Added interface

data/test.txt

@@ -1 +1,2 @@
-ATGGACAAACTCTAGTAACGGT
+ATGGACAAACTCTAGTAACGGT
+ATGGACAAACTCTAGTAACGGTATGGACAAACTCTAGTAACGGT

data/test_1.txt

@@ -0,0 +1,2 @@
+ATGGACAAACTCTAGTAACGGT
+ATGGACAAACTCTAGTAACGGTATGGACAAACTCTAGTAACGGT

data/test_2.txt

@@ -0,0 +1,2 @@
+GGCTGGGAGCTCCTCCATTCCATACCTTTTGATCCATTGATCGGATGGTCATGGCCGAGAGAGATGCAATCTGAAACGTCCTTTATACATTAGAGTTATATTGAATGATCACCCTATTAGGTCGAAGAGCCGGTGACTTGCATCAGGCTTAGTGCATATGCTTTTGGTTCGTCAACCTTCACCAAAAGGAAGGGGGCATATGTTGAATAGCAAAAATGACACCCGGACCGTCCGCCCTAGGGCCGGATGGTCCGCGGTCCGAACTATCCACGGTGGCGACACAGACGGTCTACACGTACGCAGAATCAGATAGGGTTCCGAATTTCTAGCGGGATTTGTTAGCAAAGCATGTAGGAATAACTTGATGACCGACTTGTAACAGGTCTAGACCTCCACCTTTTATCTGGATGAAGGGGTACAACCGATTGAACCTCTCACAATCGATCCAATCAAATCTACTTATCAATTACCTTATTTGCATCATTCTTCTAATTCCTAGGAGTAGGAGTAATTTTGCCTTAGGTTTAGATCTAGTTTAGTCTTCCACAACAACCTCTTCTTTGACTCTACGCCGATTAGAGGAGCACCAGGCGGCCTGCCGACCCAGAGCACGCCTTGGAACTCTCCCCCTCGATGGGATCTCTCCCGTGGCGAGTTCTAGGATTCTCCACGGAGATGAAGACCCTCTCCACCACTGCGGACCGTCCGGCCCTAGGCGCGGACACATCCGAAAGCCTGCATAGGAGGATCCGCTCTTGTGCCCTGTGCCGCGGACCGTTCGCGCCTCCGTAGAGAGCACACCGCGCAGGTGGTTCGTTTCAGTGGTTGGCATCCAGATCGGCGCCAACAGGGTGATTTGGTTGTGTGAATTTTACAACGTTTAAACTGATGGGTGTATAGATATAGATATAGATTTTAAAAAACAAAAGAAATAAAAAAATGCCCAACGGGGAAGGTAGTATTTGGTCTGTACTGTTTCCTTGCGTGATGGACATGGACT
+GGCCCAGCGCACAGCTGTGATAGTGTGGCATTGTCCGGGCGCGCGCGCCCGCTCTCTCTCATCGTACCGTACGTCCGGGCCTTCTTCTCTCTCCGTGCTGTTGCCTCCTCCCTCCACCGATCTACCACGTACGACTACTGTACATTCTTGCCTTGTGAATTTACAGTGAATTGTATATATAAATATATATACATACATACAGTAGGTGTAGATATATCAATATATATATACAGAGATGGAGAGGGAGGGGGCATGAGGCATGCATATTCTGCAAAAAAAAAACATTTTATTTTGTCAGAGCTTCATGCAATTTGGGCCTACTAGCTGGCAGGCTAGCTAGCTAGGGTGGATGCGATCGTCGACCAGGGCTGGCAGCGACAAACCACCTGTGGTGGCAGTCTGCCTCCCCCCTTTGTTTTCTCCTGCCCCCCTGCTCTTCTTCCCCAAGTAGCTAGCAGGGAGTAGAGTAATATGCGTACCCTACGCAGTAATTAATCTTCACTGGATTAAATTGCGTGTATATATATAGAGAGAGATGCGTGAGCTGTCTACGATTATTGTCACAGACGTGCTGTAGCCAAATAATCCTAGCGACCGCGAGGGTGGAGCGGACTACGTACGGTCTACGTCTACGTCTACGGCGGTGGGTGATTAGAATTTCCTCCGATCCACTGATTACCAGCCGGCCTGTACATGTACATCTCTGATTCTCTGTGCGTGTTTAATTTTATTACTGCTGACTTGAGGAGCATGGAGGAGGATATGCATGCGTGCGTACATACGTCTGTCTGTCGTCGTCCATCACCGCGCGCCGCCCTCGCCTCTCTTTCTCACATCATCTTCTTGCATTGGTCCTCATCTAGCCTGCTGCCTGCCATACCTAGCAGCGTTTCACTTCACTTCACCGCCGCCGATGGATGCCCATGCTCACCGGTCTTCCCCCGCCCCCTCACCGCCCTATTCTAGCTATTTAGTGCTGCTAGCCTAGCTCTACTGCTAC

