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GitHub Actions commited on Oct 9, 2025

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models/.DS_Store +0 -0
models/city_to_idx.json +1 -0
models/model.keras +3 -0
models/scalers/scaler_dyn_global.pkl +1 -1
models/scalers/scaler_static_global.pkl +1 -1
models/scalers/scaler_target_global.pkl +2 -2
predict.py +188 -139

models/.DS_Store CHANGED Viewed

Binary files a/models/.DS_Store and b/models/.DS_Store differ

models/city_to_idx.json ADDED Viewed

	@@ -0,0 +1 @@

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predict.py CHANGED Viewed

@@ -1,33 +1,38 @@
 import os
 import numpy as np
 import pandas as pd
-import joblib
 import warnings
 from pathlib import Path
 from datetime import timedelta
 import tensorflow as tf
 import matplotlib.pyplot as plt
-import base64
-from io import BytesIO
-from huggingface_hub import hf_hub_download
-warnings.filterwarnings('ignore')
 plt.style.use('seaborn-v0_8-darkgrid')
-# --- MUDANÇA: Definição da loss customizada necessária para carregar o modelo ---
 def asymmetric_mse(y_true, y_pred):
-    penalty_factor = 5.0
     error = y_true - y_pred
-    penalty = tf.where(error > 0, penalty_factor, 1.0)
     loss = tf.square(error) * penalty
     return tf.reduce_mean(loss)
 class DenguePredictor:
     def __init__(self, project_root=None):
         self.project_root = Path(project_root) if project_root else Path(__file__).resolve().parent
-        # --- MUDANÇA: Constantes do modelo alinhadas com o treinamento final ---
         self.sequence_length = 12
-        self.horizon = 8
         self.year_min_train = 2014
         self.year_max_train = 2025
         self.dynamic_features = [
@@ -37,167 +42,211 @@ class DenguePredictor:
         ]
         self.static_features = ["latitude", "longitude"]
         self.feature_names_pt = {
-            "numero_casos": "Nº de Casos de Dengue", "T2M": "Temperatura Média (°C)",
             "PRECTOTCORR": "Precipitação (mm)"
         }
         self.load_assets()
     def load_assets(self):
-        print("INFO: Carregando todos os ativos da IA (modelo, scalers, dados)...")
-        AI_ASSETS_DIR = self.project_root / "models"
-        INFERENCE_PATH = hf_hub_download(
-            repo_id='previdengue/predict_inference_data',
-            filename='inference_data.parquet',
-            repo_type='dataset',
-            token=os.environ.get('HF_TOKEN')
         )
-        SCALER_DIR = AI_ASSETS_DIR / "scalers"
-        MODEL_PATH = AI_ASSETS_DIR / "checkpoints" / "model_checkpoint_best_city.keras"
-        # --- MUDANÇA: Carregamento dos scalers GLOBAIS ---
-        self.scaler_dyn = joblib.load(SCALER_DIR / "scaler_dyn_global.pkl")
-        self.scaler_static = joblib.load(SCALER_DIR / "scaler_static_global.pkl")
-        self.scaler_target = joblib.load(SCALER_DIR / "scaler_target_global.pkl")
-        # Lê os dados de inferência do arquivo baixado
-        df_master = pd.read_parquet(INFERENCE_PATH)
-        df_master['codigo_ibge'] = df_master['codigo_ibge'].astype(int)
-        df_master['date'] = pd.to_datetime(df_master['ano'].astype(str) + df_master['semana'].astype(str) + '0', format='%Y%W%w', errors='coerce')
-        df_master = df_master.sort_values(by=['codigo_ibge', 'date']).reset_index(drop=True)
-        self.df_master = df_master
-        self.municipios = df_master[['codigo_ibge', 'municipio']].drop_duplicates().sort_values('codigo_ibge')
-        # --- MUDANÇA: Carregamento do modelo com a loss customizada ---
-        self.model = tf.keras.models.load_model(MODEL_PATH, custom_objects={'asymmetric_mse': asymmetric_mse})
-        print("INFO: Ativos da IA carregados com sucesso.")
