Update app.py

app.py

@@ -1,58 +1,38 @@
 import gradio as gr
-import io
-import sys
-
-try:
-    from doctr.io import DocumentFile
-    from doctr.models import ocr_predictor
-except ImportError:
-    print("Error: Failed to import doctr. Please ensure it's installed correctly.")
-    print("Python version:", sys.version)
-    print("Python path:", sys.path)
-    raise
-
-# Initialize the OCR model
-try:
-    model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
-except Exception as e:
-    print(f"Error initializing OCR model: {e}")
-    raise
-
-def ocr_process(file):
-    try:
-        # Read the uploaded file
-        if file.name.lower().endswith('.pdf'):
-            doc = DocumentFile.from_pdf(file.name)
-        else:
-            # Assume it's an image if not PDF
-            image_stream = io.BytesIO(file.read())
-            doc = DocumentFile.from_images(image_stream)
-        
-        # Perform OCR
-        result = model(doc)
-        
-        # Extract text from the result
-        extracted_text = ""
-        for page in result.pages:
-            for block in page.blocks:
-                for line in block.lines:
-                    for word in line.words:
-                        extracted_text += word.value + " "
-                    extracted_text += "\n"
-                extracted_text += "\n"
-        
-        return extracted_text.strip()
-    except Exception as e:
-        return f"Error processing file: {str(e)}"
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=ocr_process,
-    inputs=gr.File(label="Upload PDF or Image"),
-    outputs=gr.Textbox(label="Extracted Text"),
-    title="OCR with doctr",
-    description="Upload a PDF or image file to extract text using OCR."
-)
-
-# Launch the interface
-iface.launch()
+from doctr.io import DocumentFile
+from doctr.models import ocr_predictor
+
+# Cargar el modelo preentrenado
+model = ocr_predictor(pretrained=True)
+
+def process_file(file):
+    """Procesa un archivo (PDF o imagen) con docTR y retorna el texto extraído."""
+    if file is None:
+        return "Por favor, sube un archivo."
+    
+    # Leer el archivo subido
+    doc = DocumentFile.from_pdf(file.name) if file.name.endswith('.pdf') else DocumentFile.from_images(file.name)
+
+    # Realizar OCR
+    result = model(doc)
+
+    # Extraer el texto y retornarlo
+    extracted_text = "\n".join([block['text'] for page in result.pages for block in page['blocks']])
+    return extracted_text
+
+# Configuración de la interfaz de Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## OCR con docTR")
+    gr.Markdown("Sube un archivo PDF o una imagen para extraer texto utilizando un modelo preentrenado de docTR.")
+
+    with gr.Row():
+        input_file = gr.File(label="Subir archivo (PDF o imagen)")
+        output_text = gr.Textbox(label="Texto extraído", lines=10)
+
+    process_button = gr.Button("Procesar archivo")
+
+    process_button.click(fn=process_file, inputs=[input_file], outputs=[output_text])
+
+# Ejecutar la app
+if __name__ == "__main__":
+    demo.launch()