Update app.py

app.py

@@ -1,51 +1,59 @@
+import gradio as gr
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
 import os
 import re
 
-# Load Hugging Face OCR model
+# Load OCR model
 processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-stage1")
 model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
 
-# Directory where patient records are stored
-PATIENT_RECORDS_DIR = "records/"
+# Folder to store extracted records
+PATIENT_RECORDS_DIR = "records"
+os.makedirs(PATIENT_RECORDS_DIR, exist_ok=True)
 
-# Function to extract patient name from filename
+# Extract patient name from filename
 def extract_patient_name(file_name):
-    match = re.match(r"([A-Za-z]+[A-Za-z]*)_.*\.(jpg|png|jpeg|pdf)$", file_name)
-    if match:
-        return match.group(1)
-    return None
-
-# OCR function
-def extract_text_from_image(image_path):
-    image = Image.open(image_path).convert("RGB")
+    # Example: JuanDelaCruz_2025-06-13.png → JuanDelaCruz
+    match = re.match(r"([A-Za-z]+[A-Za-z]*)_.*\.(jpg|jpeg|png)$", file_name)
+    return match.group(1) if match else None
+
+# OCR logic
+def perform_ocr(image_file):
+    image = Image.open(image_file).convert("RGB")
     pixel_values = processor(images=image, return_tensors="pt").pixel_values
     generated_ids = model.generate(pixel_values)
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    return generated_text.strip()
-
-# Save text to patient record
-def save_to_patient_record(patient_name, text):
-    os.makedirs(PATIENT_RECORDS_DIR, exist_ok=True)
-    filepath = os.path.join(PATIENT_RECORDS_DIR, f"{patient_name}_records.txt")
-    with open(filepath, "a") as file:
-        file.write("\n\n===== New Upload =====\n")
-        file.write(text)
-
-# Main process
-def process_uploaded_lab_result(file_path):
-    print(f"Processing: {file_path}")
-    patient_name = extract_patient_name(os.path.basename(file_path))
+    text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+    return text
+
+# Save to patient record file
+def save_record(patient_name, ocr_text):
+    file_path = os.path.join(PATIENT_RECORDS_DIR, f"{patient_name}_records.txt")
+    with open(file_path, "a") as f:
+        f.write("\n\n===== New Lab Result =====\n")
+        f.write(ocr_text)
+
+# Gradio function
+def process_lab_result(image):
+    file_name = os.path.basename(image.name)
+    patient_name = extract_patient_name(file_name)
+
     if not patient_name:
-        return "❌ Could not determine patient name from filename."
+        return "❌ Cannot extract patient name from filename. Please name the file like JuanDelaCruz_2025-06-13.png"
+
+    ocr_text = perform_ocr(image)
+    save_record(patient_name, ocr_text)
+
+    return f"✅ OCR completed. Lab result saved to `{patient_name}_records.txt`.\n\n---\n📄 Extracted Text:\n{ocr_text}"
 
-    ocr_text = extract_text_from_image(file_path)
-    save_to_patient_record(patient_name, ocr_text)
-    return f"✅ OCR completed and saved under {patient_name}'s record."
+# Gradio interface
+iface = gr.Interface(
+    fn=process_lab_result,
+    inputs=gr.File(label="Upload Lab Result Image (.png, .jpg)", type="file"),
+    outputs="text",
+    title="🩺 Lab Result OCR with Patient Linking",
+    description="Upload a lab result image named like `JuanDelaCruz_2025-06-13.png`. The system will extract the text and save it to the patient's record."
+)
 
-# Example usage
 if __name__ == "__main__":
-    file_to_upload = "JuanDelaCruz_2025-06-13.jpg"  # Example uploaded file
-    result = process_uploaded_lab_result(file_to_upload)
-    print(result)
+    iface.launch()