     def plot_to_base64(self, fig):
         buf = BytesIO()
-        fig.savefig(buf, format='png', bbox_inches='tight', facecolor='#18181b')
         buf.seek(0)
-        img_str = base64.b64encode(buf.read()).decode('utf-8')
         plt.close(fig)
         return img_str
-    def predict(self, ibge_code: int):
-        df_mun = self.df_master[self.df_master['codigo_ibge'] == ibge_code].copy()
         if df_mun.empty or len(df_mun) < self.sequence_length:
-            raise ValueError(f"Não há dados ou histórico suficiente para o município {ibge_code}")
-        municipio_name = self.municipios[self.municipios['codigo_ibge'] == ibge_code].iloc[0]['municipio']
-        # 1. Pega a última sequência de dados históricos completos
-        last_complete_sequence = df_mun.dropna(subset=['numero_casos']).tail(self.sequence_length).copy()
-        if len(last_complete_sequence) < self.sequence_length:
-            raise ValueError(f"Histórico insuficiente de casos conhecidos para {ibge_code}")
-        # 2. Engenharia de Features na sequência de entrada
-        last_complete_sequence['casos_velocidade'] = last_complete_sequence['numero_casos'].diff().fillna(0)
-        last_complete_sequence['casos_aceleracao'] = last_complete_sequence['casos_velocidade'].diff().fillna(0)
-        last_complete_sequence['casos_mm_4_semanas'] = last_complete_sequence['numero_casos'].rolling(4, min_periods=1).mean()
-        last_complete_sequence['week_sin'] = np.sin(2 * np.pi * last_complete_sequence['semana'] / 52)
-        last_complete_sequence['week_cos'] = np.cos(2 * np.pi * last_complete_sequence['semana'] / 52)
-        last_complete_sequence['year_norm'] = (last_complete_sequence['ano'] - self.year_min_train) / (self.year_max_train - self.year_min_train)
-        # 3. Prepara os dados de entrada para o modelo
-        dynamic_input_raw = last_complete_sequence[self.dynamic_features].values
-        static_input_raw = last_complete_sequence[self.static_features].iloc[-1].values.reshape(1, -1)
-        dynamic_input_scaled = self.scaler_dyn.transform(dynamic_input_raw).reshape(1, self.sequence_length, -1)
-        static_input_scaled = self.scaler_static.transform(static_input_raw)
-        # 4. Faz a predição (tiro único)
-        predictions_scaled = self.model.predict([dynamic_input_scaled, static_input_scaled], verbose=0)
-        pred_casos_scaled = predictions_scaled[0] # Primeira saída do modelo
-        # 5. Inverte a transformação para obter o número de casos reais
-        pred_casos_log = self.scaler_target.inverse_transform(pred_casos_scaled.reshape(1, -1))
-        pred_casos_real = np.expm1(pred_casos_log).flatten()
-        predictions_final = [max(0, round(case)) for case in pred_casos_real]
-        # 6. Formata a resposta
-        last_real_date = last_complete_sequence['date'].iloc[-1]
-        predicted_data = [{
-            "date": (last_real_date + timedelta(weeks=i + 1)).strftime('%Y-%m-%d'),
-            "predicted_cases": cases
-        } for i, cases in enumerate(predictions_final)]
-        historic_data = [{
-            "date": row['date'].strftime('%Y-%m-%d'),
-            "cases": int(row["numero_casos"]) if pd.notna(row["numero_casos"]) else None
-        } for _, row in df_mun.tail(52).iterrows()]
-        # Geração de insights (Análise de Lag)
         lag_plot_b64, strategic_summary, tipping_points = self.generate_lag_insights(df_mun)
         return {
-            "municipality_name": municipio_name,
             "historic_data": historic_data,
             "predicted_data": predicted_data,
-            "insights": {
-                "lag_analysis_plot_base64": lag_plot_b64,
-                "strategic_summary": strategic_summary,
-                "tipping_points": tipping_points
-            }
         }
     def generate_lag_insights(self, df_mun):
-        # Renomeia as colunas para a análise e para os gráficos
-        df_analysis = df_mun.rename(columns={"T2M": "Temperatura Média (°C)", "PRECTOTCORR": "Precipitação (mm)"})
         max_lag = 12
-        cases_col_name = 'numero_casos'
-        lag_features = ['Temperatura Média (°C)', 'Precipitação (mm)']
         lag_correlations = {}
-        # Calcula a correlação para cada feature com diferentes defasagens (lags)
         for col in lag_features:
-            # Garante que a coluna existe antes de tentar usá-la
