import gradio as gr
from utils.pdf_parser import extract_text_from_pdf
from summarizer import Summarizer
from qa_engine import QABot
from chatbot import ask_model
from suggestions import suggest_questions

# Initialize summarizer and global variables
summarizer = Summarizer()
qa_bot = None
summary = ""
text_chunks = []

# Gradio chat history
chat_history = []

def process_pdf(file):
    global summary, qa_bot, text_chunks, chat_history
    text = extract_text_from_pdf(file.name)
    summary = summarizer.summarize(text)
    text_chunks = text.split("\n\n")
    qa_bot = QABot(text_chunks)
    chat_history.clear()
    return summary, "PDF processed. You can now ask questions."

def chat_with_doc(question):
    if not qa_bot:
        return chat_history, "Please upload and summarize a document first."

    context = qa_bot.retrieve_context(question)
    response = ask_model(context, question)

    chat_history.append((question, response))
    suggestions = suggest_questions(summary)
    suggestions_block = "💡 You can also ask:\n" + "\n".join([f"• {q}" for q in suggestions])

    return chat_history, suggestions_block

# UI layout
with gr.Blocks(title="BioSummarize.ai") as iface:
    gr.Markdown("# 🧬 BioSummarize.ai")
    gr.Markdown("Upload a biotech research paper, generate its summary, and chat with it using an AI-powered assistant.")

    with gr.Row():
        file_input = gr.File(label="Upload Biotech Research PDF")
        summarize_btn = gr.Button("Summarize + Start Chat")

    summary_box = gr.Textbox(label="📘 Summary", lines=6)
    summary_status = gr.Textbox(label="Status / Info", lines=2)

    chat_input = gr.Textbox(label="💬 Ask a Question", placeholder="What is the main finding?")
    chatbot = gr.Chatbot(label="🧠 BioResearch Chatbot")

    suggestions_box = gr.Textbox(label="💡 Follow-up Suggestions", interactive=False)

    # Bind actions
    summarize_btn.click(fn=process_pdf, inputs=file_input, outputs=[summary_box, summary_status])
    chat_input.submit(fn=chat_with_doc, inputs=chat_input, outputs=[chatbot, suggestions_box])

# Launch the app
if __name__ == "__main__":
    iface.launch()