module/metrics.py

@@ -40,6 +40,8 @@ def get_predictions(
             outputs = model(**inputs)
         del inputs  # to free up space on GPU
         logits = outputs[0]
-        pred_labels.append([round(e, 4) for e in logits.cpu().tolist()[0]])
+        logits = logits.cpu().tolist()
+        for i in range(len(logits)):
+            pred_labels.append([round(e, 4) for e in logits[i]])
     
     return pred_labels

module/utils.py

@@ -11,7 +11,8 @@ from pathlib import PosixPath, Path
 import importlib as im
 import json
 import pickle
-
+from pydantic import *
+from typing import List
 import pandas as pd
 import numpy as np
 from IPython.display import display
@@ -41,21 +42,21 @@ def preprocess_genex(genex_data: pd.DataFrame, settings: dict) -> pd.DataFrame:
         return genex_data
 
 def get_args(
-    data_dir=data_final / "transformer" / "seq",
-    train_data="all_seqs_train.txt",
-    eval_data=None,
-    test_data="all_seqs_test.txt",
-    output_dir=models / "transformer" / "language-model",
-    model_name=None,
-    pretrained_model=None,
-    tokenizer_dir=None,
-    log_offset=None,
-    preprocessor=None,
-    filter_empty=False,
-    hyperparam_search_metrics=None,
-    hyperparam_search_trials=None,
-    transformation=None,
-    output_mode=None,
+    data_dir: DirectoryPath = data_final / "transformer" / "seq",
+    train_data: FilePath = "all_seqs_train.txt",
+    eval_data: FilePath = None,
+    test_data: FilePath = "all_seqs_test.txt",
+    output_dir: DirectoryPath = models / "transformer" / "language-model",
+    model_name: str = None,
+    pretrained_model: FilePath = None,
+    tokenizer_dir: DirectoryPath = None,
+    log_offset: int = None,
+    preprocessor: str = None,
+    filter_empty: bool = False,
+    hyperparam_search_metrics: List[str] = None,
+    hyperparam_search_trials: int = None,
+    transformation: str = None,
+    output_mode: str = None,
 ) -> argparse.Namespace:
     """Use Python's ArgumentParser to create a namespace from (optional) user input
 
@@ -207,7 +208,7 @@ def get_args(
         default=None,
     )
     parser.add_argument("--batch-norm", action="store_true", default=False)
-    args = parser.parse_args()
+    args, unknown = parser.parse_known_args()
 
     if args.pretrained_model and not args.pretrained_model.startswith("/"):
         args.pretrained_model = str(Path.cwd() / args.pretrained_model)

