Update app.py

app.py

@@ -108,6 +108,29 @@ def predict_single(features: Dict) -> Dict:
     except Exception as e:
         return {"error": str(e)}
 
+def predict_from_dict(
+    koi_period: float, koi_duration: float, koi_depth: float, 
+    koi_prad: float, koi_srad: float, koi_teq: float,
+    koi_steff: float, koi_slogg: float, koi_smet: float,
+    koi_kepmag: float, koi_model_snr: float, koi_num_transits: float
+) -> Dict:
+    """Wrapper que toma parámetros individuales y los convierte a dict"""
+    features = {
+        "koi_period": koi_period,
+        "koi_duration": koi_duration,
+        "koi_depth": koi_depth,
+        "koi_prad": koi_prad,
+        "koi_srad": koi_srad,
+        "koi_teq": koi_teq,
+        "koi_steff": koi_steff,
+        "koi_slogg": koi_slogg,
+        "koi_smet": koi_smet,
+        "koi_kepmag": koi_kepmag,
+        "koi_model_snr": koi_model_snr,
+        "koi_num_transits": koi_num_transits
+    }
+    return predict_single(features)
+
 def predict_toi_realtime():
     """Función para TOI en tiempo real"""
     try:
@@ -126,20 +149,64 @@ def predict_toi_realtime():
         if toi_df.empty:
             return "❌ No se encontraron objetos TOI"
         
-        # Procesar datos (código simplificado)
+        # Tomar muestra
         toi_sample = toi_df.sample(min(3, len(toi_df)), random_state=7)
+        toi_sample.columns = [c.strip().lower() for c in toi_sample.columns]
+        
+        # Mapeo de sinónimos
+        candidates_map = {
+            "koi_period": ["pl_orbper", "tce_period", "orbper", "period"],
+            "koi_duration": ["pl_trandurh", "tce_duration", "tran_dur", "trandur", "duration", "dur"],
+            "koi_depth": ["pl_trandep", "tce_depth", "depth", "trandep"],
+            "koi_prad": ["pl_rade", "prad", "rade", "planet_radius"],
+            "koi_srad": ["st_rad", "srad", "stellar_radius", "star_radius"],
+            "koi_teq": ["pl_eqt", "teq", "equilibrium_temp"],
+            "koi_steff": ["st_teff", "teff", "stellar_teff", "effective_temp"],
+            "koi_slogg": ["st_logg", "logg", "slogg"],
+            "koi_smet": ["st_met", "feh", "metallicity", "smet"],
+            "koi_kepmag": ["st_tmag", "tmag", "kepmag", "koi_kepmag"],
+            "koi_model_snr": ["tce_model_snr", "model_snr", "snr"],
+            "koi_num_transits": ["tce_num_transits", "num_transits", "ntransits", "tran_count"]
+        }
+        
+        def first_present(candidates, cols_set):
+            for name in candidates:
+                if name in cols_set:
+                    return name
+            for name in candidates:
+                found = [c for c in cols_set if name in c]
+                if found:
+                    return found[0]
+            return None
         
-        # Simular predicciones para ejemplo
+        cols_set = set(toi_sample.columns)
         results = []
-        for i in range(len(toi_sample)):
-            result = predict_single({})
-            results.append({
-                "TOI": f"TOI-{i+1}",
-                "Predicción": result["prediction"],
-                "P(Confirmado)": f"{result['probabilities']['CONFIRMED']:.3f}",
-                "P(Candidato)": f"{result['probabilities']['CANDIDATE']:.3f}",
-                "P(Falso Positivo)": f"{result['probabilities']['FALSE_POSITIVE']:.3f}"
-            })
+        
+        for idx, row in toi_sample.iterrows():
+            # Preparar características
+            features = {}
+            for feat in feature_columns:
+                src = first_present(candidates_map.get(feat, []), cols_set)
+                if src and src in row and pd.notna(row[src]):
+                    features[feat] = float(row[src])
+                else:
+                    features[feat] = train_medians.get(feat, 0)
+            
+            # Predecir
+            result = predict_single(features)
+            
+            if "error" not in result:
+                results.append({
+                    "TOI": row.get('toi', f"TOI-{idx}"),
+                    "Disposición": row.get('tfopwg_disp', 'Unknown'),
+                    "Predicción": result['prediction'],
+                    "P(Confirmado)": f"{result['probabilities']['CONFIRMED']:.3f}",
+                    "P(Candidato)": f"{result['probabilities']['CANDIDATE']:.3f}",
+                    "P(Falso Positivo)": f"{result['probabilities']['FALSE_POSITIVE']:.3f}"
+                })
+        
+        if not results:
+            return "❌ No se pudieron generar predicciones"
         
         result_df = pd.DataFrame(results)
         return f"**Predicciones TOI:**\n\n{result_df.to_markdown(index=False)}"
@@ -147,17 +214,14 @@ def predict_toi_realtime():
     except Exception as e:
         return f"❌ Error: {str(e)}"
 
