update

app.py

@@ -1,7 +1,8 @@
 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_tranlaton
+from process.translation import get_translaton
 from process.gradio_css import CUSTOM_CSS
 
 
@@ -28,7 +29,7 @@ def update_api_keys(mistral_key, gemini_key):
 	return "API keys saved"
 
 
-def ocr_workflow_wrapper(file, mistral_key):
+def ocr_workflow_wrapper(file: File, mistral_key: str):
 	"""
 	Manages the OCR workflow, processing an uploaded file to extract text.
 
@@ -41,21 +42,19 @@ def ocr_workflow_wrapper(file, mistral_key):
 	"""
 	if not mistral_key:
 		error_msg = "Error: Mistral API Key not set."
-		yield error_msg, error_msg + "\n\n"
+		yield error_msg, error_msg
 		return
-	if not file:
+	if not file or file.name == "":
 		error_msg = "Error: File/Text not found."
-		yield error_msg, error_msg + "\n\n"
+		yield error_msg, error_msg
 		return
 
-	yield "Processing...", "⏳ Processing, please wait...\n\n"
-
 	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 + "\n\n"
+		yield error_msg, error_msg
 
 
 def ai_correct(current_text: str, mistral_key: str):
@@ -71,20 +70,19 @@ def ai_correct(current_text: str, mistral_key: str):
 	"""
 	if not mistral_key:
 		error_msg = "Error: Mistral API Key not set."
-		yield error_msg, error_msg + "\n\n"
+		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
 
-	yield "⏳ AI Correcting text...", "⏳ AI Correcting text...\n\n*Please wait...*"
 	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 + "\n\n"
+		yield error_msg, error_msg
 
 
 def interpretation_workflow(text: str, genre: str, learn_language: str, target_language: str, gemini_key: str):
@@ -107,11 +105,11 @@ def interpretation_workflow(text: str, genre: str, learn_language: str, target_l
 	if not text or text.strip() == "":
 		yield "Error: Text is empty"
 		return
-	if not learn_language or target_language:
+	if not learn_language or not target_language:
 		yield "Error: Language not selected"
+		return
 
-	if genre.lower() in ["general", "news"]:
-		yield f"⏳ Generating interpretation for genre: {[genre]} ... (10s - 2min)"
+	if genre.lower() in ["general", "news", "philosophy"]:
 		result = get_interpretation(genre.lower(), gemini_key, text, learn_language, target_language)
 		yield result
 	else:
@@ -139,9 +137,8 @@ def translation_workflow(text: str, target_language: str, gemini_key):
 	if not target_language:
 		yield "Error: Language not selected"
 	
-	if target_language in ["Deutsch", "English", "Français", "Русский язык", "中文"]:
-		yield f"⏳ Generating interpretation for target_language: {[target_language]} ..."
-		result = get_tranlaton(text, gemini_key, target_language)
+	if target_language in ["العربية", "Deutsch", "Español", "English", "Français", "Italiano", "日本語", "Русский язык", "中文"]:
+		result = get_translaton(text, gemini_key, target_language)
 		yield result
 	else:
 		yield "not implemented yet"
@@ -206,6 +203,7 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
 					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
@@ -228,15 +226,16 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
 
 		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")
+				prof_language_seletor = gr.Dropdown(["AR", "DE", "ES", "EN", "FR", "IT", "JA", "RU", "ZH"], label="Prof's Language", value="EN")
+				learn_language_seletor = gr.Dropdown(["AR", "DE", "ES", "EN", "FR", "IT", "JA", "RU", "ZH"], label="Language to Learn", value="EN")
+				style_seletor = gr.Dropdown(["General", "News", "Philosophy", "Narrative", "Poem", "Paper"], 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
 					)
 
@@ -251,7 +250,7 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
 		with gr.Row():
 			with gr.Column(scale=1):
 				target_language_selector = gr.Dropdown(
-					["Deutsch", "English", "Français", "Русский язык", "中文"],
+					["العربية", "Deutsch", "Español", "English", "Français", "Italiano", "日本語", "Русский язык", "中文"],
 					value="English",
 					label="Target Language",
 					interactive=True)
@@ -260,6 +259,7 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
 			with gr.Column(scale=2):
 				interpretation_output = gr.Markdown(
 					value="*Translation will appear here ...*\n\n",
+					label="Translation Result",
 					show_copy_button=True
 					)
 

process/interpretation.py

@@ -1,11 +1,11 @@
 from google import genai
 from google.genai import types
-from process.sys_prompt import GENERAL_PROMPT, NEWS_PROMPT
+from process.sys_prompt import GENERAL_PROMPT, NEWS_PROMPT, PHILO_PROMPT
 
 
 NARRATIVE_PROMPT = ""
 POEM_PROMPT = ""
-PHILO_PROMPT = ""
+
 
 def get_interpretation(genre: str,
 						api_key: str,
@@ -18,9 +18,13 @@ def get_interpretation(genre: str,
 	if not text:
 		return "Error: text not found."
 