scripts/app.py

@@ -0,0 +1,105 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import HTMLResponse, JSONResponse
+# from fastapi.staticfiles import StaticFiles
+from starlette.requests import Request
+# from fastapi.templating import Jinja2Templates
+from fastapi.middleware.cors import CORSMiddleware 
+from pydantic import *
+
+from FloraBERT.module import config, transformers_utility as tr, utils, metrics, dataio
+# from prettytable import PrettyTable
+import numpy as np
+
+app = FastAPI()
+app.add_middleware( 
+    CORSMiddleware,
+    allow_origins=["*"], 
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+# app.mount("FloraBERT.static", StaticFiles(directory="FloraBERT.static"), name="static")
+# templates = Jinja2Templates(directory="templates")
+
+# table = PrettyTable()
+TOKENIZER_DIR = config.models / "byte-level-bpe-tokenizer"
+PRETRAINED_MODEL = config.models / "transformer" / "prediction-model" / "saved_model.pth"
+DATA_DIR = config.data
+
+def load_model(args, settings):
+    return tr.load_model(
+        args.model_name,
+        args.tokenizer_dir,
+        pretrained_model=args.pretrained_model,
+        log_offset=args.log_offset,
+        **settings,
+    )
+
+@app.get("/", response_class=HTMLResponse)
+def read_root(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/uploadfile/")
+async def create_upload_file(file: UploadFile = File(...)):
+    file_path = DATA_DIR / file.filename
+    with open(file_path, "wb") as f:
+        f.write(file.file.read())
+    return {"filename": file.filename}
+
+@app.get("/process/{filename}", response_class=HTMLResponse)
+def process_file(request: Request, filename: str):
+    file_path = DATA_DIR / filename
+    preds = main(
+        data_dir=DATA_DIR,
+        train_data=file_path,
+        test_data=file_path,
+        pretrained_model=PRETRAINED_MODEL,
+        tokenizer_dir=TOKENIZER_DIR,
+        model_name="roberta-pred-mean-pool",
+    )
+    predictions = []
+    for i in range(len(preds)):
+        predictions.append([{"tissue": config.tissues[j], "prediction": preds[i][j] } for j in range(8)])
+    # print(predictions)
+    return JSONResponse(content=predictions)
+
+def main(data_dir: str, train_data: str, test_data: str, pretrained_model: str, tokenizer_dir: str, model_name: str):
+    args = utils.get_args(
+        data_dir=data_dir,
+        train_data=train_data,
+        test_data=test_data,
+        pretrained_model=pretrained_model,
+        tokenizer_dir=tokenizer_dir,
+        model_name=model_name,
+    )
+
+    settings = utils.get_model_settings(config.settings, args)
+    if args.output_mode:
+        settings["output_mode"] = args.output_mode
+    if args.tissue_subset is not None:
+        settings["num_labels"] = len(args.tissue_subset)
+    
+    print("Loading model...")
+    config_obj, tokenizer, model = load_model(args, settings)
+
+    print("Loading data...")
+    datasets = dataio.load_datasets(
+        tokenizer,
+        args.train_data,
+        eval_data=args.eval_data,
+        test_data=args.test_data,
+        seq_key="text",
+        file_type="text",
+        filter_empty=args.filter_empty,
+        shuffle=False,
+    )
+    dataset_test = datasets["train"]
+
+    print("Getting predictions:")
+    preds = np.exp(np.array(metrics.get_predictions(model, dataset_test))) - 1
+    # print(preds)
+    # for e in preds:
+    #     table.add_row(e)
+    # print(table)
+
+    return preds.tolist()

