# Rewriting the app into a Gradio-compatible format
import zipfile
import os

# Directory for Gradio version
gradio_dir = "/mnt/data/exam-ai-gradio"
os.makedirs(gradio_dir, exist_ok=True)

# Gradio-based app.py content
gradio_app_code = '''import gradio as gr
from transformers import pipeline
from googlesearch import search
import requests
from bs4 import BeautifulSoup
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Initialize models
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

def summarize_text(text, max_len=512):
    if not text.strip():
        return "No content to summarize."
    try:
        summary = summarizer(text, max_length=max_len, min_length=100, do_sample=False)
        return summary[0]["summary_text"]
    except Exception as e:
        return f"Summarization error: {e}"

def search_links(query, max_results=5):
    try:
        return list(search(query, num_results=max_results))
    except Exception as e:
        return []

def fetch_page_content(url):
    try:
        res = requests.get(url, timeout=10)
        soup = BeautifulSoup(res.text, "html.parser")
        return soup.get_text(separator=" ", strip=True)
    except:
        return ""

def embed_chunks(chunks):
    return embed_model.encode(chunks)

def create_faiss_index(chunks):
    embeddings = embed_chunks(chunks)
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(np.array(embeddings))
    return index, embeddings

def generate_notes(query):
    query_phrases = [
        f"{query} exam syllabus 2025",
        f"{query} exam dates",
        f"{query} preparation resources",
        f"{query} important topics"
    ]

    chunks = []
    docs = []

    for phrase in query_phrases:
        urls = search_links(phrase, max_results=3)
        for url in urls:
            content = fetch_page_content(url)
            if len(content.strip()) > 200:
                docs.append(content)

    for doc in docs:
        chunks.extend([doc[i:i+300] for i in range(0, len(doc), 300)])

    if not chunks:
        return "⚠️ No content could be retrieved. Please try again with a different query."

    index, _ = create_faiss_index(chunks)
    prompt = f"important topics and notes for {query} exam"
    query_vec = embed_chunks([prompt])[0].reshape(1, -1)
    D, I = index.search(query_vec, k=15)
    selected = [chunks[i] for i in I[0]]
    unique_chunks = list(set([c.strip() for c in selected if len(c.strip()) > 200]))
    combined = "\\n\\n".join(unique_chunks[:10])
    notes = summarize_text(combined)

    return notes

iface = gr.Interface(
    fn=generate_notes,
    inputs=gr.Textbox(lines=1, placeholder="Enter exam name (e.g., AAI ATC)", label="Exam Name"),
    outputs=gr.Textbox(lines=15, label="AI-Generated Important Topic Notes"),
    title="📘 AI Exam Assistant",
    description="Enter your exam name and get summarized notes with syllabus, dates, topics and resources."
)

if __name__ == "__main__":
    iface.launch()
'''

# Gradio-specific requirements
gradio_requirements = '''gradio
transformers
torch
sentence-transformers
faiss-cpu
googlesearch-python
beautifulsoup4
requests
'''

# Save files
with open(os.path.join(gradio_dir, "app.py"), "w") as f:
    f.write(gradio_app_code)

with open(os.path.join(gradio_dir, "requirements.txt"), "w") as f:
    f.write(gradio_requirements)

# Create zip
zip_path = "/mnt/data/exam-ai-gradio.zip"
with zipfile.ZipFile(zip_path, "w") as zipf:
    for root, _, files in os.walk(gradio_dir):
        for file in files:
            full_path = os.path.join(root, file)
            arcname = os.path.relpath(full_path, gradio_dir)
            zipf.write(full_path, arcname=arcname)

zip_path