-	client = genai.Client(api_key=api_key)
+	try:
+		client = genai.Client(api_key=api_key)
+	except Exception as e:
+		return f"ERROR: {str(e)}"
+
 
-	lang_map ={"DE": "German", "EN": "English", "FR": "French", "RU":"Russian", "ZH": "Chinese"}
+	lang_map ={"AR": "Arabic", "DE": "German", "ES": "Spanish", "EN": "English", "FR": "French", "IT": "Italian", "JA": "Japanese", "RU":"Russian", "ZH": "Chinese"}
 	learn_lang = lang_map.get(learn_language.upper(), "English")
 	prof_lang = lang_map.get(prof_language.upper(), "English")
 	genres = {
@@ -30,7 +34,7 @@ def get_interpretation(genre: str,
 		"poem": POEM_PROMPT,
 		"philosophy": PHILO_PROMPT
 		}
-	if genre.lower() in ["general", "news"]:
+	if genre.lower() in ["general", "news", "philosophy"]:
 		sys_prompt = genres[genre.lower()].replace("[LEARN_LANGUAGE]", learn_lang).replace("[PROF_LANGUAGE]", prof_lang)
 
 	response = client.models.generate_content(

process/ocr.py

@@ -1,16 +1,21 @@
 from mistralai import Mistral
 from mistralai.models import OCRResponse
-
+from gradio import File
 
 OCR_MODEL = "mistral-ocr-latest"
 CHAT_MODEL = "mistral-large-latest"
 
 
-def ocr_from_file(file_path, api_key, mode="image"):
+def ocr_from_file(file_path, api_key: str, mode="image"):
 
 	if not api_key:
 		raise ValueError("Mistral API Key is required.")
-	client = Mistral(api_key=api_key)
+
+	try:
+		client = Mistral(api_key=api_key)
+	except Exception as e:
+		raise ValueError("API invalid.")
+
 	uploaded_image = client.files.upload(
 		file={
 			"file_name": file_path,
@@ -51,11 +56,15 @@ def get_combined_markdown(ocr_response: OCRResponse) -> str:
 	return "\n\n".join(markdowns)
 
 
-def correct_text_with_ai(text: str, api_key: str):
+def correct_text_with_ai(text: str, api_key: str) -> str:
 
 	if not api_key:
 		raise ValueError("Mistral API Key is required.")
-	client = Mistral(api_key=api_key)
+
+	try:
+		client = Mistral(api_key=api_key)
+	except Exception as e:
+		return f"ERROR: {str(e)}"
 
 	response = client.chat.complete(
 		model=CHAT_MODEL,
@@ -85,8 +94,8 @@ def correct_text_with_ai(text: str, api_key: str):
 	return(response.choices[0].message.content)
 
 
-def perform_raw_ocr(input_file, api_key):
-	if input_file != None:
+def perform_raw_ocr(input_file: File, api_key: str):
+	if input_file and input_file.name:
 		file_ext = input_file.name.split('.')[-1].lower()
 	else:
 		return "File/Text not found"

process/sys_prompt.py

@@ -89,6 +89,50 @@ NEWS_PROMPT = """
 * Clear and logical in language, able to deconstruct complex news content into easily understandable parts.
 """
 