templates/index.html

@@ -0,0 +1,282 @@
+<!-- templates/index.html -->
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>File Upload</title>
+    <!-- Add Bootstrap CDN link for styling -->
+    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" rel="stylesheet">
+        <!-- Add these links to the head section of your HTML -->
+    <link href="https://fonts.googleapis.com/css?family=Roboto:400,700" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css?family=Lora:400,700" rel="stylesheet">
+
+    <style>
+        /* Custom CSS for fade-in transition effect */
+        .card {
+            opacity: 0;
+            transition: opacity 0.5s ease-in-out;
+        }
+
+        .card.show {
+            opacity: 1;
+        }
+        body {
+            font-family: 'Lora', sans-serif;
+            background-color: #eaf4ea; /* Light green background */
+            color: #333; /* Dark text color */
+        }
+
+        h1, h2, h3, h4, h5, h6 {
+            font-family: 'Roboto', serif;
+            font-weight: bold;
+        }
+
+        .container {
+            background-color: rgba(255, 255, 255, 0.9); /* Semi-transparent white background for content */
+            border-radius: 15px;
+            padding: 20px;
+            margin-top: 50px;
+        }
+
+        h1, h2 {
+            color: #4CAF50; /* Dark green title color */
+        }
+
+        button.btn-primary {
+            background-color: #4CAF50; /* Dark green button color */
+            border: none;
+        }
+
+        button.btn-primary:hover {
+            background-color: #45a049; /* Slightly darker green on hover */
+        }
+
+        .card {
+            background-color: #f8f9fa; /* Light gray background for cards */
+            border: 1px solid #ddd; /* Light gray border */
+            border-radius: 10px;
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); /* Subtle shadow */
+        }
+
+        .card-header {
+            background-color: #4CAF50; /* Dark green header color for cards */
+            color: white;
+            border-radius: 10px 10px 0 0;
+        }
+
+        .card-body {
+            padding: 15px;
+        }
+
+        .list-group-item {
+            border: none;
+        }
+
+        .btn-success {
+            background-color: #28a745; /* Dark green for success button */
+            border: none;
+        }
+
+        .btn-success:hover {
+            background-color: #218838; /* Slightly darker green on hover */
+        }
+
+        #predictionsList:hover::before {
+            content: "Tables displaying TPM values corresponding to plant tissues";
+            position: absolute;
+            bottom: 100%; /* Position the tooltip above the text */
+            left: 50%;
+            transform: translateX(-50%);
+            background-color: #333;
+            color: #fff;
+            padding: 5px;
+            border-radius: 5px;
+            font-size: 12px;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1 class="mb-4">
+            <a href="https://huggingface.co/Gurveer05/FloraBERT" target="_blank" rel="noopener noreferrer" style="color: #218838;">FloraBERT</a>
+        </h1>
+        <form>
+            <div class="form-group">
+                <label for="fileInput">Select .txt files:</label>
+                <div class="input-group">
+                    <div class="custom-file">
+                        <input type="file" class="custom-file-input" id="fileInput" accept=".txt" multiple onchange="displaySelectedFiles()">
+                        <label class="custom-file-label" for="fileInput">Choose files</label>
+                    </div>
+                </div>
+            </div>
+            <button type="button" class="btn btn-primary" id="uploadButton" onclick="processFiles()">Upload</button>
+            <p id="uploadMessage" class="text-muted mt-2"></p>
+            <div id="selectedFiles" class="mt-2"></div>
+        </form>
+
+        <h2 class="mt-5" id="predictionsList" title="Table displaying TPM values corresponding to plant tissues">Predictions</h2>
+        <div id="loadingIcon" class="d-none">
+            <p class="text-muted">Processing... <span class="spinner-border spinner-border-sm" role="status" aria-hidden="true"></span></p>
+        </div>
+        <div id="predictionsList" class="mt-3"></div>
+
+        <button type="button" class="btn btn-success mt-3" onclick="downloadAllPredictions()">Download All Predictions</button>
+    </div>
+
+    <script>
+        // Maintain a cache for predictions
+        const predictionsCache = {};
+
+        function displaySelectedFiles() {
+            const fileInput = document.getElementById('fileInput');
+            const selectedFilesContainer = document.getElementById('selectedFiles');
+            const files = fileInput.files;
+
+            selectedFilesContainer.innerHTML = '';
+
+            for (let i = 0; i < files.length; i++) {
+                const fileName = files[i].name;
+                const fileLabel = document.createElement('span');
+                fileLabel.innerText = fileName;
+
+                if (i > 0) {
+                    selectedFilesContainer.appendChild(document.createTextNode(', '));
+                }
+
+                selectedFilesContainer.appendChild(fileLabel);
+            }
+        }
+
+        async function processFiles() {
+            const fileInput = document.getElementById('fileInput');
+            const files = fileInput.files;
+
+            const uploadButton = document.getElementById('uploadButton');
+            const uploadMessage = document.getElementById('uploadMessage');
+            const loadingIcon = document.getElementById('loadingIcon');
+            const predictionsList = document.getElementById('predictionsList');
