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import pandas as pd
import gradio as gr
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
import zipfile
import os
import io
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "herbaria-ai-3c860bcb0f44.json"
# Global DataFrame declaration
results_df = pd.DataFrame(columns=["Filename", "Extracted Text", "Translated Text"])
# 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()
result = translate_client.translate(text, target_language=target_language)
return result["translatedText"]
def batch_process_documents(file_stream, file_mime_type="image/jpeg") -> tuple:
opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")
client = documentai.DocumentProcessorServiceClient(client_options=opts)
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)
return extracted_text, translated_text
def find_images(directory, extensions=('.jpeg', '.jpg')):
for root, _, filenames in os.walk(directory):
for filename in filenames:
if filename.lower().endswith(extensions) and not filename.startswith('.'):
yield os.path.join(root, filename)
def process_zip_file(file_info):
global results_df
results_df = results_df.iloc[0:0] # Clear the DataFrame if re-running this cell
with zipfile.ZipFile(io.BytesIO(file_info["content"]), 'r') as zip_ref:
zip_ref.extractall("extracted_files")
image_files = list(find_images("extracted_files"))
for file_path in image_files:
try:
extracted_text, translated_text = batch_process_documents(open(file_path, "rb"))
new_row = pd.DataFrame([{
"Filename": os.path.basename(file_path),
"Extracted Text": extracted_text,
"Translated Text": translated_text
}])
results_df = pd.concat([results_df, new_row], ignore_index=True)
except Exception as e:
return f"An error occurred while processing {file_path}: {e}"
return results_df
interface = gr.Interface(fn=process_zip_file,
inputs=gr.File(label="Upload ZIP File"),
outputs=gr.Dataframe(label="Processed Results"),
title="Document Processing and Translation")
interface.launch()
# def greet(name):
# return "Hello " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
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