             if col in df_analysis.columns:
-                corrs = [df_analysis[cases_col_name].corr(df_analysis[col].shift(lag)) for lag in range(1, max_lag + 1)]
                 lag_correlations[col] = corrs
-        # Cria a figura para o gráfico
-        fig, ax = plt.subplots(figsize=(10, 6), facecolor='#18181b')
-        ax.set_facecolor('#18181b')
-        # Plota as correlações
         for feature_name, corrs in lag_correlations.items():
-            ax.plot(range(1, max_lag + 1), corrs, marker='o', linestyle='-', label=feature_name)
-        # Estiliza o gráfico
-        ax.set_title('Análise de Defasagem (Lag)', color='white')
-        ax.set_xlabel('Defasagem (Semanas)', color='white')
-        ax.set_ylabel('Correlação com Casos', color='white')
-        ax.tick_params(colors='white')
-        ax.legend(facecolor='#27272a', edgecolor='gray', labelcolor='white')
-        ax.grid(True, which='both', linestyle='--', linewidth=0.5, color='#444')
-        # Converte o gráfico para base64 para enviar na resposta da API
         lag_plot_b64 = self.plot_to_base64(fig)
-        # Encontra o pico de correlação para cada feature
-        lag_peaks = {
-            feature: (np.argmax(np.abs(corrs)) + 1) if corrs and not all(pd.isna(corrs)) else 'N/A'
-            for feature, corrs in lag_correlations.items()
-        }
-        temp_lag = lag_peaks.get('Temperatura Média (°C)', 'N/A')
-        rain_lag = lag_peaks.get('Precipitação (mm)', 'N/A')
-        # Cria um resumo estratégico
         summary = (
-            f"A IA identifica a **Temperatura** e a **Precipitação** como os principais gatilhos climáticos. "
-            f"O impacto da temperatura tende a ser máximo após **{temp_lag} semanas**, enquanto o da chuva ocorre após **{rain_lag} semanas**. "
-            "Ações preventivas devem ser intensificadas nesta janela após eventos climáticos extremos."
         )
-        # Cria os pontos-chave (tipping points)
         tipping_points = [
-            {"factor": "Temperatura", "value": f"Impacto máximo em {temp_lag} semanas"},
-            {"factor": "Precipitação", "value": f"Impacto máximo em {rain_lag} semanas"},
-            {"factor": "Umidade", "value": "Aumenta a sobrevida do mosquito adulto"},
         ]
-        return lag_plot_b64, summary, tipping_points

 import os
+import json
+import joblib
 import numpy as np
 import pandas as pd
 import warnings
 from pathlib import Path
 from datetime import timedelta
+from io import BytesIO
+import base64
 import tensorflow as tf
+from tensorflow.keras.utils import register_keras_serializable
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
+from huggingface_hub import hf_hub_download
+warnings.filterwarnings("ignore")
 plt.style.use('seaborn-v0_8-darkgrid')
+@register_keras_serializable(package="Custom", name="asymmetric_mse")
 def asymmetric_mse(y_true, y_pred):
+    penalty_factor = 10.0
     error = y_true - y_pred
+    denom = tf.maximum(tf.abs(y_true), 1.0)
+    rel = tf.abs(error) / denom
+    penalty = tf.where(error > 0, 1.0 + penalty_factor * rel, 1.0)
     loss = tf.square(error) * penalty
     return tf.reduce_mean(loss)
 class DenguePredictor:
     def __init__(self, project_root=None):
         self.project_root = Path(project_root) if project_root else Path(__file__).resolve().parent
         self.sequence_length = 12
+        self.horizon = 6
         self.year_min_train = 2014
         self.year_max_train = 2025
         self.dynamic_features = [
         ]
         self.static_features = ["latitude", "longitude"]
         self.feature_names_pt = {
+            "numero_casos": "Nº de Casos de Dengue",
+            "T2M": "Temperatura Média (°C)",
             "PRECTOTCORR": "Precipitação (mm)"
         }
+        self._loaded = False
         self.load_assets()
     def load_assets(self):
+        models_dir = self.project_root / "models"
+        scalers_dir = models_dir / "scalers"
+        model_path = models_dir / "model.keras"
+        city_map_path = models_dir / "city_to_idx.json"
+        if not scalers_dir.exists():
+            raise FileNotFoundError(str(scalers_dir) + " not found")
+        self.scaler_dyn = joblib.load(scalers_dir / "scaler_dyn_global.pkl")
+        self.scaler_static = joblib.load(scalers_dir / "scaler_static_global.pkl")
