Update app.py

app.py

@@ -1,11 +1,19 @@
+
+
 import gradio as gr
 import pandas as pd
 import easyocr
+from paddleocr import PaddleOCR
 from file_processing import FileProcessor
 from entity_recognition import process_text
 from utils import safe_dataframe
 
-reader = easyocr.Reader(['en'],download_enabled=True, gpu=True)  # Initialize OCR model
+
+# Initialize PaddleOCR globally (CPU mode)
+reader = PaddleOCR(use_angle_cls=True, lang="en") 
+
+# reader = easyocr.Reader(['en'],download_enabled=True, gpu=True)  # Initialize OCR model
+
 
 
 
@@ -15,17 +23,22 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         file_input = gr.File(label="📂 Upload Report")
-        # submit_btn = gr.Button("Extract")
+    submit_btn = gr.Button("Extract",visible=False)
+
+    welcome_msg=gr.Markdown("# Please Upload any lab report file 📂 and the processing will start automatically ")
+
+    def invisible():
+      return gr.update(visible=False)
+    file_input.upload(invisible,outputs=welcome_msg,api_name=False)
 
-    
-    # metadata_md = gr.Markdown("Report will show below....")
-    # submit_btn.click(fn=extract_it,inputs=file_input,outputs=metadata_md)
 
 
 
     @gr.render(inputs=file_input,triggers=[file_input.upload])
     def extract(file):
         """Processes the uploaded file and extracts medical data."""
+        # welcome_msg.update(visible=False)
+
 
         text = read_file(file.name, reader)  # Read the file (implement `read_file`)
         print("Performing NER...")
@@ -44,6 +57,7 @@ with gr.Blocks() as demo:
         print(f"Processed report for {metadata['patient_name']}")
         metadata_md = gr.Markdown(metadata_str)
 
+
         for test in output["report"]:
             test_type = test["test_type"]
             lab_tests = safe_dataframe(test,"lab_tests")
@@ -53,6 +67,7 @@ with gr.Blocks() as demo:
 
         gr.JSON(output,label="📜 Extracted Report")
         return output
-    
+    output_JSON=gr.JSON(visible=False)
+    submit_btn.click(extract,inputs=file_input,outputs=output_JSON,api_name="extract_report")
 
 demo.launch(debug=True, share=True)