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import os
import time
import json
import base64
from datetime import datetime
import gradio as gr
from google import genai
from google.genai import types

# ── Gemini Client Setup ──
api_key = os.environ.get("GEMINI_API_KEY", "")
client = genai.Client(api_key=api_key) if api_key else None

# ── Mock Data & Configuration ──
SUBJECTS = [
    {"id": "math", "label": "Mathematics", "emoji": "📐", "description": "Algebra, Calculus, Geometry & more"},
    {"id": "science", "label": "Science", "emoji": "🔬", "description": "Physics, Chemistry, Biology & more"},
    {"id": "history", "label": "History", "emoji": "🏛️", "description": "World events, civilizations & culture"},
    {"id": "literature", "label": "Literature", "emoji": "📖", "description": "Essays, poetry, novels & analysis"},
    {"id": "geography", "label": "Geography", "emoji": "🌍", "description": "Maps, climate, human geography"},
    {"id": "cs", "label": "Computer Science", "emoji": "💻", "description": "Algorithms, data structures & coding"},
]

SUGGESTIONS = {
    "math": ["Explain the quadratic formula", "What is a derivative?", "How do matrices work?"],
    "science": ["Explain Newton's third law", "What is DNA replication?", "How does nuclear fusion work?"],
    "history": ["What caused WWI?", "Explain the French Revolution", "Who was Genghis Khan?"],
    "literature": ["Analyse Hamlet's soliloquy", "What is a metaphor?", "Explain stream of consciousness"],
    "geography": ["What causes earthquakes?", "Explain plate tectonics", "What is the water cycle?"],
    "cs": ["Explain Big O notation", "What is recursion?", "How does a hash table work?"],
}

def get_greeting():
    h = datetime.now().hour
    if h < 12: return "Good morning 👋"
    elif h < 17: return "Good afternoon 👋"
    else: return "Good evening 👋"

# ── Gemini Stream & Logic Functions ──
def predict(message, history, subject_id, file_input):
    if not client:
        yield "⚠️ Please configure GEMINI_API_KEY in your Hugging Face Space secrets."
        return

    subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
    system_instruction = f"You are an expert AI Tutor specializing in {subj}. Help the student understand concepts clearly."
    
    formatted_contents = []
    if history:
        for turn in history:
            if isinstance(turn, dict):
                formatted_contents.append(types.Content(role=turn.get("role", "user"), parts=[types.Part.from_text(str(turn.get("content", "")))]))
            elif isinstance(turn, (list, tuple)) and len(turn) >= 2:
                if turn[0]: formatted_contents.append(types.Content(role="user", parts=[types.Part.from_text(str(turn[0]))]))
                if turn[1]: formatted_contents.append(types.Content(role="model", parts=[types.Part.from_text(str(turn[1]))]))

    current_parts = [types.Part.from_text(message)]
    
    if file_input is not None:
        try:
            filepath = file_input if isinstance(file_input, str) else getattr(file_input, "name", str(file_input))
            with open(filepath, "rb") as f:
                pdf_bytes = f.read()
            current_parts.append(
                types.Part.from_bytes(
                    data=pdf_bytes,
                    mime_type="application/pdf"
                )
            )
        except Exception as e:
            yield f"Error reading PDF file: {str(e)}"
            return

    formatted_contents.append(types.Content(role="user", parts=current_parts))

    try:
        response_stream = client.models.generate_content_stream(
            model='gemini-2.5-flash',
            contents=formatted_contents,
            config=types.GenerateContentConfig(
                system_instruction=system_instruction,
                temperature=0.7,
            )
        )
        
        partial_message = ""
        for chunk in response_stream:
            if chunk.text:
                partial_message += chunk.text
                yield partial_message
    except Exception as e:
        yield f"An error occurred: {str(e)}"

# ── Dynamic Features Generators ──
def generate_quiz(subject_id):
    if not client: return "API key missing."
    subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
    prompt = f"Generate a 3-question multiple choice quiz about {subj} with answers at the bottom. Format beautifully with markdown."
    try:
        response = client.models.generate_content(model='gemini-2.5-flash', contents=prompt)
        return response.text
    except Exception as e:
        return f"Error generating quiz: {str(e)}"

def generate_notes(history, subject_id):
    if not client: return "API key missing."
    subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
    prompt = f"Based on this study history for {subj}: {str(history)}. Create highly structured, clean summary revision notes with key bullet points."
    try:
        response = client.models.generate_content(model='gemini-2.5-flash', contents=prompt)
        return response.text
    except Exception as e:
        return f"Error generating notes: {str(e)}"

def generate_flashcards(history, subject_id):
    if not client: return "API key missing."
    subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
    prompt = f"Based on this study history for {subj}: {str(history)}. Generate 4 interactive flashcards formatted as 'Question: [Text] | Answer: [Text]'."
    try:
        response = client.models.generate_content(model='gemini-2.5-flash', contents=prompt)
        return response.text
    except Exception as e:
        return f"Error generating flashcards: {str(e)}"

