyframes arrow-anim {\n",
+    "        0% { transform: translateX(-20px); }\n",
+    "        50% { transform: translateX(20px); }\n",
+    "        100% { transform: translateX(-20px); }\n",
+    "    }\n",
+    "</style>\n",
+    "\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

app.py

@@ -3,7 +3,6 @@
 import gradio as gr
 import numpy as np
 import pandas as pd
-from phasehunter.model import Onset_picker, Updated_onset_picker
 from phasehunter.data_preparation import prepare_waveform
 import torch
 
@@ -31,7 +30,7 @@ def make_prediction(waveform):
     processed_input = prepare_waveform(waveform)
     
     # Make prediction
-    with torch.no_grad():
+    with torch.inference_mode():
         output = model(processed_input)
 
     p_phase = output[:, 0]
@@ -339,39 +338,40 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
 
     return image, output_picks
 
-
-model = Onset_picker.load_from_checkpoint("./weights.ckpt",
-                                 picker=Updated_onset_picker(),
-                                    learning_rate=3e-4)
-model.eval()
+model = torch.jit.load("model.pt")
 
 with gr.Blocks() as demo:
-    gr.HTML("""<h1>PhaseHunter</h1>
-<p>This app allows uses a new neural network called PhaseHunter to detect <span style="background-image: linear-gradient(to right, #ED213A, #93291E); 
-               -webkit-background-clip: text;
-               -webkit-text-fill-color: transparent;
-               background-clip: text;
-               font-size: 24px;">P</span> and 
-               <span style="background-image: linear-gradient(to right, #00B4DB, #0083B0); 
-               -webkit-background-clip: text;
-               -webkit-text-fill-color: transparent;
-               background-clip: text;
-               font-size: 24px;">S</span> seismic phases along with <span style="background-image: linear-gradient(to right, #f12711, #f5af19); 
-               -webkit-background-clip: text;
-               -webkit-text-fill-color: transparent;
-               background-clip: text;
-               font-size: 24px;">
-     uncertainty
-  </span> of the detection.</p>
-<ol>
-  <li>By selecting one of the sample waveforms.</li>
-  <li>By uploading your own waveform.</li>
-  <li>By selecting an earthquake from the global earthquake catalogue.</li>
-</ol>
-<p>Please upload your waveform in <code>.npy</code> (numpy) format.</p>
-<p>Your waveform should be sampled at 100 samples per second and have 3 (Z, N, E) or 1 (Z) channels. 
-If your file is longer than 60 seconds, the app will only use the first 60 seconds of the waveform.</p>
+    gr.HTML("""
+<div style="padding: 20px; border-radius: 10px;">
+    <h1 style="font-size: 30px; text-align: center; margin-bottom: 20px;">PhaseHunter <span style="animation: arrow-anim 10s linear infinite; display: inline-block; transform: rotate(45deg) translateX(-20px);">🏹</span>
+
+<style>
+    @keyframes arrow-anim {
+        0% { transform: translateX(-20px); }
+        50% { transform: translateX(20px); }
+        100% { transform: translateX(-20px); }
+    }
+</style></h1> 
+    
+
+    <p style="font-size: 16px; margin-bottom: 20px;">Detect <span style="background-image: linear-gradient(to right, #ED213A, #93291E); 
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;">P</span> and <span style="background-image: linear-gradient(to right, #00B4DB, #0083B0); 
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;">S</span> seismic phases with <span style="background-image: linear-gradient(to right, #f12711, #f5af19); 
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;">uncertainty</span></p>
+    <ul style="font-size: 16px; margin-bottom: 40px;">
+        <li>Detect seismic phases by selecting a sample waveform or uploading your own waveform in <code>.npy</code> format.</li>
+        <li>Select an earthquake from the global earthquake catalogue and PhaseHunter will analyze seismic stations in the given radius.</li>
+        <li>Waveforms should be sampled at 100 samples/sec and have 3 (Z, N, E) or 1 (Z) channels. PhaseHunter analyzes the first 6000 samples of your file.</li>
+    </ul>
+</div>
 """)
+
     with gr.Tab("Try on a single station"):
         with gr.Row(): 
             # Define the input and output types for Gradio

weights.ckpt → model.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:80255c65b749559f7c5c3f2bb993a25cc666d9a63a0d3050024679dd8064dcec
-size 200977197
+oid sha256:d783548eb6c267cac43d59a4a6e53298b1667242bbb2009ee22f3b9e57b7dd47
+size 68468411