app.py

import gradio as gr
from gradio import File
from process.ocr import perform_raw_ocr, correct_text_with_ai
from process.interpretation import get_interpretation
from process.translation import get_translaton
from process.gradio_css import CUSTOM_CSS


MISTRAL_API_KEY = ""
GEMINI_API_KEY = ""


def update_api_keys(mistral_key, gemini_key):
	"""
	Updates the global MISTRAL_API_KEY and GEMINI_API_KEY variables.

	Args:
		mistral_key: The Mistral API key.
		gemini_key: The Gemini API key.

	Returns:
		A string confirming that the API keys have been saved.
	"""
	global MISTRAL_API_KEY, GEMINI_API_KEY

	MISTRAL_API_KEY = mistral_key
	GEMINI_API_KEY = gemini_key

	return "API keys saved"


def ocr_workflow_wrapper(file: File, mistral_key: str):
	"""
	Manages the OCR workflow, processing an uploaded file to extract text.

	Args:
		file: The file object to process (image, PDF, or text).
		mistral_key: The Mistral API key for OCR processing.

	Yields:
		Status messages and the extracted text or error messages.
	"""
	if not mistral_key:
		error_msg = "Error: Mistral API Key not set."
		yield error_msg, error_msg
		return
	if not file or file.name == "":
		error_msg = "Error: File/Text not found."
		yield error_msg, error_msg
		return

	try:
		result = perform_raw_ocr(file, mistral_key)
		yield result, f"\n{result}\n"
	except Exception as e:
		error_msg = f"An error occurred during processing: {str(e)}"
		yield error_msg, error_msg


def ai_correct(current_text: str, mistral_key: str):
	"""
	Corrects the provided text using an AI model.

	Args:
		current_text: The text to be corrected.
		mistral_key: The Mistral API key for AI correction.

	Yields:
		Status messages and the corrected text or error messages.
	"""
	if not mistral_key:
		error_msg = "Error: Mistral API Key not set."
		yield error_msg, error_msg
		return
	if not current_text or current_text.strip() == "":
		error_msg = "*No text to correct. Upload a file, or paste text into 'Raw Text' box first*"
		yield error_msg, error_msg
		return

	try:
		result = correct_text_with_ai(current_text, mistral_key)
		yield result, result
	except Exception as e:
		error_msg = f"Error : {e}"
		yield error_msg, error_msg


def interpretation_workflow(text: str, genre: str, learn_language: str, target_language: str, gemini_key: str):
	"""
	Generates an interpretation of the text based on genre and language settings.

	Args:
		text: The text to interpret.
		genre: The genre of the text (e.g., "general", "news").
		learn_language: The language being learned.
		target_language: The language for the interpretation output.
		gemini_key: The Gemini API key for interpretation.

	Yields:
		Status messages and the generated interpretation or error messages.
	"""
	if not gemini_key:
		yield "Error: Gemini api key not found."
		return
	if not text or text.strip() == "":
		yield "Error: Text is empty"
		return
	if not learn_language or not target_language:
		yield "Error: Language not selected"
		return

	if genre.lower() in ["general", "news"]:
		result = get_interpretation(genre.lower(), gemini_key, text, learn_language, target_language)
		yield result
	else:
		yield "not implemented yet"


def translation_workflow(text: str, target_language: str, gemini_key):
	"""
    Translates the provided text to the target language.

    Args:
        text: The text to translate.
        target_language: The language to translate the text into.
        gemini_key: The Gemini API key for translation.