# ── Gradio UI Construction ──
with gr.Blocks() as demo:
    current_subject_id = gr.State(value="math")
    
    with gr.Row():
        # SIDEBAR
        with gr.Column(scale=1, min_width=280):
            gr.Markdown("## ⚡ StudyAI Dashboard")
            greeting_md = gr.Markdown(f"*{get_greeting()}*")
            
            subject_dropdown = gr.Dropdown(
                choices=[(s["label"] + " " + s["emoji"], s["id"]) for s in SUBJECTS],
                value="math",
                label="📚 Select Active Subject",
                interactive=True
            )
            
            gr.Markdown("---")
            gr.Markdown("⏱️ **Focus Pomodoro Timer**")
            gr.HTML("<div style='font-size:24px; font-weight:bold; text-align:center;'>25:00</div>")
            with gr.Row():
                gr.Button("▶️ Start", size="sm")
                gr.Button("⏹️ Reset", size="sm")
            
            gr.Markdown("---")
            gr.Markdown("📝 **Todo Checklist**")
            todo_list = gr.CheckboxGroup(
                choices=["Read textbook chapters", "Complete AI Quiz assignment", "Review Generated Notes"],
                label="Tasks for Session"
            )
            new_todo = gr.Textbox(placeholder="+ Add custom task & press Enter", show_label=False)
            
            def add_todo(task, current_todos):
                if task and task.strip(): 
                    current_todos.append(task.strip())
                return gr.update(choices=current_todos, value=current_todos), ""
            new_todo.submit(add_todo, [new_todo, todo_list], [todo_list, new_todo])

        # MAIN WORKSPACE
        with gr.Column(scale=4):
            with gr.Tabs() as main_tabs:
                
                # TAB 1: Chat
                with gr.Tab("💬 AI Chat Workspace", id="chat_tab"):
                    subject_info_banner = gr.Markdown("### 📐 Mathematics \n*Algebra, Calculus, Geometry & more*")
                    
                    suggestion_box = gr.Radio(
                        choices=SUGGESTIONS["math"], 
                        label="💡 Instant Suggestions (Click to choose, then type or send)",
                        interactive=True
                    )
                    
                    file_upload = gr.File(label="📂 Upload Study Material (PDF up to 50MB)", file_types=[".pdf"])
                    chatbot = gr.Chatbot(height=400)
                    
                    with gr.Row():
                        msg_input = gr.Textbox(placeholder="Ask anything...", scale=4, show_label=False)
                        audio_input = gr.Audio(sources=["microphone"], type="filepath", label="🎙️ Voice", scale=1)
                        submit_btn = gr.Button("Send 🚀", variant="primary", scale=1)

                    def update_subject_view(sub_id):
                        subj_obj = next((s for s in SUBJECTS if s["id"] == sub_id), SUBJECTS[0])
                        banner_text = f"### {subj_obj['emoji']} {subj_obj['label']} \n*{subj_obj['description']}*"
                        new_radio_choices = SUGGESTIONS.get(sub_id, [])
                        return banner_text, gr.update(choices=new_radio_choices, value=None), sub_id, []

                    subject_dropdown.change(
                        update_subject_view, 
                        inputs=[subject_dropdown], 
                        outputs=[subject_info_banner, suggestion_box, current_subject_id, chatbot]
                    )
                    
                    suggestion_box.select(lambda x: x.value, inputs=[suggestion_box], outputs=[msg_input])

                    def handle_audio(audio_path):
                        if audio_path: 
                            return "[Voice Note Uploaded: Analyzing audio content]"
                        return ""
                    audio_input.change(handle_audio, inputs=[audio_input], outputs=[msg_input])

                    def respond(message, chat_history, sub_id, pdf_file):
                        if not message or not message.strip(): 
                            return "", chat_history
                        
