Update app.py

app.py

@@ -1,57 +1,77 @@
 import gradio as gr
 import pandas as pd
-# from file_processing import FileProcessorFactory
-from file_processing import read_file
+import easyocr
+from file_processing import FileProcessor
 from entity_recognition import process_text
 from utils import safe_dataframe
 
-def show_to_UI(file):
+reader = easyocr.Reader(['en'], gpu=True)  # Initialize OCR model
+
+
+def extract_it(file):
     """Processes the uploaded file and extracts medical data."""
-    # processor = FileProcessorFactory.get_processor(file.name)  # Get the correct processor
 
-    # if processor is None:
-    #     raise ValueError(f"Unsupported file format: {file.name}") 
+    text = read_file(file.name, reader)  # Read the file (implement `read_file`)
+    print("Performing NER...")
+    global output
+    output = process_text(text)  # Perform entity recognition (implement `process_text`)
+
 
-    # text = processor.extract_text(file.name)  # Extract content
-    text = read_file(file.name)  # Extract content
-    output = process_text(text)  # Perform entity recognition
-    
     metadata = output["metadata"]
 
-    # Convert extracted data safely
-    highs = safe_dataframe(output["reds"], "high")
-    lows = safe_dataframe(output["reds"], "low")
-    labtests = safe_dataframe(output, "lab_tests")
 
     metadata_str = f"**Patient Name:** {metadata['patient_name']}\n\n" \
-                   f"**Age:** {metadata['age']}\n\n" \
+                   f"**Age:** {metadata['age']} \n\n" \
                    f"**Gender:** {metadata['gender']}\n\n" \
                    f"**Lab Name:** {metadata['lab_name']}\n\n" \
                    f"**Report Date:** {metadata['report_date']}"
 
     print(f"Processed report for {metadata['patient_name']}")
 
-    return metadata_str, highs, lows, labtests, output
+    return metadata_str
+
 
-# ✅ Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🏥 Medical Lab Report Processor")
+    gr.Markdown("# 🏥 Medical Lab Test Report Extracter")
 
     with gr.Row():
-        pdf_input = gr.File(label="📂 Upload Report")
-        submit_btn = gr.Button("Process")
+        file_input = gr.File(label="📂 Upload Report")
+        # submit_btn = gr.Button("Extract")
 
-    metadata_output = gr.Markdown("**Patient Name: Prashasst Dongre...**")
-    
-    with gr.Row():
-        high_output = gr.Dataframe(label="🔺 High Values")
-        low_output = gr.Dataframe(label="🔻 Low Values")
     
-    lab_test_output = gr.Dataframe(label="📊 Lab Test Results")
-    output_JSON = gr.JSON(label="📜 Extracted Report")
+    # metadata_md = gr.Markdown("Report will show below....")
+    # submit_btn.click(fn=extract_it,inputs=file_input,outputs=metadata_md)
+
 
-    submit_btn.click(show_to_UI, inputs=[pdf_input], outputs=[metadata_output, high_output, low_output, lab_test_output, output_JSON])
+    @gr.render(inputs=file_input,triggers=[file_input.upload])
+    def extract_it(file):
+        """Processes the uploaded file and extracts medical data."""
 
-demo.launch()
+        text = read_file(file.name, reader)  # Read the file (implement `read_file`)
+        print("Performing NER...")
+        output = process_text(text)  # Perform entity recognition (implement `process_text`)
 
 
+        metadata = output["metadata"]
+
+
+        metadata_str = f"**Patient Name:** {metadata['patient_name']}\n\n" \
+                      f"**Age:** {metadata['age']} \n\n" \
+                      f"**Gender:** {metadata['gender']}\n\n" \
+                      f"**Lab Name:** {metadata['lab_name']}\n\n" \
+                      f"**Report Date:** {metadata['report_date']}"
+
+        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")
+
+            gr.Markdown(f"### 📊 Test : {test_type}")
+            gr.Dataframe(lab_tests)
+
+        gr.JSON(output,label="📜 Extracted Report")
+    
+
+demo.launch(debug=True, share=True)