app.py

import gradio as gr
import pandas as pd
import torch
from transformers import BertTokenizer, BertModel
import numpy as np
from sklearn.preprocessing import StandardScaler
import logging

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class EasyLearningPlatform:
    def __init__(self):
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        logger.info(f"Using device: {self.device}")
        self.initialize_models()
        
    def initialize_models(self):
        """Initialize BERT model for processing"""
        try:
            self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
            self.model = BertModel.from_pretrained('bert-base-uncased').to(self.device)
        except Exception as e:
            logger.error(f"Error initializing models: {str(e)}")
            raise
    
    def process_learning_request(
        self,
        name: str,
        age: int,
        education_level: str,
        interests: str,
        learning_goal: str,
        preferred_learning_style: str,
        available_hours_per_week: int
    ):
        """Process user input and generate learning recommendations"""
        try:
            # Create user profile
            profile = {
                'name': name,
                'age': age,
                'education': education_level,
                'interests': interests,
                'goal': learning_goal,
                'learning_style': preferred_learning_style,
                'hours_available': available_hours_per_week
            }
            
            # Generate recommendations based on profile
            recommendations = self.generate_recommendations(profile)
            
            # Create response
            return {
                "status": "Success",
                "personal_learning_path": recommendations['learning_path'],
                "estimated_completion_time": recommendations['completion_time'],
                "recommended_resources": recommendations['resources'],
                "next_steps": recommendations['next_steps']
            }
            
        except Exception as e:
            logger.error(f"Error processing request: {str(e)}")
            return {
                "status": "Error",
                "message": "There was an error processing your request. Please try again."
            }
    
    def generate_recommendations(self, profile):
        """Generate personalized learning recommendations"""
        # Simplified recommendation logic
        learning_styles = {
            'visual': ['video tutorials', 'infographics', 'diagrams'],
            'auditory': ['podcasts', 'audio books', 'lectures'],
            'reading/writing': ['textbooks', 'articles', 'written guides'],
            'kinesthetic': ['practical exercises', 'hands-on projects', 'interactive tutorials']
        }
        
        # Get recommended resources based on learning style
        preferred_resources = learning_styles.get(
            profile['learning_style'].lower(),
            learning_styles['visual']  # default to visual if style not found
        )
        
        # Calculate estimated completion time (simplified)
        weekly_hours = min(max(profile['hours_available'], 1), 168)  # Limit between 1 and 168 hours
        estimated_weeks = 12  # Default to 12-week program
        
        return {
            'learning_path': [
                f"Week 1-2: Introduction to {profile['goal']}",
                f"Week 3-4: Fundamental Concepts",
                f"Week 5-8: Core Skills Development",
                f"Week 9-12: Advanced Topics and Projects"
            ],
            'completion_time': f"{estimated_weeks} weeks at {weekly_hours} hours per week",
            'resources': preferred_resources,
            'next_steps': [
                "1. Review your personalized learning path",
                "2. Schedule your study time",
                "3. Start with the recommended resources",
                "4. Track your progress weekly"
            ]
        }

    def create_interface(self):
        """Create the Gradio interface"""
        
        # Define the interface
        iface = gr.Interface(
            fn=self.process_learning_request,
            inputs=[
                gr.Textbox(label="Name"),
                gr.Number(label="Age", minimum=1, maximum=120),
                gr.Dropdown(
                    choices=[
                        "High School",
                        "Bachelor's Degree",
                        "Master's Degree",
                        "PhD",
                        "Other"
                    ],
                    label="Education Level"
                ),
                gr.Textbox(
                    label="Interests",
                    placeholder="e.g., programming, data science, web development"
                ),
                gr.Textbox(
                    label="Learning Goal",
                    placeholder="What do you want to learn?"
                ),
                gr.Dropdown(
                    choices=[
                        "Visual",
                        "Auditory",
                        "Reading/Writing",
                        "Kinesthetic"
                    ],
                    label="Preferred Learning Style",
                    info="How do you learn best?"
                ),
                gr.Slider(
                    minimum=1,
                    maximum=40,
                    value=10,
                    label="Available Hours per Week",
                    info="How many hours can you dedicate to learning each week?"
                )
            ],
            outputs=gr.JSON(label="Your Personalized Learning Plan"),
            title="AI Learning Path Generator",
            description="""
            Welcome to your personalized learning journey! 
            
            Fill in your information below to get a customized learning path:
            1. Enter your basic information
            2. Specify your learning goals
            3. Choose your preferred learning style
            4. Set your weekly time commitment
            Contact-: AJoshi 91-8847374914 email [email protected]
            
            Click submit to generate your personalized learning plan!
            """,
            examples=[
                [
                    "John Doe",
                    25,
                    "Bachelor's Degree",
                    "Machine Learning, Python",
                    "Learn Data Science",
                    "Visual",
                    10
                ],
                [
                    "Jane Smith",
                    30,
                    "Master's Degree",
                    "Web Development, JavaScript",
                    "Full Stack Development",
                    "Kinesthetic",
                    15
                ]
            ]
        )
        return iface

def main():
    # Create and launch the platform
    platform = EasyLearningPlatform()
    interface = platform.create_interface()
    interface.launch(share=True)

if __name__ == "__main__":
    """
    # Run these commands in Google Colab first:
    !pip install gradio transformers torch numpy pandas scikit-learn
    """
    main()