app.py

from flask import Flask, render_template, request, jsonify
import ollama
from googletrans import Translator

def translate_to_indian_language(text, target_language):
    translator = Translator()
    try:
        translated = translator.translate(text, dest=target_language)
        return translated.text
    except Exception as e:
        return f"Error occurred: {e}"

# List of Indian language codes
indian_languages = {
    'Hindi': 'hi',
    'Bengali': 'bn',
    'Telugu': 'te',
    'Marathi': 'mr',
    'Tamil': 'ta',
    'Gujarati': 'gu',
    'Kannada': 'kn',
    'Malayalam': 'ml',
    'Odia': 'or',
    'Punjabi': 'pa',
    'Urdu': 'ur',
    'English': 'en'
}

app = Flask(__name__)

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

# Route for the "use now" page where users can input a topic
@app.route('/use_now', methods=['GET', 'POST'])
def use_now():
    if request.method == 'POST':
        # Retrieve user input from the form
        topic = request.form['topic']
        grade = request.form['grade']
        board = request.form['board']
        selected_language = request.form['language']  # Get selected language

        # Create the prompt for the AI model
        prompt_message = (
            f"Tell me more about {topic} and explain it to me as if I am in {grade} grade, studying in {board} board. "
            "Give me a simple explanation of the topic, a cross-question for better understanding, "
            "an example related to the topic, and tips to understand it better."
        )

        def generate_response():
            response_text = ""
            try:
                # Use ollama API to get the streaming response from the AI model
                stream = ollama.chat(
                    model="llama3.1",
                    messages=[
                        {"role": "user", "content": prompt_message}
                    ],
                    stream=True  # Enable streaming
                )

                # Collect each chunk of the response
                for chunk in stream:
                    response_text += chunk['message']['content']
            except ollama.ResponseError as e:
                response_text = f"Error: {str(e)}"
            return response_text

        # Get AI response
        response_text = generate_response()

        # Translate response to the selected language
        if selected_language != 'English':
            translated_text = translate_to_indian_language(response_text, indian_languages[selected_language])
        else:
            translated_text = response_text

        return jsonify({'response': translated_text, 'topic': topic, 'language': selected_language})
    
    return render_template('use_now.html')

if __name__ == '__main__':
    app.run(debug=True)