    Yields:
        Status messages and the translated text or error messages.
    """
	if not gemini_key:
		yield "Error: Gemini api key not found."
		return
	if not text or text.strip() == "":
		yield "Error: Text is empty"
		return
	if not target_language:
		yield "Error: Language not selected"
	
	if target_language in ["Deutsch", "English", "Français", "Русский язык", "中文"]:
		result = get_translaton(text, gemini_key, target_language)
		yield result
	else:
		yield "not implemented yet"


with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
	gr.Markdown("# 📚 LogosAI - Intensive Reading in Any Language", elem_classes=["section-header"])

	# --- API Key ---
	with gr.Accordion("API Configuration", open=True):
		with gr.Row():
			with gr.Column(scale=2):
				mistral_api = gr.Textbox(
					label="Mistral API Key", 
					type="password", 
					placeholder="Enter your key",
					info="OCR recognition & text processing"
				)
			with gr.Column(scale=2):
				gemini_api = gr.Textbox(
					label="Gemini API Key", 
					type="password", 
					placeholder="Enter your key",
					info="text interpretation"
				)
			with gr.Column(scale=1):
				update_keys_button = gr.Button("Save keys")

	api_key_status_output = gr.Markdown()

	update_keys_button.click(
		fn=update_api_keys,
		inputs=[mistral_api, gemini_api],
		outputs=api_key_status_output
	)

	# --- Text Processing ---
	gr.Markdown("---")
	with gr.Tab("Text"):

		with gr.Row():
			with gr.Column(scale=1):
				gr.Markdown("### Upload documents")
				file_input = gr.File(
					label="Upload Image/PDF/text",
					file_types=["image", ".pdf", ".txt"]
					)
				process_button = gr.Button("1. File Process (OCR/Read)", variant="primary")
				ai_correct_button = gr.Button("2. AI Correct", variant="primary")
			with gr.Column(scale=2):
				gr.Markdown("### Processed result")
				with gr.Tabs():
					with gr.Tab("Raw Text"):
						text_display = gr.Textbox(
							label="Raw Text(editable)",
							lines=15,
							max_lines=20,
							show_copy_button=True,
							value="",
							interactive=True
						)
					with gr.Tab("Formatted Text"):
						text_markdown = gr.Markdown(
							value="*Processed text will appear here...*\n\n",
							label="Formatted Text"
						)

	# Hook the ocr button to click event
	process_button.click(
		fn=ocr_workflow_wrapper,
		inputs=[file_input, mistral_api],
		outputs=[text_display, text_markdown]
	)

	# AI correction button to click event
	ai_correct_button.click(
		fn=ai_correct,
		inputs=[text_display, mistral_api],
		outputs=[text_display, text_markdown]
	)

	# --- Text Interpertation ---
	with gr.Tab("🎓 Interpretation"):
		gr.Markdown("### Configure Interpretation Settings")

		with gr.Row():
			with gr.Column(scale=1):
				prof_language_seletor = gr.Dropdown(["DE", "EN", "FR", "RU", "ZH"], label="Prof's Language", value="EN")
				learn_language_seletor = gr.Dropdown(["DE", "EN", "FR", "RU", "ZH"], label="Language to Learn", value="EN")
				style_seletor = gr.Dropdown(["General", "Paper", "News", "Narrative", "Poem", "Philosophy"], label="Genre")
				interpret_button = gr.Button("Generate Interpretation", variant="primary")

			with gr.Column(scale=2):
				gr.Markdown("### COURSE")
				interpretation_output = gr.Markdown(
					value="*Interpretation will appear here after processing...*\n\n",
					label="Interpretation Result",
					show_copy_button=True
					)

	interpret_button.click(
		fn=interpretation_workflow,
		inputs=[text_display, style_seletor, learn_language_seletor, prof_language_seletor, gemini_api],
		outputs=interpretation_output
	)

	with gr.Tab("Translation"):
		gr.Markdown("### Configure Translation Settings")
		with gr.Row():
			with gr.Column(scale=1):
				target_language_selector = gr.Dropdown(
					["Deutsch", "English", "Français", "Русский язык", "中文"],
					value="English",
					label="Target Language",
					interactive=True)
				translation_button = gr.Button("Translate!", variant="primary")

			with gr.Column(scale=2):
				interpretation_output = gr.Markdown(
					value="*Translation will appear here ...*\n\n",
					label="Translation Result",
					show_copy_button=True
					)

	translation_button.click(
		fn=translation_workflow,
		inputs=[text_display, target_language_selector, gemini_api],
		outputs=interpretation_output
	)



if __name__ == "__main__":
	demo.launch(mcp_server=True)