update

app.py

@@ -1,30 +1,31 @@
-import gradio as gr
-import numpy as np
-import joblib
-import os
-
-# Ruta para cargar el modelo desde la carpeta 'model' dentro del directorio actual del repositorio
-model_path = os.path.join('model', 'svm_model.joblib')
-svm_model = joblib.load(model_path)
-
-def predict_signature(signature):
-    try:
-        # Convertir la entrada de texto a un array numpy
-        signature_array = np.array([float(x.replace(',', '.').strip()) for x in signature.split("\n") if x.strip()]).reshape(1, -1)
-        
-        # Predecir y devolver el resultado
-        prediction = svm_model.predict(signature_array)
-        return 'Java' if prediction[0] == 0 else 'Bangka Belitung'
-    except ValueError as e:
-        return f"Error in input: {e}"
-
-# Crear la interfaz de Gradio
-iface = gr.Interface(fn=predict_signature, 
-                     inputs=gr.Textbox(lines=2, placeholder="Paste the spectral signature here. Ensure that values are separated by newlines and decimals by commas."),
-                     outputs="text",
-                     title="Spectral Signature Classification",
-                     description="Paste the spectral signature into the text box to classify between Java and Bangka Belitung. Ensure that values are separated by newlines and decimals by commas.",
-                     examples=[["0,005666667\n0,005666667\n0,005666667\n..."]])
-
-# Ejecutar la aplicación y crear un enlace público
-iface.launch(share=True)
+import gradio as gr
+import numpy as np
+import joblib
+import os
+
+# Ruta para cargar el modelo desde la carpeta 'model' dentro del directorio actual del repositorio
+model_path = os.path.join('model', 'svm_model.joblib')
+svm_model = joblib.load(model_path)
+
+def predict_signature(signature):
+    try:
+        # Convertir la entrada de texto a un array numpy
+        signature_array = np.array([float(x.replace(',', '.').strip()) for x in signature.split("\n") if x.strip()]).reshape(1, -1)
+        
+        # Predecir y devolver el resultado
+        prediction = svm_model.predict(signature_array)
+        return 'Java' if prediction[0] == 0 else 'Bangka Belitung'
+    except ValueError as e:
+        return f"Error in input: {e}"
+
+# Crear la interfaz de Gradio
+iface = gr.Interface(fn=predict_signature, 
+                     inputs=gr.Textbox(lines=2, placeholder="Paste the spectral signature here. Ensure that values are separated by newlines and decimals by commas."),
+                     outputs="text",
+                     title="Spectral Signature Classification",
+                     description="Paste the spectral signature into the text box to classify between Java and Bangka Belitung. Ensure that values are separated by newlines and decimals by commas.",
+                     examples=[["0,005666667\n0,005666667\n0,005666667\n..."]])
+
+# Ejecutar la aplicación y crear un enlace público
+iface.launch(share=True)
+