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phonefern commited on Oct 4, 2024

Commit

6384276

1 Parent(s): c2ad725

4/10

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Files changed (5) hide show

app.py +65 -7
requirements.txt +2 -1
static/index.html +14 -23
static/script.js +35 -11
static/style.css +82 -40

app.py CHANGED Viewed

@@ -1,17 +1,75 @@
 from fastapi import FastAPI
 from fastapi.staticfiles import StaticFiles
 from fastapi.responses import FileResponse
-from transformers import pipeline
 app = FastAPI()
-pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
-@app.get("/infer_t5")
-def t5(input):
-    output = pipe_flan(input)
-    return {"output": output[0]["generated_text"]}
 app.mount("/", StaticFiles(directory="static", html=True), name="static")

+from fastapi import FastAPI, HTTPException
 from fastapi import FastAPI
 from fastapi.staticfiles import StaticFiles
 from fastapi.responses import FileResponse
+import requests
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from pydantic import BaseModel
 app = FastAPI()
+# Load model directly from Hugging Face Hub
+model_name = "SandboxBhh/sentiment-thai-text-model"
+try:
+    device = 0 if torch.cuda.is_available() else -1  # Check if GPU is available
+    # Ensure correct indentation here
+    reloaded_pipe = pipeline(
+        "text-classification",
+        model=model_name,
+        tokenizer=model_name,
+        device=device,
+    )
+except Exception as e:
+    print(f"Error loading model: {e}")
+    reloaded_pipe = None
+class TextInput(BaseModel):
+    text: str
+def send_line_notification(message, line_token):
+    url = "https://notify-api.line.me/api/notify"
+    headers = {"Authorization": f"Bearer {line_token}"}
+    data = {"message": message}
+    response = requests.post(url, headers=headers, data=data)
+    return response.status_code
+def split_message(message, max_length=1000):
+    return [message[i:i+max_length] for i in range(0, len(message), max_length)]
+# Use environment variable for LINE token
+line_token = "C9r65PpEvIvOJSK2xMhgl53WvmOhhnKEOuQq7DsiVJT"
+@app.post("/classify-text")
+async def classify_text(input: TextInput):
+    if reloaded_pipe is None:
+        raise HTTPException(status_code=500, detail="Model not loaded")
+    try:
+        result = reloaded_pipe(input.text)
+        sentiment = result[0]['label'].lower()
+        score = result[0]['score']
+        if sentiment == 'neg':
+            message = f"[แจ้งเตือน CSI]: ความพึงพอใจของผู้ป่วย \n ข้อความ: {input.text} \n csi score: {score:.2f}"
+            message_parts = split_message(message)
+            for i, part in enumerate(message_parts):
+                status = send_line_notification(part, line_token)
+                if status == 200:
+                    print(f"ส่งการแจ้งเตือนส่วนที่ {i+1}/{len(message_parts)} ผ่าน LINE สำเร็จ")
+                else:
+                    print(f"การส่งการแจ้งเตือนส่วนที่ {i+1}/{len(message_parts)} ผ่าน LINE ล้มเหลว")
+            return {"result": result, "message": "Negative sentiment detected and notification sent to LINE."}
+        else:
+            return {"result": result, "message": "Sentiment is not negative. No notification sent."}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 app.mount("/", StaticFiles(directory="static", html=True), name="static")

requirements.txt CHANGED Viewed

@@ -3,4 +3,5 @@ uvicorn[standard]
 transformers
 torch
 requests
-sentencepiece

 transformers
 torch
 requests
+sentencepiece
+pydantic

static/index.html CHANGED Viewed

@@ -3,34 +3,25 @@
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Fast API 🤗 Space served with Uvicorn</title>
     <link rel="stylesheet" href="style.css" />
     <script type="module" src="script.js"></script>
   </head>
   <body>
     <main>
-      <section id="text-gen">
-        <h1>Text generation using Flan T5</h1>
-        <p>
-          Model:
-          <a
-              href="https://huggingface.co/google/flan-t5-small"
-              rel="noreferrer"
-              target="_blank"
-            >google/flan-t5-small</a
-          >
-        </p>
-        <form class="text-gen-form">
-          <label for="text-gen-input">Text prompt</label>
-          <input
-            id="text-gen-input"
-            type="text"
-            value="English: Translate There are many ducks. German:"
-          />
-          <button id="text-gen-submit">Submit</button>
-          <p class="text-gen-output"></p>
-        </form>
-      </section>
     </main>
   </body>
 </html>