                        chat_history = chat_history or []
                        chat_history.append({"role": "user", "metadata": {"title": ""}, "content": message})
                        chat_history.append({"role": "assistant", "metadata": {"title": ""}, "content": ""})
                        
                        raw_history = []
                        for i in range(0, len(chat_history)-2, 2):
                            if i+1 < len(chat_history):
                                raw_history.append((chat_history[i]["content"], chat_history[i+1]["content"]))

                        for chunk in predict(message, raw_history, sub_id, pdf_file):
                            chat_history[-1]["content"] = chunk
                            yield "", chat_history

                    submit_btn.click(respond, [msg_input, chatbot, current_subject_id, file_upload], [msg_input, chatbot])
                    msg_input.submit(respond, [msg_input, chatbot, current_subject_id, file_upload], [msg_input, chatbot])

                # TAB 2: Quiz
                with gr.Tab("🎯 AI Quiz Mode", id="quiz_tab"):
                    quiz_generate_btn = gr.Button("✨ Generate New Custom Quiz via Gemini", variant="secondary")
                    quiz_output = gr.Markdown("*Click the button above to dynamically render an AI quiz.*")
                    quiz_generate_btn.click(generate_quiz, inputs=[current_subject_id], outputs=[quiz_output])

                # TAB 3: Notes
                with gr.Tab("📝 Summary Study Notes", id="notes_tab"):
                    notes_generate_btn = gr.Button("📖 Compile Revision Notes from Chat History", variant="secondary")
                    notes_output = gr.Markdown("*Click above to transform chat threads into notes.*")
                    notes_generate_btn.click(generate_notes, inputs=[chatbot, current_subject_id], outputs=[notes_output])

                # TAB 4: Flashcards
                with gr.Tab("🃏 Active Flashcards", id="flashcards_tab"):
                    flash_generate_btn = gr.Button("🔄 Formulate Flashcards from Chat History", variant="secondary")
                    flash_output = gr.Markdown("*Click above to extract flashcards.*")
                    flash_generate_btn.click(generate_flashcards, inputs=[chatbot, current_subject_id], outputs=[flash_output])

    gr.Markdown("⚡ *Powered by Gemini 2.5 Flash API.*")

if __name__ == "__main__":
    demo.queue()
    demo.launch(theme=gr.themes.Soft(primary_hue="indigo", secondary_hue="slate"))

	import os
	import time
	import json
	import base64
	from datetime import datetime
	import gradio as gr
	from google import genai
	from google.genai import types

	# ── Gemini Client Setup ──
	api_key = os.environ.get("GEMINI_API_KEY", "")
	client = genai.Client(api_key=api_key) if api_key else None

	# ── Mock Data & Configuration ──
	SUBJECTS = [
	{"id": "math", "label": "Mathematics", "emoji": "📐", "description": "Algebra, Calculus, Geometry & more"},
	{"id": "science", "label": "Science", "emoji": "🔬", "description": "Physics, Chemistry, Biology & more"},
	{"id": "history", "label": "History", "emoji": "🏛️", "description": "World events, civilizations & culture"},
	{"id": "literature", "label": "Literature", "emoji": "📖", "description": "Essays, poetry, novels & analysis"},
	{"id": "geography", "label": "Geography", "emoji": "🌍", "description": "Maps, climate, human geography"},
	{"id": "cs", "label": "Computer Science", "emoji": "💻", "description": "Algorithms, data structures & coding"},
	]

	SUGGESTIONS = {
	"math": ["Explain the quadratic formula", "What is a derivative?", "How do matrices work?"],
	"science": ["Explain Newton's third law", "What is DNA replication?", "How does nuclear fusion work?"],
	"history": ["What caused WWI?", "Explain the French Revolution", "Who was Genghis Khan?"],
	"literature": ["Analyse Hamlet's soliloquy", "What is a metaphor?", "Explain stream of consciousness"],
	"geography": ["What causes earthquakes?", "Explain plate tectonics", "What is the water cycle?"],
	"cs": ["Explain Big O notation", "What is recursion?", "How does a hash table work?"],
	}

	def get_greeting():
	h = datetime.now().hour
	if h < 12: return "Good morning 👋"
	elif h < 17: return "Good afternoon 👋"
	else: return "Good evening 👋"

	# ── Gemini Stream & Logic Functions ──
	def predict(message, history, subject_id, file_input):
	if not client:
	yield "⚠️ Please configure GEMINI_API_KEY in your Hugging Face Space secrets."
	return

	subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
	system_instruction = f"You are an expert AI Tutor specializing in {subj}. Help the student understand concepts clearly."

