app.py

import os
# 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
import zipfile
import os
import io
import gradio as gr

# 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_path: str, file_mime_type: str) -> tuple:
    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)
    return extracted_text, translated_text

def unzip_and_find_jpgs(file_path):
    extract_path = "extracted_files"
    os.makedirs(extract_path, exist_ok=True)
    jpg_files = []
    with zipfile.ZipFile(file_path, 'r') as zip_ref:
        zip_ref.extractall(extract_path)
        for root, dirs, files in os.walk(extract_path):
            if '__MACOSX' in root:
                continue
            for file in files:
                if file.lower().endswith('.jpg'):
                    full_path = os.path.join(root, file)
                    jpg_files.append(full_path)
    return jpg_files

def get_random_pairs_list(shots, num_pairs=2):
    keys = random.sample(list(shots.keys()), num_pairs)
    return [(key, shots[key]) for key in keys]

def construct_prompt(input_text, random_pairs):
    # Example setup based on your specified format
    prompt = \
    """    
    Follow the examples below. Your response should contain only JSON. If you
    encounter two dates in an input, prefer the earliest. If the answer is not
    exact, try your best, but do not use excess wording. If you are completely
    unsure or there is no answer, insert UNKNOWN.

    Input 1:
    {random_pairs[0][0]}

    Output 1:
    {{"Collector":"{random_pairs[0][1]['Collector']}","Location":"{random_pairs[0][1]['Location']}","Taxon":"{random_pairs[0][1]['Taxon']}","Date":"{random_pairs[0][1]['Date']}"}}

    Input 2:
    {random_pairs[1][0]}

    Output 2:
    {{"Collector":"{random_pairs[1][1]['Collector']}","Location":"{random_pairs[1][1]['Location']}","Taxon":"{random_pairs[1][1]['Taxon']}","Date":"{random_pairs[1][1]['Date']}"}}

    Input 3:
    {input_text}
    Output 3:
    """
    return prompt

def process_responses(responses):
    structured_responses = []
    for response in responses:
        try:
            # Assuming response is a string of JSON data
            parsed_json = json.loads(response.text)
            structured_responses.append(parsed_json)
        except json.JSONDecodeError:
            structured_responses.append({
                "Collector": "UNKNOWN",
                "Location": "UNKNOWN",
                "Taxon": "UNKNOWN",
                "Date": "UNKNOWN"
            })
    return structured_responses

def process_images(uploaded_file):
    global results_df
    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:
            return "No JPG files found in the zip."

        for file_path in image_files:
            extracted_text, translated_text = batch_process_documents(file_path, "image/jpeg")
            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)

        # Configure the generative AI model
        genai.configure(api_key='AIzaSyB9iHlqAgz5TEF36Kg_fJLJvoIDCJkqwJI')
        model = genai.GenerativeModel('gemini-pro')

        # Prepare data for few-shot learning
        shots = \
        {
            "Chinese National Herbarium (PE) Plants of Xizang CHINA, Xizang, Lhoka City, Lhozhag County, Lhakang Town, Kharchhu Gompa vicinity 28°5'37.15"N, 91°7'24.74"E; 3934 m Herbs. Slopes near roadsides. PE-Xizang Expedition #PE6663 NCIL 14 September 2017 N° 2581259 TIBET PE CHINESE NATIONAL HERBARIUM (PE) 02334125 #PE6663 COMPOSITAE Aster albescens (DC.) Hand.-Mazz. A: it (Guo-Jin ZHANG) 01 April 2018"\
            :{"Collector":"Guo-Jin, Zhang",
              "Location":"Xizang, Tibet, China, Lhoka City, Lhozhag County, Lhakang Town, near Kharchhu Gompa",
              "Taxon":"Aster albescens (DC.) Hand.-Mazz., Compositae (Asteraceae) family",
              "Date":"14 September 2017"
            },

            "PE-Xizang Expedition #PE6673 9 NSIT Chinese National Herbarium (PE) Plants of Xizang CHINA, Xizang, Lhoka City, Lhozhag County, Lhakang Town, Kharchhu Gompa vicinity 28°5'37.15"N, 91°7'24.74"E; 3934 m Herbs. Slopes near roadsides. PE-Xizang Expedition #PE6673 9 NSIT Chinese National Herbarium (PE) Plants of Xizang CHINA, Xizang, Lhoka City, Lhozhag County, Lhakang Town, Kharchhu Gompa vicinity 28°5'37.15"N, 91°7'24.74"E; 3934 m Herbs. Slopes near roadsides. PE-Xizang Expedition #PE6673 9 NSIT Chinese National Herbarium (PE) Plants of Xizang Spiral Leaf Green 17 May 2018"
            :{"Collector":"UNKNOWN",
              "Location":"Xizang, Tibet, China, Lhoka City, Lhozhag County, Lhakang Town, near Kharchhu Gompa",
              "Taxon":"Spiral Leaf Green",
              "Date":"17 May 2018"
            },

            "Honey Plants Research Institute of the Chinese Academy of Agricultural Sciences Collection No.: 13687. May 7, 1993 Habitat Roadside Altitude: 1600 * Characters Shrub No. Herbarium of the Institute of Botany, Chinese Academy of Sciences Collector 3687 Scientific Name Height: m (cm) Diameter at breast height m (cm) Flower: White Fruit: Notes Blooming period: from January to July Honey: Scientific Name: Rosa Sericea Lindl. Appendix: Collector: cm 1 2 3 4 25 CHINESE NATIONAL HERBARUM ( 01833954 No 1479566 * Herbarium of the Institute of Botany, Chinese Academy of Sciences Sichuan SZECHUAN DET. Rosa sercea Lindl. var. Various Zhi 2009-02-16"
            :{"Collector":"UNKNOWN",
              "Location":"Sichuan, China",
              "Taxon":"Rosa sericea Lindl., with possible variant identification as 'var. Various Zhi'",
              "Date":"7 May 1993",
            },
        }

        responses = []
        for input_text in results_df["Translated Text"]:
            random_pairs = get_random_pairs_list(shots)
            prompt = construct_prompt(input_text, random_pairs)
            response = model.generate_content(prompt)
            responses.append(response)

        # Processing responses
        json_responses = process_responses(responses)
        results_df = pd.concat([results_df, pd.DataFrame(json_responses)], axis=1)

    except Exception as e:
        return f"An error occurred: {str(e)}"

    return results_df.to_html()

css = """
    body { font-family: Arial, sans-serif; }
    .input-container { width: 95%; margin: auto; }
    .output-container { width: 95%; margin: auto; }
"""

interface = gr.Interface(
    fn=process_images,
    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."
)

if __name__ == "__main__":
    interface.launch(share=True)