   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Sentiment Classification</title>
     <link rel="stylesheet" href="style.css" />
     <script type="module" src="script.js"></script>
   </head>
   <body>
     <main>
+        <div class="container">
+            <h1>Sentiment Classifier</h1>
+            <form id="textForm">
+                <textarea id="inputText" placeholder="Enter your message here..."></textarea>
+                <button type="submit">Send To Model and Notify To line</button>
+            </form>
+            <div class="loader" id="loader"></div>
+            <div class="result success" id="successMessage">ไปแล้ววว..........</div>
+            <div class="result error" id="errorMessage">Error message goes here...</div>
+        </div>
     </main>
   </body>
 </html>

static/script.js CHANGED Viewed

@@ -1,17 +1,41 @@
-const textGenForm = document.querySelector(".text-gen-form");
-const translateText = async (text) => {
-  const inferResponse = await fetch(`infer_t5?input=${text}`);
-  const inferJson = await inferResponse.json();
-  return inferJson.output;
-};
-textGenForm.addEventListener("submit", async (event) => {
-  event.preventDefault();
-  const textGenInput = document.getElementById("text-gen-input");
-  const textGenParagraph = document.querySelector(".text-gen-output");
-  textGenParagraph.textContent = await translateText(textGenInput.value);
 });

+document.getElementById('textForm').addEventListener('submit', async function(event) {
+    event.preventDefault();
+    const inputText = document.getElementById('inputText').value;
+    const loader = document.getElementById('loader');
+    const successMessage = document.getElementById('successMessage');
+    const errorMessage = document.getElementById('errorMessage');
+    if (!inputText.trim()) {
+        alert("Please enter some text!");
+        return;
+    }
+    loader.style.display = "block";
+    successMessage.style.display = "none";
+    errorMessage.style.display = "none";
+    try {
+        const response = await fetch('/classify-text', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({ text: inputText }),
+        });
+        const data = await response.json();
+        loader.style.display = "none";
+        if (response.ok) {
+            successMessage.style.display = "block";
+            successMessage.textContent = data.message;
+        } else {
+            throw new Error(data.detail || 'An error occurred');
+        }
+    } catch (error) {
+        loader.style.display = "none";
+        errorMessage.style.display = "block";
+        errorMessage.textContent = error.message;
+    }
 });

static/style.css CHANGED Viewed

@@ -1,45 +1,87 @@
 body {
-    --text: hsl(0 0% 15%);
-    padding: 2.5rem;
-    font-family: sans-serif;
-    color: var(--text);
-  }
-  body.dark-theme {
-    --text: hsl(0 0% 90%);
-    background-color: hsl(223 39% 7%);
-  }
-  main {
-    max-width: 80rem;
-    text-align: center;
-  }
-  section {
     display: flex;
-    flex-direction: column;
     align-items: center;
-  }
-  a {
-    color: var(--text);
-  }
-  form {
-    width: 30rem;
-    margin: 0 auto;
-  }
-  input {
     width: 100%;
-  }
-  button {
     cursor: pointer;
-  }
-  .text-gen-output {
-    min-height: 1.2rem;
-    margin: 1rem;
-    border: 0.5px solid grey;
-  }

 body {
+    font-family: Arial, sans-serif;
+    background-color: #f5f5f5;
+    padding: 20px;
     display: flex;
+    justify-content: center;
     align-items: center;
+    height: 100vh;
+    margin: 0;
+}
+.container {
+    background-color: #fff;
+    border-radius: 8px;
+    box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
+    padding: 30px;
+    max-width: 500px;
+    width: 100%;
+}
+h1 {
+    text-align: center;
+    color: #333;
+}
+textarea {
+    width: 100%;
+    height: 120px;
+    padding: 10px;
+    margin-top: 10px;
+    font-size: 16px;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    resize: none;
+}
+button {
     width: 100%;
+    padding: 10px;
+    background-color: #4CAF50;
+    color: white;
+    border: none;
+    border-radius: 4px;
+    font-size: 18px;
     cursor: pointer;
+    margin-top: 15px;
+}
+button:hover {
+    background-color: #45a049;
+}
+.result {
+    margin-top: 20px;
+    padding: 15px;
+    border-radius: 4px;
+    font-size: 16px;
+    display: none;
+}
+.success {
+    background-color: #e7f9e7;
+    color: #2d7a2d;
+    border: 1px solid #4CAF50;
+}
+.error {
+    background-color: #fdecea;
+    color: #a94442;
+    border: 1px solid #ebccd1;
+}
+.loader {
+    border: 6px solid #f3f3f3;
+    border-radius: 50%;
+    border-top: 6px solid #4CAF50;
+    width: 40px;
+    height: 40px;
+    animation: spin 1s linear infinite;
+    margin: 20px auto;
+    display: none;
+}
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}