+        self.scaler_target = joblib.load(scalers_dir / "scaler_target_global.pkl")
+        if city_map_path.exists():
+            with open(city_map_path, "r", encoding="utf-8") as fh:
+                self.city_to_idx = {int(k): int(v) for k, v in json.load(fh).items()}
+        else:
+            self.city_to_idx = {}
+        hf_token = os.environ.get("HF_TOKEN")
+        inference_path = hf_hub_download(
+            repo_id="previdengue/predict_inference_data",
+            filename="inference_data.parquet",
+            repo_type="dataset",
+            token=hf_token
         )
+        df = pd.read_parquet(inference_path)
+        df["codigo_ibge"] = df["codigo_ibge"].astype(int)
+        df["ano"] = df["ano"].astype(int)
+        df["semana"] = df["semana"].astype(int)
+        try:
+            df["date"] = pd.to_datetime(df["ano"].astype(str) + df["semana"].astype(str) + "0", format="%Y%W%w", errors="coerce")
+        except Exception:
+            df["date"] = pd.NaT
+        df = df.sort_values(by=["codigo_ibge", "date"]).reset_index(drop=True)
+        df["week_sin"] = np.sin(2 * np.pi * df["semana"] / 52)
+        df["week_cos"] = np.cos(2 * np.pi * df["semana"] / 52)
+        df["year_norm"] = (df["ano"] - self.year_min_train) / (self.year_max_train - self.year_min_train)
+        self.df_master = df
+        self.municipios = df[["codigo_ibge", "municipio"]].drop_duplicates().sort_values("codigo_ibge")
+        if not model_path.exists():
+            raise FileNotFoundError(str(model_path) + " not found")
+        self.model = tf.keras.models.load_model(model_path, custom_objects={"asymmetric_mse": asymmetric_mse}, compile=False)
+        self._loaded = True
     def plot_to_base64(self, fig):
         buf = BytesIO()
+        fig.savefig(buf, format="png", bbox_inches="tight", facecolor=fig.get_facecolor())
         buf.seek(0)
+        img_str = base64.b64encode(buf.read()).decode("utf-8")
         plt.close(fig)
         return img_str
+    def _prepare_sequence(self, df_mun):
+        df_seq = df_mun.tail(self.sequence_length).copy()
+        df_seq["casos_velocidade"] = df_seq["numero_casos"].diff().fillna(0)
+        df_seq["casos_aceleracao"] = df_seq["casos_velocidade"].diff().fillna(0)
+        df_seq["casos_mm_4_semanas"] = df_seq["numero_casos"].rolling(4, min_periods=1).mean()
+        df_seq["week_sin"] = np.sin(2 * np.pi * df_seq["semana"] / 52)
+        df_seq["week_cos"] = np.cos(2 * np.pi * df_seq["semana"] / 52)
+        df_seq["year_norm"] = (df_seq["ano"] - self.year_min_train) / (self.year_max_train - self.year_min_train)
+        return df_seq
+    def predict(self, ibge_code: int, show_plot=False, display_history_weeks=52):
+        if not self._loaded:
+            raise RuntimeError("assets not loaded")
+        df_mun = self.df_master[self.df_master["codigo_ibge"] == int(ibge_code)].copy().reset_index(drop=True)
         if df_mun.empty or len(df_mun) < self.sequence_length:
+            raise ValueError(f"No data or insufficient history for ibge {ibge_code}")
+        municipio_row = self.municipios[self.municipios["codigo_ibge"] == int(ibge_code)]
+        municipality_name = municipio_row.iloc[0]["municipio"] if not municipio_row.empty else str(ibge_code)
+        df_mun_clean = df_mun.dropna(subset=["numero_casos"]).reset_index(drop=True)
+        if len(df_mun_clean) < self.sequence_length:
+            raise ValueError(f"Insufficient known-case history for {ibge_code}")
+        seq_df = self._prepare_sequence(df_mun_clean)
+        if len(seq_df) < self.sequence_length:
+            raise ValueError(f"Insufficient sequence length for {ibge_code}")
+        dynamic_raw = seq_df[self.dynamic_features].values
+        static_raw = seq_df[self.static_features].iloc[-1].values.reshape(1, -1)
+        dynamic_scaled = self.scaler_dyn.transform(dynamic_raw).reshape(1, self.sequence_length, -1)
+        static_scaled = self.scaler_static.transform(static_raw)
+        city_idx = int(self.city_to_idx.get(int(ibge_code), 0))
+        city_input = np.array([[city_idx]], dtype=np.int32)
+        y_pred = self.model.predict([dynamic_scaled, static_scaled, city_input], verbose=0)
+        if isinstance(y_pred, (list, tuple)):
+            y_pred_reg = y_pred[0]
+        else:
+            y_pred_reg = y_pred
+        y_pred_flat = y_pred_reg.reshape(-1, 1)
