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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ctgov_all_trials_trial_space_lineitems_10-31-24.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -8,7 +8,7 @@ sdk_version: 5.9.1
 app_file: app.py
 pinned: false
 license: cc-by-nc-2.0
-short_description: Gradio version of cancer trial search engine
+short_description: For research use only. Not for clinical decision support.
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,142 @@
+import gradio as gr
+import pandas as pd
+import torch
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+from safetensors import safe_open
+from transformers import pipeline, AutoTokenizer
+
+# Load trial spaces data
+trial_spaces = pd.read_csv('ctgov_all_trials_trial_space_lineitems_10-31-24.csv')
+
+# Load embedding model
+embedding_model = SentenceTransformer('ksg-dfci/trialspace', trust_remote_code=True)
+
+# Load precomputed trial space embeddings
+with safe_open("trial_space_embeddings.safetensors", framework="pt", device=0) as f:
+    trial_space_embeddings = f.get_tensor("space_embeddings")
+
+# Load checker pipeline
+tokenizer = AutoTokenizer.from_pretrained("roberta-large")
+checker_pipe = pipeline('text-classification', 'ksg-dfci/TrialChecker', tokenizer=tokenizer, 
+                        truncation=True, padding='max_length', max_length=512)
+
+
+import gradio as gr
+import pandas as pd
+import torch
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+from safetensors import safe_open
+from transformers import pipeline, AutoTokenizer
+
+# We assume the following objects have already been loaded:
+# trial_spaces (DataFrame), embedding_model (SentenceTransformer),
+# trial_space_embeddings (torch.tensor), checker_pipe (transformers pipeline)
+
+def match_clinical_trials(patient_summary: str):
+    # Encode patient summary
+    patient_embedding = embedding_model.encode([patient_summary], convert_to_tensor=True)
+    
+    # Compute similarities
+    similarities = F.cosine_similarity(patient_embedding, trial_space_embeddings)
+    
+    # Pull top 10
+    sorted_similarities, sorted_indices = torch.sort(similarities, descending=True)
+    top_indices = sorted_indices[0:10].cpu().numpy()
+    
+    relevant_spaces = trial_spaces.iloc[top_indices].this_space
+    relevant_nctid = trial_spaces.iloc[top_indices].nct_id
+    relevant_title = trial_spaces.iloc[top_indices].title
+    relevant_brief_summary = trial_spaces.iloc[top_indices].brief_summary
+    relevant_eligibility_criteria = trial_spaces.iloc[top_indices].eligibility_criteria
+
+    analysis = pd.DataFrame({
+        'patient_summary': patient_summary, 
+        'this_space': relevant_spaces,
+        'nct_id': relevant_nctid, 
+        'trial_title': relevant_title,
+        'trial_brief_summary': relevant_brief_summary, 
+        'trial_eligibility_criteria': relevant_eligibility_criteria
+    }).reset_index(drop=True)
+    
+    analysis['pt_trial_pair'] = analysis['this_space'] + "\nNow here is the patient summary:" + analysis['patient_summary']
+    
+    # Run checker pipeline
+    classifier_results = checker_pipe(analysis.pt_trial_pair.tolist())
+    analysis['trial_checker_result'] = [x['label'] for x in classifier_results]
+    analysis['trial_checker_score'] = [x['score'] for x in classifier_results]
+    
+    # Return a subset of columns that are most relevant
+    return analysis[[
+        'nct_id', 
+        'trial_title', 
+        'trial_brief_summary', 
+        'trial_eligibility_criteria', 
+        'trial_checker_result', 
+        'trial_checker_score'
+    ]]
+
+custom_css = """
+#input_box textarea {
+    width: 600px !important;
+    height: 250px !important;
+}
+
+#output_df table {
+    width: 100% !important;
+    table-layout: auto !important;
+    border-collapse: collapse !important;
+}
+
+#output_df table td, #output_df table th {
+    min-width: 100px;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    border: 1px solid #ccc;
+    padding: 4px;
+}
+"""
+
+# JavaScript for enabling colResizable
+js_script = """
+<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/[email protected]/colResizable-1.6.min.js"></script>
+<script>
