Create app.py

app.py

@@ -0,0 +1,51 @@
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import os
+import re
+
+# Load Hugging Face 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/"
+
+# Function to 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")
+    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))
+    if not patient_name:
+        return "❌ Could not determine patient name from filename."
+
+    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."
+
+# 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)