+        y_pred_inv_flat = self.scaler_target.inverse_transform(y_pred_flat)
+        y_pred_inv = y_pred_inv_flat.reshape(y_pred_reg.shape)
+        pred_values = y_pred_inv.flatten()
+        pred_values = np.maximum(pred_values, 0.0)
+        last_known_case = seq_df["numero_casos"].iloc[-1]
+        connected_prediction = np.insert(pred_values, 0, last_known_case)
+        last_real_date = seq_df["date"].iloc[-1] if "date" in seq_df.columns else None
+        predicted_data = []
+        for i, val in enumerate(connected_prediction[1:]):
+            pred_date = (last_real_date + timedelta(weeks=i + 1)).strftime("%Y-%m-%d") if pd.notna(last_real_date) else None
+            predicted_data.append({"date": pred_date, "predicted_cases": int(round(float(val)))})
+        hist_tail = df_mun.tail(min(len(df_mun), display_history_weeks)).copy()
+        historic_data = []
+        for _, row in hist_tail.iterrows():
+            historic_data.append({
+                "date": row["date"].strftime("%Y-%m-%d") if pd.notna(row.get("date")) else None,
+                "cases": int(row["numero_casos"]) if pd.notna(row.get("numero_casos")) else None
+            })
         lag_plot_b64, strategic_summary, tipping_points = self.generate_lag_insights(df_mun)
+        insights = {
+            "lag_analysis_plot_base64": lag_plot_b64,
+            "strategic_summary": strategic_summary,
+            "tipping_points": tipping_points
+        }
         return {
+            "municipality_name": municipality_name,
+            "ibge": int(ibge_code),
+            "last_known_index": int(df_mun.index[-1]),
             "historic_data": historic_data,
             "predicted_data": predicted_data,
+            "insights": insights
         }
     def generate_lag_insights(self, df_mun):
+        df_analysis = df_mun.rename(columns={"T2M": "Temperature_C", "PRECTOTCORR": "Precipitation_mm"})
         max_lag = 12
+        cases_col = "numero_casos"
+        lag_features = ["Temperature_C", "Precipitation_mm"]
         lag_correlations = {}
         for col in lag_features:
             if col in df_analysis.columns:
+                corrs = []
+                for lag in range(1, max_lag + 1):
+                    try:
+                        corr = df_analysis[cases_col].corr(df_analysis[col].shift(lag))
+                    except Exception:
+                        corr = np.nan
+                    corrs.append(corr)
                 lag_correlations[col] = corrs
+            else:
+                lag_correlations[col] = [np.nan] * max_lag
+        fig, ax = plt.subplots(figsize=(10, 6), facecolor="#18181b")
+        ax.set_facecolor("#18181b")
         for feature_name, corrs in lag_correlations.items():
+            ax.plot(range(1, max_lag + 1), corrs, marker="o", linestyle="-", label=feature_name)
+        ax.set_title("Lag Analysis", color="white")
+        ax.set_xlabel("Lag (weeks)", color="white")
+        ax.set_ylabel("Correlation with cases", color="white")
+        ax.tick_params(colors="white")
+        ax.legend(facecolor="#27272a", edgecolor="gray", labelcolor="white")
+        ax.grid(True, which="both", linestyle="--", linewidth=0.5, color="#444")
         lag_plot_b64 = self.plot_to_base64(fig)
+        lag_peaks = {}
+        for feature, corrs in lag_correlations.items():
+            if corrs and not all(pd.isna(corrs)):
+                peak = int(np.nanargmax(np.abs(np.array(corrs))) + 1)
+            else:
+                peak = "N/A"
+            lag_peaks[feature] = peak
+        temp_lag = lag_peaks.get("Temperature_C", "N/A")
+        rain_lag = lag_peaks.get("Precipitation_mm", "N/A")
         summary = (
+            f"The model identifies Temperature and Precipitation as key climate triggers. "
+            f"Temperature shows maximum impact after {temp_lag} weeks and precipitation after {rain_lag} weeks. "
+            "Preventive actions should be intensified within these windows after extreme weather events."
         )
         tipping_points = [
+            {"factor": "Temperature", "value": f"Peak impact in {temp_lag} weeks"},
+            {"factor": "Precipitation", "value": f"Peak impact in {rain_lag} weeks"},
+            {"factor": "Humidity", "value": "Increases adult mosquito survival"}
         ]
+        return lag_plot_b64, summary, tipping_points