Upload app.py

app.py

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+import streamlit as st
+import os
+import gdown
+from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
+from safetensors.torch import load_file
+import torch
+
+# ================================
+# 1. Google Drive FILE ID (model.safetensors)
+# ================================
+FILE_ID = "1eMR7jxkj5XLLIV6t9IIllpfegxHWCi_A"
+MODEL_DIR = "model_folder"
+MODEL_FILE = os.path.join(MODEL_DIR, "model.safetensors")
+
+# ================================
+# 2. Download model file (jika belum ada)
+# ================================
+if not os.path.exists(MODEL_DIR):
+    os.makedirs(MODEL_DIR, exist_ok=True)
+
+if not os.path.exists(MODEL_FILE):
+    st.write("Mengunduh model.safetensors dari Google Drive...")
+    url = f"https://drive.google.com/uc?id={FILE_ID}"
+    gdown.download(url, MODEL_FILE, quiet=False)
+    st.success("model.safetensors berhasil di-download!")
+
+# ================================
+# 3. Load model & tokenizer TANPA META MODE
+# ================================
+st.write("Memuat model...")
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+
+# 3A — Load config
+config = AutoConfig.from_pretrained(MODEL_DIR)
+
+# 3B — Buat model kosong
+model = AutoModelForSequenceClassification.from_config(config)
+
+# 3C — Load bobot SAFETENSORS
+state_dict = load_file(MODEL_FILE)
+model.load_state_dict(state_dict, strict=True)
+
+model.to("cpu")
+model.eval()
+
+st.success("Model siap digunakan!")
+
+# ================================
+# 4. Label Mapping
+# ================================
+label_map = {
+    0: "Negatif",
+    1: "Positif"
+}
+
+# ================================
+# 5. Streamlit UI
+# ================================
+st.title("🚀 Klasifikasi Kalimat dengan Model dari Google Drive")
+
+text = st.text_area("Masukkan kalimat:")
+
+if st.button("Klasifikasi"):
+    if text.strip() == "":
+        st.warning("Tolong masukkan kalimat terlebih dahulu.")
+    else:
+        # Tokenisasi
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+        # Prediksi
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.softmax(outputs.logits, dim=1)
+
+        pred_tensor = torch.argmax(probs, dim=1)
+        pred = int(pred_tensor.cpu().numpy()[0])
+
+        # Ambil label
+        label = label_map.get(pred, "Unknown")
+
+        st.subheader("Hasil Prediksi:")
+        st.write("Kelas:", f"**{label}**")
+        st.write("Probabilitas:", probs.tolist()[0])