import os
from flask import Flask, render_template, request, jsonify
from transformers import pipeline
import google.generativeai as genai
import random
import markdown  # Converts Markdown to HTML

app = Flask(__name__, static_folder='static', template_folder='templates')

# Set a writable Hugging Face cache directory in /tmp
CACHE_DIR = "/tmp/huggingface_cache"
os.environ["HF_HOME"] = CACHE_DIR  # Use HF_HOME instead of TRANSFORMERS_CACHE

os.environ["HF_HOME"] = os.environ.get("TRANSFORMERS_CACHE", "~/.cache/huggingface")


# Ensure the cache directory exists
if not os.path.exists(CACHE_DIR):
    try:
        os.makedirs(CACHE_DIR, exist_ok=True)
    except PermissionError:
        print(f"❌ Permission Denied: Cannot create cache directory at {CACHE_DIR}")
        exit(1)

# Set up Google Gemini Nano (Ensure you have Google AI SDK installed: pip install google-generativeai)
GOOGLE_API_KEY = "AIzaSyCuAIelmhXYbJWJMy-shk0x1el7t4hp0xQ" #"AIzaSyDBiTxdCAY6uWSmEDMDsI0o_8wndXZeFt8"  #problem:"AIzaSyBA4icnk8d_2Gt_dC_ZsCvCDzLVhSaf_-Y"  # API key
genai.configure(api_key=GOOGLE_API_KEY)

# Load the Gemini Nano model
model = genai.GenerativeModel("gemini-1.5-flash")  # Use "gemini-pro" or "gemini-nano" if available ""gemini-pro-vision""

@app.route("/")
def home():
    return render_template("index.html")

@app.route("/ai")
def ai():
    return render_template("ai.html")

@app.route("/about")
def about():
    return render_template("about.html")

@app.route("/contact")
def contact():
    return render_template("contact.html")

@app.route("/services")
def services():
    return render_template("services.html")

@app.route("/library")
def library():
    return render_template("library.html")

#-------

#-----

@app.route("/chat", methods=["POST"])
def chat():
    user_message = request.json.get("message", "")
    if not user_message:
        return jsonify({"response": "Please enter a message!"})

    # Generate chatbot response
    response = model.generate_content(user_message).text


    # Convert Markdown response to HTML
    markdown_response = markdown.markdown(response)

    return jsonify({"response": markdown_response})

# Sample Questions and Predefined Answers
sample_responses = {
    "What is AI?": "AI, or Artificial Intelligence, is the simulation of human intelligence in machines.",
    "How does machine learning work?": "Machine learning is a subset of AI that allows systems to learn from data without explicit programming.",
    "What are neural networks?": "Neural networks are a series of algorithms that recognize patterns, mimicking the human brain.",
    "Explain the Turing Test.": "The Turing Test evaluates a machine's ability to exhibit human-like intelligence in conversation.",
    "How does deep learning differ from AI?": "Deep learning is a subset of machine learning that uses neural networks with multiple layers.",
    "Hello": "Hi there! How can I help you?",
    "How are you?": "I'm just a bot from Edu MentorAI, but I'm here to help!"
}

@app.route("/ask", methods=["POST"])
def ask():
    question = request.json.get("question", "").strip()


    if not question:
        return jsonify({"response": "Please enter a question!"})
    if question in sample_responses:
        return jsonify({"response": sample_responses[question]})

    # Generate AI response
    response = model.generate_content(question).text  

    # Convert Markdown response to HTML
    markdown_response = markdown.markdown(response)

    return jsonify({"response": markdown_response})
if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=True)