	formatted_contents = []
	if history:
	for turn in history:
	if isinstance(turn, dict):
	formatted_contents.append(types.Content(role=turn.get("role", "user"), parts=[types.Part.from_text(str(turn.get("content", "")))]))
	elif isinstance(turn, (list, tuple)) and len(turn) >= 2:
	if turn[0]: formatted_contents.append(types.Content(role="user", parts=[types.Part.from_text(str(turn[0]))]))
	if turn[1]: formatted_contents.append(types.Content(role="model", parts=[types.Part.from_text(str(turn[1]))]))

	current_parts = [types.Part.from_text(message)]

	if file_input is not None:
	try:
	filepath = file_input if isinstance(file_input, str) else getattr(file_input, "name", str(file_input))
	with open(filepath, "rb") as f:
	pdf_bytes = f.read()
	current_parts.append(
	types.Part.from_bytes(
	data=pdf_bytes,
	mime_type="application/pdf"
	)
	)
	except Exception as e:
	yield f"Error reading PDF file: {str(e)}"
	return

	formatted_contents.append(types.Content(role="user", parts=current_parts))

	try:
	response_stream = client.models.generate_content_stream(
	model='gemini-2.5-flash',
	contents=formatted_contents,
	config=types.GenerateContentConfig(
	system_instruction=system_instruction,
	temperature=0.7,
	)
	)

	partial_message = ""
	for chunk in response_stream:
	if chunk.text:
	partial_message += chunk.text
	yield partial_message
	except Exception as e:
	yield f"An error occurred: {str(e)}"

	# ── Dynamic Features Generators ──
	def generate_quiz(subject_id):
	if not client: return "API key missing."
	subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
	prompt = f"Generate a 3-question multiple choice quiz about {subj} with answers at the bottom. Format beautifully with markdown."
	try:
	response = client.models.generate_content(model='gemini-2.5-flash', contents=prompt)
	return response.text
	except Exception as e:
	return f"Error generating quiz: {str(e)}"

	def generate_notes(history, subject_id):
	if not client: return "API key missing."
	subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
	prompt = f"Based on this study history for {subj}: {str(history)}. Create highly structured, clean summary revision notes with key bullet points."
	try:
	response = client.models.generate_content(model='gemini-2.5-flash', contents=prompt)
	return response.text
	except Exception as e:
	return f"Error generating notes: {str(e)}"

	def generate_flashcards(history, subject_id):
	if not client: return "API key missing."
	subj = next((s["label"] for s in SUBJECTS if s["id"] == subject_id), "General Studies")
	prompt = f"Based on this study history for {subj}: {str(history)}. Generate 4 interactive flashcards formatted as 'Question: [Text] \| Answer: [Text]'."
	try:
	response = client.models.generate_content(model='gemini-2.5-flash', contents=prompt)
	return response.text
	except Exception as e:
	return f"Error generating flashcards: {str(e)}"

	# ── Gradio UI Construction ──
	with gr.Blocks() as demo:
	current_subject_id = gr.State(value="math")

	with gr.Row():
	# SIDEBAR
	with gr.Column(scale=1, min_width=280):
	gr.Markdown("## ⚡ StudyAI Dashboard")
	greeting_md = gr.Markdown(f"{get_greeting()}")

	subject_dropdown = gr.Dropdown(
	choices=[(s["label"] + " " + s["emoji"], s["id"]) for s in SUBJECTS],
	value="math",
	label="📚 Select Active Subject",
	interactive=True
	)

	gr.Markdown("---")
	gr.Markdown("⏱️ Focus Pomodoro Timer")
	gr.HTML("<div style='font-size:24px; font-weight:bold; text-align:center;'>25:00</div>")
	with gr.Row():
	gr.Button("▶️ Start", size="sm")
	gr.Button("⏹️ Reset", size="sm")

	gr.Markdown("---")
	gr.Markdown("📝 Todo Checklist")
	todo_list = gr.CheckboxGroup(
	choices=["Read textbook chapters", "Complete AI Quiz assignment", "Review Generated Notes"],
	label="Tasks for Session"
	)
	new_todo = gr.Textbox(placeholder="+ Add custom task & press Enter", show_label=False)

	def add_todo(task, current_todos):
	if task and task.strip():
	current_todos.append(task.strip())
	return gr.update(choices=current_todos, value=current_todos), ""
	new_todo.submit(add_todo, [new_todo, todo_list], [todo_list, new_todo])