+
+PHILO_PROMPT = """
+## Core Purpose and Goals:
+
+*   To assist students with **intermediate to advanced `[LEARN_LANGUAGE]` proficiency** in mastering sophisticated aspects of the language through the deep analysis of complex, authentic **philosophical and literary** texts.
+*   To explain complex grammatical phenomena and syntactic structures, particularly those characteristic of philosophical argumentation or literary prose in `[LEARN_LANGUAGE]`.
+*   To analyze and clarify idiomatic expressions, domain-specific terminology (especially philosophical and literary terms), and fixed phrases found in the `[LEARN_LANGUAGE]` text.
+*   To deconstruct long, complex sentences in `[LEARN_LANGUAGE]`, analyzing their internal logical relationships, information hierarchy, and argumentative chains.
+*   To deeply explore rhetorical devices (e.g., metaphors, irony, paradoxes) and any cultural, historical, or philosophical allusions within the text.
+*   To reveal the underlying logical connections—such as causality, contrast, or dialectical progression—between sentences and paragraphs.
+*   To analyze the macro-structure, authorial intent, and organizational methods of the text, adapting the analysis to its specific genre (e.g., argumentative structure of an essay, narrative framework of prose).
+*   To deliver instruction primarily in **`[PROF_LANGUAGE]`**, while flexibly using `[LEARN_LANGUAGE]` and English for clarification. When relevant, to introduce etymological insights from source languages (e.g., Latin, Ancient Greek) to aid vocabulary comprehension.
+*   To demonstrate profound understanding of the text's **philosophical or literary** subject matter, interpreting it from a broader intellectual perspective to help the student grasp its full context and deeper meaning.
+
+## Behaviors and Rules:
+
+### 1) Text Selection and Presentation:
+
+*   a) Use the challenging and profound `[LEARN_LANGUAGE]` text chosen by the user as the core material for analysis.
+*   b) When presenting the text, add annotations or highlights to key terminology and complex structures as needed to aid understanding.
+
+### 2) Explanation and Analysis:
+
+*   a) Explain **advanced or complex grammar points** within the text, focusing on syntactic structures common to its specific style and genre. **Omit basic grammar explanations.**
+*   b) Elucidate commonly used `[LEARN_LANGUAGE]` idioms, domain-specific terminology, and fixed phrases, providing contextual examples.
+*   c) Analyze the architecture of complex sentences to help the student map their logical flow and information hierarchy.
+*   d) Discuss rhetorical devices in the text and how the author uses language to construct an argument, shape opinion, or create a literary effect.
+*   e) Explain any cultural allusions, historical backgrounds, or philosophical concepts necessary to understand the text, providing essential context.
+*   f) Analyze the logical connectors and relationships between sentences and paragraphs, showing how the discourse unfolds.
+*   g) Explain the text's overall structure, argumentation methods, or narrative techniques, adapting the analysis to the text's genre (e.g., philosophical essay, literary prose).
+*   h) For each part of the analysis, deliver a **coherent, continuous lecture-style talk**, integrating all knowledge points into a unified and flowing explanation.
+
+### 3) Language Usage:
+
+*   a) Primarily use **`[PROF_LANGUAGE]`** and **`[LEARN_LANGUAGE]`** for instruction, with English readily available as an auxiliary language for clarification.
+*   b) When discussing etymology, introduce it based on its relevance to the `[LEARN_LANGUAGE]` and its practical utility for understanding modern vocabulary, especially philosophical terms.
+*   c) Demonstrate rigorous, clear, and precise language, especially when explaining complex concepts and structural analyses.
+
+## Overall Tone:
+
+*   **Knowledgeable and Rigorous:** Demonstrating deep expertise in `[LEARN_LANGUAGE]` linguistics as well as the philosophical and literary subject matter of the text.
+*   **Patient and Inspiring:** Encouraging the student to engage in critical thinking and deep reading.
+*   **Clear and Logical:** Capable of deconstructing complex material into understandable components.
+"""
+
 NARRATIVE_PROMPT = ""
 POEM_PROMPT = ""
-PHILO_PROMPT = ""

process/translation.py

@@ -9,15 +9,29 @@ Do not add any extra information, explanations, or stylistic changes.
 Maintain the original meaning and tone as closely as possible.
 """
 
-def get_tranlaton(text: str, api_key: str, target_language: str) -> str:
+def get_translaton(text: str, api_key: str, target_language: str) -> str:
 
 	if not api_key:
 		return "Error: Gemini API Key not found."
 	if not text:
 		return "Error: text not found."
 
-	client = genai.Client(api_key=api_key)
+	try:
+		client = genai.Client(api_key=api_key)
+	except Exception as e:
+		return f"ERROR: {str(e)}"
 
+	lang_map = {
+		"العربية": "Arabic",
+		"Deutsch": "German",
+		"Español": "Spanish",
+		"English": "English",
+		"Français": "French",
+		"Italiano": "Italian",
+		"日本語": "Japanese",
+		"Русский язык": "Russian",
+		"中文": "Chinese"
+		}
 	lang_map = {"Deutsch": "German", "English": "English", "Français": "French", "Русский язык": "Russain", "中文": "Chinese"}
 	tar_lang = lang_map.get(target_language, "English")
 	sys_prompt = SYS_PROMPT_TRANSLATION.replace("[TARGET_LANGUAGE]", tar_lang)