Update utils/model.py

utils/model.py

@@ -1,47 +1,35 @@
-from transformers import pipeline, AutoModelForTimeSeriesPrediction
+from transformers import AutoModelForTimeSeriesPrediction, pipeline
 import torch
 import numpy as np
-import pandas as pd
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 def predict_umkm(data):
     try:
-        # ===== 1. Validasi Input =====
-        if not isinstance(data, pd.DataFrame):
-            data = pd.DataFrame(data)
-            
-        required_cols = ['demand', 'supply', 'harga']
-        for col in required_cols:
-            if col not in data.columns:
-                raise ValueError(f"Kolom {col} tidak ditemukan")
+        # ===== 1. Validasi Data =====
+        demand_values = data['demand'].values.astype(float)
+        if len(demand_values) < 3:
+            raise ValueError("Data historis terlalu pendek (min 3 titik)")
         
         # ===== 2. GRANITE-TTM Forecasting =====
-        # Konversi data ke tensor dengan format khusus
-        values = torch.tensor(
-            data['demand'].astype(float).values,
-            dtype=torch.float32
-        ).unsqueeze(0).unsqueeze(-1).to(device)  # Shape: [1, seq_len, 1]
-        
-        # Load model (pastikan nama model benar)
-        ttm_model = AutoModelForTimeSeriesPrediction.from_pretrained(
+        # Format input khusus untuk model time series IBM
+        inputs = {
+            "past_values": torch.tensor(demand_values, dtype=torch.float32).unsqueeze(0).to(device),
+            "static_categorical_features": torch.zeros(1, 1, dtype=torch.long).to(device)
+        }
+        
+        # Load model dengan config yang benar
+        model = AutoModelForTimeSeriesPrediction.from_pretrained(
             "ibm/granite-timeseries-ttm-r2",
             trust_remote_code=True
         ).to(device)
         
-        # Generate predictions
+        # Generate prediksi
         with torch.no_grad():
-            predictions = ttm_model.generate(
-                inputs=values,
-                max_length=min(7, len(data)+3)  # Prediksi maks 7 hari
-            )
-        
-        # Post-processing output
-        demand_pred = predictions.cpu().numpy().squeeze().tolist()
-        if isinstance(demand_pred, float):
-            demand_pred = [demand_pred]  # Convert single value to list
+            outputs = model(**inputs)
+            predictions = outputs.last_hidden_state.mean(dim=1).squeeze()
         
-        # ===== 3. Chronos-T5 Decision =====
+        # ===== 3. Format untuk Chronos-T5 =====
         chronos = pipeline(
             "text-generation",
             model="amazon/chronos-t5-small",
@@ -50,26 +38,18 @@ def predict_umkm(data):
         
         prompt = f"""
         [INSTRUCTION]
-        Berikan rekomendasi inventory untuk UMKM berdasarkan:
-        - Prediksi demand 7 hari: {demand_pred[:7]}
-        - Stok saat ini: {data['supply'].iloc[-1]:.0f}
-        - Trend: {'naik' if demand_pred[-1] > demand_pred[0] else 'turun'}
+        Berikan rekomendasi stok untuk 7 hari ke depan berdasarkan:
+        - Prediksi demand: {predictions.cpu().numpy().tolist()[:7]}
+        - Stok saat ini: {data['supply'].iloc[-1]}
+        - Tren: {'↑' if predictions[-1] > predictions[0] else '↓'}
         
         [FORMAT]
-        - Gunakan bahasa Indonesia
-        - Maksimal 1 kalimat
-        - Sertakan angka konkret
+        1 kalimat dengan angka spesifik
         [/FORMAT]
         """
         
-        recommendation = chronos(
-            prompt,
-            max_new_tokens=50,
-            do_sample=True,
-            temperature=0.7
-        )[0]['generated_text']
-        
-        return recommendation.split("[/FORMAT]")[-1].strip()
+        result = chronos(prompt, max_new_tokens=50)[0]['generated_text']
+        return result.split("[/FORMAT]")[-1].strip()
     
     except Exception as e:
-        return f"🚨 Error: {str(e)}"
+        return f"⚠️ Kesalahan sistem: {str(e)}"