+document.addEventListener('DOMContentLoaded', function() {
+    var interval = setInterval(function() {
+        var table = document.querySelector('#output_df table');
+        if (table && typeof jQuery !== 'undefined' && typeof jQuery(table).colResizable === 'function') {
+            jQuery('#output_df table').colResizable({liveDrag:true});
+            clearInterval(interval);
+        }
+    }, 500);
+});
+</script>
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.HTML("<h3>Clinical Trial Matcher</h3>")
+    patient_summary_input = gr.Textbox(label="Enter Patient Summary", elem_id="input_box")
+    submit_btn = gr.Button("Find Matches")
+    output_df = gr.DataFrame(
+        headers=[
+            "nct_id", 
+            "trial_title", 
+            "trial_brief_summary", 
+            "trial_eligibility_criteria", 
+            "trial_checker_result", 
+            "trial_checker_score"
+        ], 
+        elem_id="output_df"
+    )
+
+    submit_btn.click(fn=match_clinical_trials, 
+                     inputs=patient_summary_input, 
+                     outputs=output_df)
+    
+    gr.HTML(js_script)
+
+if __name__ == "__main__":
+    demo.launch()

ctgov_all_trials_trial_space_lineitems_10-31-24.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e1253f9d2545f6db6e1c2b66105cbb9d54d2409a3f5ac266da9e30faf9262de
+size 404858338

gradio_app.ipynb

@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d31b58d0-132d-4a98-b199-c3b1d2ed9eb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/klkehl/miniconda3/envs/vllm/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████| 2/2 [00:03<00:00,  1.62s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from safetensors import safe_open\n",
+    "from transformers import pipeline, AutoTokenizer\n",
+    "\n",
+    "# Load trial spaces data\n",
+    "trial_spaces = pd.read_csv('ctgov_all_trials_trial_space_lineitems_10-31-24.csv')\n",
+    "\n",
+    "# Load embedding model\n",
+    "embedding_model = SentenceTransformer('reranker_round2.model', trust_remote_code=True, device='cuda')\n",
+    "\n",
+    "# Load precomputed trial space embeddings\n",
+    "with safe_open(\"trial_space_embeddings.safetensors\", framework=\"pt\", device=0) as f:\n",
+    "    trial_space_embeddings = f.get_tensor(\"space_embeddings\")\n",
+    "\n",
+    "# Load checker pipeline\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"roberta-large\")\n",
+    "checker_pipe = pipeline('text-classification', './roberta-checker', tokenizer=tokenizer, \n",
+    "                        truncation=True, padding='max_length', max_length=512, device='cuda')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "36d48a31-8514-4b0d-84a9-5fccc7ec7227",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "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>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from safetensors import safe_open\n",
+    "from transformers import pipeline, AutoTokenizer\n",
+    "\n",
+    "# We assume the following objects have already been loaded:\n",
+    "# trial_spaces (DataFrame), embedding_model (SentenceTransformer),\n",
+    "# trial_space_embeddings (torch.tensor), checker_pipe (transformers pipeline)\n",
+    "\n",
+    "def match_clinical_trials(patient_summary: str):\n",
+    "    # Encode patient summary\n",
+    "    patient_embedding = embedding_model.encode([patient_summary], convert_to_tensor=True)\n",
+    "    \n",
+    "    # Compute similarities\n",
+    "    similarities = F.cosine_similarity(patient_embedding, trial_space_embeddings)\n",
+    "    \n",
+    "    # Pull top 10\n",
+    "    sorted_similarities, sorted_indices = torch.sort(similarities, descending=True)\n",
+    "    top_indices = sorted_indices[0:10].cpu().numpy()\n",
+    "    \n",
+    "    relevant_spaces = trial_spaces.iloc[top_indices].this_space\n",
+    "    relevant_nctid = trial_spaces.iloc[top_indices].nct_id\n",
+    "    relevant_title = trial_spaces.iloc[top_indices].title\n",
+    "    relevant_brief_summary = trial_spaces.iloc[top_indices].brief_summary\n",
+    "    relevant_eligibility_criteria = trial_spaces.iloc[top_indices].eligibility_criteria\n",
+    "\n",
