Update app.py

app.py

@@ -1,60 +1,46 @@
-import pandas as pd
 import os
-import json
-import zipfile
-import io
-import gradio as gr
-import logging
-from google.oauth2 import service_account
+# Upload credential json file from default compute service account
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "herbaria-ai-3c860bcb0f44.json"
+
+import pandas as pd
 from google.api_core.client_options import ClientOptions
 from google.cloud import documentai_v1 as documentai
 from google.cloud.documentai_v1.types import RawDocument
 from google.cloud import translate_v2 as translate
-from gradio import Interface 
-
-# Setup logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Load credentials from environment variable
-credentials_raw = os.environ.get("google_authentication")
-if not credentials_raw:
-    raise EnvironmentError("Google Cloud credentials not found in environment.")
-credentials_json = json.loads(credentials_raw)
-credentials = service_account.Credentials.from_service_account_info(credentials_json)
-logging.info("Loaded Google Cloud credentials successfully.")
+import zipfile
+import os
+import io
+import gradio as gr
 
 # Global DataFrame declaration
 results_df = pd.DataFrame(columns=["Filename", "Extracted Text", "Translated Text"])
 
-# Google Cloud Document AI processor details
+# Set your Google Cloud Document AI processor details here
 project_id = "herbaria-ai"
 location = "us"
 processor_id = "4307b078717a399a"
 
 def translate_text(text, target_language="en"):
-    translate_client = translate.Client(credentials=credentials)
+    translate_client = translate.Client()
     result = translate_client.translate(text, target_language=target_language)
     return result["translatedText"]
 
 def batch_process_documents(file_path: str, file_mime_type: str) -> tuple:
-    logging.info(f"Processing document {file_path}.")
-    opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com", credentials=credentials)
+    opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")
     client = documentai.DocumentProcessorServiceClient(client_options=opts)
-    
+
     with open(file_path, "rb") as file_stream:
         raw_document = RawDocument(content=file_stream.read(), mime_type=file_mime_type)
 
     name = client.processor_path(project_id, location, processor_id)
     request = documentai.ProcessRequest(name=name, raw_document=raw_document)
     result = client.process_document(request=request)
-    
+
     extracted_text = result.document.text
     translated_text = translate_text(extracted_text)
-    logging.info(f"Document processed and translated for {file_path}.")
     return extracted_text, translated_text
 
 def unzip_and_find_jpgs(file_path):
-    logging.info(f"Unzipping file {file_path}.")
     extract_path = "extracted_files"
     os.makedirs(extract_path, exist_ok=True)
     jpg_files = []
@@ -67,25 +53,21 @@ def unzip_and_find_jpgs(file_path):
                 if file.lower().endswith('.jpg'):
                     full_path = os.path.join(root, file)
                     jpg_files.append(full_path)
-    logging.info(f"Found {len(jpg_files)} JPG files in {file_path}.")
     return jpg_files
 
 def process_images(uploaded_file):
-    logging.info("Started processing the uploaded file.")  # Check if the function is triggered
     global results_df
-    results_df = results_df.iloc[0:0]  # Clear the DataFrame
-    file_path = uploaded_file.name  # Gradio provides the file path
-    logging.info(f"Received file {file_path} for processing.")
+    results_df = results_df.iloc[0:0]  # Clear the DataFrame if re-running this cell
+
+    file_path = uploaded_file.name  # Gradio provides the file path through the .name attribute
 
     try:
         image_files = unzip_and_find_jpgs(file_path)
-        
+
         if not image_files:
-            logging.warning("No JPG files found in the zip.")
             return "No JPG files found in the zip."
 
         for file_path in image_files:
-            logging.info(f"Processing image file {file_path}.")
             extracted_text, translated_text = batch_process_documents(file_path, "image/jpeg")
             new_row = pd.DataFrame([{
                 "Filename": os.path.basename(file_path),
@@ -93,23 +75,18 @@ def process_images(uploaded_file):
                 "Translated Text": translated_text
             }])
             results_df = pd.concat([results_df, new_row], ignore_index=True)
-            logging.info(f"Data added for file {file_path}.")
     except Exception as e:
-        logging.error(f"An error occurred: {str(e)}")
         return f"An error occurred: {str(e)}"
 
-    logging.info("Processing complete. Generating HTML output.")
     return results_df.to_html()
 
-# Set up the interface
-interface = Interface(
+interface = gr.Interface(
     fn=process_images,
-    inputs=gr.components.File(label="Upload ZIP File", type="file"),
+    inputs="file",
     outputs="html",
     title="Document AI Translation",
-    description="Upload a ZIP file containing JPEG/JPG images, and the system will extract and translate text from each image.",
-    debug=True
+    description="Upload a ZIP file containing JPEG/JPG images, and the system will extract and translate text from each image."
 )
 
 if __name__ == "__main__":
-    interface.launch(debug=True)
+    interface.launch(debug=True)