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ML6-UniKP

Runtime error

Topallaj Denis commited on Mar 28, 2024

Commit

37db1e1

1 Parent(s): 027aeb0

complete predict endpoint

Files changed (5) hide show

main.py CHANGED Viewed

@@ -1,7 +1,5 @@
-from fastapi import FastAPI, Form
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
 from typing import Dict, List, Any, Tuple
 import pickle
 import math
@@ -13,6 +11,7 @@ from build_vocab import WordVocab
 from pretrain_trfm import TrfmSeq2seq
 from transformers import T5EncoderModel, T5Tokenizer
 import numpy as np
 app = FastAPI()
@@ -30,25 +29,21 @@ tokenizer = T5Tokenizer.from_pretrained(
 model = T5EncoderModel.from_pretrained(
 			"Rostlab/prot_t5_xl_half_uniref50-enc")
-#app.mount("/", StaticFiles(directory="static", html=True), name="static")
-@app.get("/")
-def home():
-	smiles = "CC1=CC(=C(C=C1)C(=O)O)O"
-	sequence = "MGLSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLKSEDEMKASEDLKKAGVTVLTALGAILKKKGHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRHPGNFGADAQGAMNKALELFRKDIAAKYKELGYQG"
 	endpointHandler = EndpointHandler()
 	result = endpointHandler.predict({
 		"inputs": {
-			"sequence": sequence,
-			"smiles": smiles
 		}
 	})
 	return result
-	# return FileResponse(path="/app/static/index.html", media_type="text/html")
 class EndpointHandler():
 	def __init__(self, path=""):

+from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from typing import Dict, List, Any, Tuple
 import pickle
 import math
 from pretrain_trfm import TrfmSeq2seq
 from transformers import T5EncoderModel, T5Tokenizer
 import numpy as np
+import pydantic
 app = FastAPI()
 model = T5EncoderModel.from_pretrained(
 			"Rostlab/prot_t5_xl_half_uniref50-enc")
+class Item(pydantic.BaseModel):
+	sequence: str
+	smiles: str
+@app.get("/predict")
+def predict(item: Item):
 	endpointHandler = EndpointHandler()
 	result = endpointHandler.predict({
 		"inputs": {
+			"sequence": item.sequence,
+			"smiles": item.smiles
 		}
 	})
 	return result
 class EndpointHandler():
 	def __init__(self, path=""):

requirements.txt CHANGED Viewed

@@ -10,4 +10,5 @@ SentencePiece==0.2.0
 scikit-learn==1.2.2
 openpyxl==3.1.2
 torch==2.2.1
-python-multipart==0.0.9

 scikit-learn==1.2.2
 openpyxl==3.1.2
 torch==2.2.1
+python-multipart==0.0.9
+pydantic==2.6.4

static/index.html DELETED Viewed

@@ -1,49 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="UTF-8" />
-		<meta name="viewport" content="width=device-width, initial-scale=1.0" />
-		<script src="./script.js" defer></script>
-		<link rel="stylesheet" href="./styles.css" />
-		<title>UniKP App</title>
-	</head>
-	<body>
-		<div class="container">
-			<h1>UniKP App</h1>
-			<p>
-				This HF app uses the UniKP framework. Be sure to check it out on
-				<a href="https://github.com/Luo-SynBioLab/UniKP"> GitHub</a>
-			</p>
-			<form id="predict-form">
-				<label for="sequence">Sequence:</label>
-				<input
-					type="text"
-					id="sequence"
-					name="sequence"
-					placeholder="Enter sequence here"
-					required
-				/>
-				<label for="substrate">Substrate (SMILES):</label>
-				<input
-					type="text"
-					id="substrate"
-					name="smiles"
-					placeholder="Enter substrate SMILES here"
-					required
-				/>
-				<input type="submit" value="Predict" />
-			</form>
-		</div>
-		<div id="result-container">
-			<div id="result" class="container">
-				<h3>Km</h3>
-				<p id="km-result"></p>
-				<h3>Kcat</h3>
-				<p id="kcat-result"></p>
-				<h3>Vmax</h3>
-				<p id="vmax-result"></p>
-			</div>
-		</div>
-	</body>
-</html>

static/script.js DELETED Viewed

@@ -1,35 +0,0 @@
-"use strict";
-document.querySelector("#predict-form").addEventListener("submit", async (e) => {
-	e.preventDefault();
-	const sequence = document.querySelector("#sequence").value;
-	const smiles = document.querySelector("#substrate").value;
-	console.log(sequence);
-	const data = await prediction(sequence, smiles);
-	document.querySelector("#km-result").innerText = data.km;
-	document.querySelector("#kcat-result").innerText = data.kcat;
-	document.querySelector("#vmax-result").innerText = data.vmax;
-});
-const prediction = async (sequence, smiles) => {
-	/*const response = await fetch("/predict", {
-		method: "POST",
-		body: {
-			sequence: sequence,
-			smiles: smiles,
-		}
-	});
-	const data = await response.json();
-	return data;
-	*/
-	return {
-		km: 1,
-		kcat: 2,
-		vmax: 3,
-	}
-};

static/styles.css DELETED Viewed

@@ -1,80 +0,0 @@
-/* styles.css */
-/* Resetting default margin and padding */
-* {
-    margin: 0;
-    padding: 0;
-    box-sizing: border-box;
-}
-/* Global styles */
-body {
-    font-family: Arial, sans-serif;
-    background-color: #f0f0f0;
-    color: #333;
-}
-.container {
-    max-width: 600px;
-    margin: 20px auto;
-    padding: 20px;
-    background-color: #fff;
-    border-radius: 8px;
-    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
-}
-h1 {
-    font-size: 24px;
-    margin-bottom: 20px;
-}
-p {
-    margin-bottom: 20px;
-}
-form {
-    margin-bottom: 20px;
-}
-label {
-    display: block;
-    margin-bottom: 5px;
-}
-input[type="text"] {
-    width: 100%;
-    padding: 10px;
-    margin-bottom: 10px;
-    border: 1px solid #ccc;
-    border-radius: 4px;
-}
-input[type="submit"] {
-    background-color: #007bff;
-    color: #fff;
-    border: none;
-    padding: 10px 20px;
-    border-radius: 4px;
-    cursor: pointer;
-}
-input[type="submit"]:hover {
-    background-color: #0056b3;
-}
-/* Result container styles */
-#result-container #result {
-    background-color: #f9f9f9;
-    padding: 20px;
-    border-radius: 8px;
-    box-shadow: 0 0 5px rgba(0, 0, 0, 0.1);
-}
-#result h3 {
-    font-size: 18px;
-    margin-bottom: 10px;
-}
-#result p {
-    margin-bottom: 10px;
-}