+    "    analysis = pd.DataFrame({\n",
+    "        'patient_summary': patient_summary, \n",
+    "        'this_space': relevant_spaces,\n",
+    "        'nct_id': relevant_nctid, \n",
+    "        'trial_title': relevant_title,\n",
+    "        'trial_brief_summary': relevant_brief_summary, \n",
+    "        'trial_eligibility_criteria': relevant_eligibility_criteria\n",
+    "    }).reset_index(drop=True)\n",
+    "    \n",
+    "    analysis['pt_trial_pair'] = analysis['this_space'] + \"\\nNow here is the patient summary:\" + analysis['patient_summary']\n",
+    "    \n",
+    "    # Run checker pipeline\n",
+    "    classifier_results = checker_pipe(analysis.pt_trial_pair.tolist())\n",
+    "    analysis['trial_checker_result'] = [x['label'] for x in classifier_results]\n",
+    "    analysis['trial_checker_score'] = [x['score'] for x in classifier_results]\n",
+    "    \n",
+    "    # Return a subset of columns that are most relevant\n",
+    "    return analysis[[\n",
+    "        'nct_id', \n",
+    "        'trial_title', \n",
+    "        'trial_brief_summary', \n",
+    "        'trial_eligibility_criteria', \n",
+    "        'trial_checker_result', \n",
+    "        'trial_checker_score'\n",
+    "    ]]\n",
+    "\n",
+    "custom_css = \"\"\"\n",
+    "#input_box textarea {\n",
+    "    width: 600px !important;\n",
+    "    height: 250px !important;\n",
+    "}\n",
+    "\n",
+    "#output_df table {\n",
+    "    width: 100% !important;\n",
+    "    table-layout: auto !important;\n",
+    "    border-collapse: collapse !important;\n",
+    "}\n",
+    "\n",
+    "#output_df table td, #output_df table th {\n",
+    "    min-width: 100px;\n",
+    "    overflow: hidden;\n",
+    "    text-overflow: ellipsis;\n",
+    "    white-space: nowrap;\n",
+    "    border: 1px solid #ccc;\n",
+    "    padding: 4px;\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "# JavaScript for enabling colResizable\n",
+    "js_script = \"\"\"\n",
+    "<script src=\"https://code.jquery.com/jquery-3.6.0.min.js\"></script>\n",
+    "<script src=\"https://cdn.jsdelivr.net/npm/[email protected]/colResizable-1.6.min.js\"></script>\n",
+    "<script>\n",
+    "document.addEventListener('DOMContentLoaded', function() {\n",
+    "    var interval = setInterval(function() {\n",
+    "        var table = document.querySelector('#output_df table');\n",
+    "        if (table && typeof jQuery !== 'undefined' && typeof jQuery(table).colResizable === 'function') {\n",
+    "            jQuery('#output_df table').colResizable({liveDrag:true});\n",
+    "            clearInterval(interval);\n",
+    "        }\n",
+    "    }, 500);\n",
+    "});\n",
+    "</script>\n",
+    "\"\"\"\n",
+    "\n",
+    "with gr.Blocks(css=custom_css) as demo:\n",
+    "    gr.HTML(\"<h3>Clinical Trial Matcher</h3>\")\n",
+    "    patient_summary_input = gr.Textbox(label=\"Enter Patient Summary\", elem_id=\"input_box\")\n",
+    "    submit_btn = gr.Button(\"Find Matches\")\n",
+    "    output_df = gr.DataFrame(\n",
+    "        headers=[\n",
+    "            \"nct_id\", \n",
+    "            \"trial_title\", \n",
+    "            \"trial_brief_summary\", \n",
+    "            \"trial_eligibility_criteria\", \n",
+    "            \"trial_checker_result\", \n",
+    "            \"trial_checker_score\"\n",
+    "        ], \n",
+    "        elem_id=\"output_df\"\n",
+    "    )\n",
+    "\n",
+    "    submit_btn.click(fn=match_clinical_trials, \n",
+    "                     inputs=patient_summary_input, \n",
+    "                     outputs=output_df)\n",
+    "    \n",
+    "    gr.HTML(js_script)\n",
+    "\n",
+    "demo.launch()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "80ba3cd2-6a76-44d0-b5f4-6d3debd510ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "demo.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e43df71-6f06-48d2-8dce-b1f27ab40d6c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+pandas
+torch
+sentence_transformers
+safetensors
+transformers

trial_space_embeddings.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c03f700caf34d1c30fdf07718fa79b86f16601275fa8fb0372cdd8954bc8c9b2
+size 156221536