+            const selectedFilesContainer = document.getElementById('selectedFiles');
+
+            // Disable the upload button
+            uploadButton.disabled = true;
+
+            uploadMessage.innerText = ''; // Clear previous messages
+
+            if (files.length === 0) {
+                // Update upload message for no file selected
+                uploadMessage.innerText = 'Please choose at least one file.';
+                // Enable the upload button
+                uploadButton.disabled = false;
+                return;
+            }
+
+            const url = 'http://127.0.0.1:8000';
+
+            loadingIcon.classList.remove('d-none');
+            // predictionsList.innerHTML = ''; // Do not clear previous cards
+
+            for (let i = 0; i < files.length; i++) {
+                const file = files[i];
+                const formData = new FormData();
+                formData.append('file', file);
+
+                const response = await fetch(`${url}/uploadfile/`, {
+                    method: 'POST',
+                    body: formData
+                });
+
+                if (response.ok) {
+                    const result = await response.json();
+                    const filename = result.filename;
+
+                    const predictionsResponse = await fetch(`${url}/process/${filename}`);
+                    const predictions = await predictionsResponse.json();
+
+                    predictionsCache[filename] = predictions;
+
+                    // Display predictions as cards (newest on top)
+                    if (!document.getElementById(`card-${filename}`)) {
+                        const cardHtml = `
+                            <div id="card-${filename}" class="card mt-3">
+                                <div class="card-header">
+                                    ${filename}
+                                </div>
+                                <div class="card-body">
+                                    <ul class="list-group list-group-flush">
+                                        ${predictions.map((predictionList, index) => `
+                                            <li class="list-group-item">
+                                                <strong>Sequence ${index + 1}</strong>
+                                                <table class="table table-bordered mt-2">
+                                                    <thead>
+                                                        <tr>
+                                                            <th scope="col">Tissue</th>
+                                                            <th scope="col">Prediction</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        ${predictionList.map(prediction => `
+                                                            <tr>
+                                                                <td>${prediction.tissue}</td>
+                                                                <td>${prediction.prediction}</td>
+                                                            </tr>
+                                                        `).join('')}
+                                                    </tbody>
+                                                </table>
+                                            </li>
+                                        `).join('')}
+                                    </ul>
+                                </div>
+                            </div>
+                        `;
+                        predictionsList.innerHTML += cardHtml;
+
+                        // Trigger fade-in effect
+                        setTimeout(() => {
+                            document.querySelectorAll('.card').forEach(card => card.classList.add('show'));
+                        }, 100);
+                    }
+
+                    // Update upload message immediately
+                    uploadMessage.innerText = `Uploaded file ${i + 1} of ${files.length}.`;
+
+                    // Clear the message after a short delay (e.g., 2000 milliseconds)
+                    setTimeout(() => {
+                        uploadMessage.innerText = '';
+                    }, 2000);
+                } else {
+                    console.error('Failed to upload file:', file.name);
+                    uploadMessage.innerText = `Failed to upload file ${i + 1} of ${files.length}.`;
+                }
+            }
+
+            // Enable the upload button after all files are processed
+            uploadButton.disabled = false;
+
+            // Hide processing icon after all files are processed
+            loadingIcon.classList.add('d-none');
+            
+            // Clear displayed selected files after processing
+            selectedFilesContainer.innerHTML = '';
+        }
+
+        function downloadAllPredictions() {
+            // Convert predictionsCache to a JSON string
+            const predictionsJSON = JSON.stringify(predictionsCache, null, 2);
+
+            // Create a Blob containing the JSON data
+            const blob = new Blob([predictionsJSON], { type: 'application/json' });
+
+            // Create a link element and trigger a click to download the file
+            const a = document.createElement('a');
+            a.href = URL.createObjectURL(blob);
+            a.download = 'all_predictions.json';
+            a.click();
+        }
+    </script>
+
+    <!-- Add Bootstrap JS and Popper.js CDN links -->
+    <script src="https://code.jquery.com/jquery-3.3.1.slim.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js"></script>
+    <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js"></script>
+</body>
+</html>