Create app.py

app.py

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+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 -joshianupam32@gmail.com
+            
+            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()