initial commit

app.py

@@ -1,110 +1,110 @@
-import torch
-from transformers import BertForSequenceClassification, BertTokenizer
-from safetensors.torch import load_file
-import gradio as gr
-
-# Load model dan tokenizer
-model_path = "/kaggle/input/model_prediction/other/default/1/model (2).safetensors"
-state_dict = load_file(model_path)
-
-model = BertForSequenceClassification.from_pretrained('indobenchmark/indobert-base-p2', num_labels=3)
-tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p2')
-
-model.load_state_dict(state_dict, strict=False)
-model.eval()  # Set model ke mode evaluasi
-
-# Fungsi deteksi stres dengan model
-def detect_stress(input_text):
-    # Tokenisasi input teks
-    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-
-    # Inference
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    # Mengambil prediksi
-    logits = outputs.logits
-    predicted_class = torch.argmax(logits, dim=1).item()
-
-    # Label, warna, dan pesan berdasarkan tingkat stres
-    labels = {
-        0: ("Tidak Stres", "#8BC34A", "Saat ini anda tidak mengalami stres. Tetap jaga kesehatan Anda!"),
-        1: ("Stres Ringan", "#FF7F00", "Saat ini anda sedang mengalami stres ringan. Luangkan waktu untuk relaksasi."),
-        2: ("Stres Berat", "#F44336", "Saat ini anda sedang mengalami stres berat. Mohon untuk segera melakukan konsultasi.")
-    }
-
-    level, color, message = labels[predicted_class]
-    return f"<div style='color: white; background-color: {color}; padding: 10px; border-radius: 5px;'>" \
-           f"Level stress anda : {level}<br>{message}" \
-           f"</div>"
-
-# Komponen Gradio
-with gr.Blocks(css="""
-body {
-    background-color: black;
-    color: white;
-    font-family: Arial, sans-serif;
-}
-.gradio-container {
-    width: 100%;
-    max-width: 600px;
-    margin: 0 auto;
-    text-align: center;
-}
-#title {
-    background-color: #ff7a33;
-    padding: 20px;
-    font-size: 24px;
-    font-weight: bold;
-}
-textarea {
-    background-color: #3a3a3a;
-    color: white;
-    border: none;
-    border-radius: 5px;
-    padding: 5px;
-    font-size: 14px;
-}
-
-textarea:focus {
-        border-color: #ff7a33 !important;
-}        
-.button_detect {
-    background-color: #ff7a33;
-    color: white;
-    border: none;
-    border-radius: 5px;
-    width: 20px;
-    height: 50px;
-    font-size: 14px;
-    cursor: pointer;
-}
-.button_detect:hover {
-    background-color: #e5662c;
-}
-""") as demo:
-    with gr.Row():
-        gr.Markdown("<h1 id='title'>Stress Detector</h1>")
-    
-    with gr.Row():
-        input_text = gr.Textbox(label="Masukkan teks", placeholder="Ketik sesuatu di sini...", lines=3)
-
-    # Tombol submit
-    with gr.Row():
-        btn_submit = gr.Button("Deteksi", elem_classes ="button_detect")
-    
-    with gr.Row():
-        output_label = gr.HTML(label="Hasil Deteksi")
-    
-    with gr.Row():
-        gr.Markdown(
-            "<p style='text-align: center;'>"
-            "0 untuk <b>Tidak Stress</b>, 1 untuk <b>Stress</b>, dan 2 untuk <b>Stress Berat</b>."
-            "</p>"
-        )
-
-    # Interaksi Gradio
-    btn_submit.click(fn=detect_stress, inputs=input_text, outputs=output_label)
-
-# Jalankan demo
-demo.launch()
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+from safetensors.torch import load_file
+import gradio as gr
+
+# Load model dan tokenizer
+model_path = "model (5).safetensors"
+state_dict = load_file(model_path)
+
+model = BertForSequenceClassification.from_pretrained('indobenchmark/indobert-base-p2', num_labels=3)
+tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p2')
+
+model.load_state_dict(state_dict, strict=False)
+model.eval()  # Set model ke mode evaluasi
+
+# Fungsi deteksi stres dengan model
+def detect_stress(input_text):
+    # Tokenisasi input teks
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+
+    # Inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Mengambil prediksi
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+    # Label, warna, dan pesan berdasarkan tingkat stres
+    labels = {
+        0: ("Tidak Stres", "#8BC34A", "Saat ini anda tidak mengalami stres. Tetap jaga kesehatan Anda!"),
+        1: ("Stres Ringan", "#FF7F00", "Saat ini anda sedang mengalami stres ringan. Luangkan waktu untuk relaksasi."),
+        2: ("Stres Berat", "#F44336", "Saat ini anda sedang mengalami stres berat. Mohon untuk segera melakukan konsultasi.")
+    }
+
+    level, color, message = labels[predicted_class]
+    return f"<div style='color: white; background-color: {color}; padding: 10px; border-radius: 5px;'>" \
+           f"Level stress anda : {level}<br>{message}" \
+           f"</div>"
+
+# Komponen Gradio
+with gr.Blocks(css="""
+body {
+    background-color: black;
+    color: white;
+    font-family: Arial, sans-serif;
+}
+.gradio-container {
+    width: 100%;
+    max-width: 600px;
+    margin: 0 auto;
+    text-align: center;
+}
+#title {
+    background-color: #ff7a33;
+    padding: 20px;
+    font-size: 24px;
+    font-weight: bold;
+}
+textarea {
+    background-color: #3a3a3a;
+    color: white;
+    border: none;
+    border-radius: 5px;
+    padding: 5px;
+    font-size: 14px;
+}
+
+textarea:focus {
+        border-color: #ff7a33 !important;
+}        
+.button_detect {
+    background-color: #ff7a33;
+    color: white;
+    border: none;
+    border-radius: 5px;
+    width: 20px;
+    height: 50px;
+    font-size: 14px;
+    cursor: pointer;
+}
+.button_detect:hover {
+    background-color: #e5662c;
+}
+""") as demo:
+    with gr.Row():
+        gr.Markdown("<h1 id='title'>Stress Detector</h1>")
+    
+    with gr.Row():
+        input_text = gr.Textbox(label="Masukkan teks", placeholder="Ketik sesuatu di sini...", lines=3)
+
+    # Tombol submit
+    with gr.Row():
+        btn_submit = gr.Button("Deteksi", elem_classes ="button_detect")
+    
+    with gr.Row():
+        output_label = gr.HTML(label="Hasil Deteksi")
+    
+    with gr.Row():
+        gr.Markdown(
+            "<p style='text-align: center;'>"
+            "0 untuk <b>Tidak Stress</b>, 1 untuk <b>Stress</b>, dan 2 untuk <b>Stress Berat</b>."
+            "</p>"
+        )
+
+    # Interaksi Gradio
+    btn_submit.click(fn=detect_stress, inputs=input_text, outputs=output_label)
+
+# Jalankan demo
+demo.launch()