-def predict_manual(period, duration, depth, prad, srad, teq, steff, slogg, smet, kepmag, snr, num_transits):
+def predict_manual(
+    period, duration, depth, prad, srad, teq, steff, slogg, smet, kepmag, snr, num_transits
+):
     """Función para predicción manual en Gradio"""
     try:
-        features = {
-            "koi_period": period, "koi_duration": duration, "koi_depth": depth,
-            "koi_prad": prad, "koi_srad": srad, "koi_teq": teq,
-            "koi_steff": steff, "koi_slogg": slogg, "koi_smet": smet,
-            "koi_kepmag": kepmag, "koi_model_snr": snr, "koi_num_transits": num_transits
-        }
-        
-        result = predict_single(features)
+        result = predict_from_dict(
+            period, duration, depth, prad, srad, teq, steff, slogg, smet, kepmag, snr, num_transits
+        )
         
         if "error" in result:
             return f"❌ {result['error']}"
@@ -173,18 +237,11 @@ def predict_manual(period, duration, depth, prad, srad, teq, steff, slogg, smet,
 
 # ==================== INTERFAZ GRADIO ====================
 
-# Crear inputs para la interfaz
-inputs = []
-for feature in feature_columns:
-    default_value = train_medians.get(feature, 0)
-    inputs.append(gr.Number(label=feature, value=default_value))
-
-# Crear la interfaz
 with gr.Blocks(theme=gr.themes.Soft(), title="Eco Finder API") as demo:
     gr.Markdown("# 🌌 Eco Finder API")
-    gr.Markdown("Clasificador de exoplanetas - Usa la interfaz o consume la API")
+    gr.Markdown("Clasificador de exoplanetas")
     
-    with gr.Tab("🎯 API Predict"):
+    with gr.Tab("🎯 Predicción API"):
         gr.Markdown("### Endpoint para consumo desde frontend")
         gr.Markdown("""
         **URL:** `https://jarpalucas-echo-finder-api.hf.space/api/predict`
@@ -192,21 +249,28 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Eco Finder API") as demo:
         **Método:** POST
         **Content-Type:** application/json
         
-        **Ejemplo de uso:**
-        ```javascript
-        fetch('https://jarpalucas-echo-finder-api.hf.space/api/predict', {
-          method: 'POST',
-          headers: {'Content-Type': 'application/json'},
-          body: JSON.stringify({
-            koi_period: 10.0,
-            koi_duration: 5.0,
-            // ... todos los parámetros
-          })
-        })
+        **Ejemplo de uso con curl:**
+        ```bash
+        curl -X POST "https://jarpalucas-echo-finder-api.hf.space/api/predict" \\
+          -H "Content-Type: application/json" \\
+          -d '{
+            "koi_period": 10.0,
+            "koi_duration": 5.0,
+            "koi_depth": 1000.0,
+            "koi_prad": 2.0,
+            "koi_srad": 1.0,
+            "koi_teq": 1000.0,
+            "koi_steff": 6000.0,
+            "koi_slogg": 4.5,
+            "koi_smet": 0.0,
+            "koi_kepmag": 12.0,
+            "koi_model_snr": 10.0,
+            "koi_num_transits": 3.0
+          }'
         ```
         """)
         
-        # Inputs para API
+        # Inputs para probar la API localmente
         with gr.Row():
             with gr.Column():
                 period = gr.Number(label="koi_period", value=10.0)
@@ -224,11 +288,11 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Eco Finder API") as demo:
                 snr = gr.Number(label="koi_model_snr", value=10.0)
                 num_transits = gr.Number(label="koi_num_transits", value=3.0)
         
-        api_btn = gr.Button("🚀 Probar API Localmente")
+        api_btn = gr.Button("🚀 Probar Predicción")
         api_output = gr.JSON()
         
         api_btn.click(
-            fn=predict_single,
+            fn=predict_from_dict,
             inputs=[period, duration, depth, prad, srad, teq, steff, slogg, smet, kepmag, snr, num_transits],
             outputs=api_output
         )
@@ -249,11 +313,6 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Eco Finder API") as demo:
             outputs=manual_output
         )
 
-# ==================== ENDPOINTS API ====================
-
-# Gradio automáticamente crea endpoints en /api/predict
-# cuando usamos funciones con inputs/outputs definidos
-
 print("🎉 Aplicación iniciada correctamente!")
 print("🌐 Interfaz disponible en: /")
 print("🔗 Endpoint API disponible en: /api/predict")