	# MAIN WORKSPACE
	with gr.Column(scale=4):
	with gr.Tabs() as main_tabs:

	# TAB 1: Chat
	with gr.Tab("💬 AI Chat Workspace", id="chat_tab"):
	subject_info_banner = gr.Markdown("### 📐 Mathematics \nAlgebra, Calculus, Geometry & more")

	suggestion_box = gr.Radio(
	choices=SUGGESTIONS["math"],
	label="💡 Instant Suggestions (Click to choose, then type or send)",
	interactive=True
	)

	file_upload = gr.File(label="📂 Upload Study Material (PDF up to 50MB)", file_types=[".pdf"])
	chatbot = gr.Chatbot(height=400)

	with gr.Row():
	msg_input = gr.Textbox(placeholder="Ask anything...", scale=4, show_label=False)
	audio_input = gr.Audio(sources=["microphone"], type="filepath", label="🎙️ Voice", scale=1)
	submit_btn = gr.Button("Send 🚀", variant="primary", scale=1)

	def update_subject_view(sub_id):
	subj_obj = next((s for s in SUBJECTS if s["id"] == sub_id), SUBJECTS[0])
	banner_text = f"### {subj_obj['emoji']} {subj_obj['label']} \n{subj_obj['description']}"
	new_radio_choices = SUGGESTIONS.get(sub_id, [])
	return banner_text, gr.update(choices=new_radio_choices, value=None), sub_id, []

	subject_dropdown.change(
	update_subject_view,
	inputs=[subject_dropdown],
	outputs=[subject_info_banner, suggestion_box, current_subject_id, chatbot]
	)

	suggestion_box.select(lambda x: x.value, inputs=[suggestion_box], outputs=[msg_input])

	def handle_audio(audio_path):
	if audio_path:
	return "[Voice Note Uploaded: Analyzing audio content]"
	return ""
	audio_input.change(handle_audio, inputs=[audio_input], outputs=[msg_input])

	def respond(message, chat_history, sub_id, pdf_file):
	if not message or not message.strip():
	return "", chat_history

	chat_history = chat_history or []
	chat_history.append({"role": "user", "metadata": {"title": ""}, "content": message})
	chat_history.append({"role": "assistant", "metadata": {"title": ""}, "content": ""})

	raw_history = []
	for i in range(0, len(chat_history)-2, 2):
	if i+1 < len(chat_history):
	raw_history.append((chat_history[i]["content"], chat_history[i+1]["content"]))

	for chunk in predict(message, raw_history, sub_id, pdf_file):
	chat_history[-1]["content"] = chunk
	yield "", chat_history

	submit_btn.click(respond, [msg_input, chatbot, current_subject_id, file_upload], [msg_input, chatbot])
	msg_input.submit(respond, [msg_input, chatbot, current_subject_id, file_upload], [msg_input, chatbot])

	# TAB 2: Quiz
	with gr.Tab("🎯 AI Quiz Mode", id="quiz_tab"):
	quiz_generate_btn = gr.Button("✨ Generate New Custom Quiz via Gemini", variant="secondary")
	quiz_output = gr.Markdown("Click the button above to dynamically render an AI quiz.")
	quiz_generate_btn.click(generate_quiz, inputs=[current_subject_id], outputs=[quiz_output])

	# TAB 3: Notes
	with gr.Tab("📝 Summary Study Notes", id="notes_tab"):
	notes_generate_btn = gr.Button("📖 Compile Revision Notes from Chat History", variant="secondary")
	notes_output = gr.Markdown("Click above to transform chat threads into notes.")
	notes_generate_btn.click(generate_notes, inputs=[chatbot, current_subject_id], outputs=[notes_output])

	# TAB 4: Flashcards
	with gr.Tab("🃏 Active Flashcards", id="flashcards_tab"):
	flash_generate_btn = gr.Button("🔄 Formulate Flashcards from Chat History", variant="secondary")
	flash_output = gr.Markdown("Click above to extract flashcards.")
	flash_generate_btn.click(generate_flashcards, inputs=[chatbot, current_subject_id], outputs=[flash_output])

	gr.Markdown("⚡ Powered by Gemini 2.5 Flash API.")

	if __name__ == "__main__":
	demo.queue()
	demo.launch(theme=gr.themes.Soft(primary_hue="indigo", secondary_hue="slate"))