app.py

import gradio as gr
import torch
from transformers import pipeline

class LearningPathGenerator:
    def __init__(self):
        self.device = 0 if torch.cuda.is_available() else -1
        # Initialize models
        self.transcriber = pipeline("automatic-speech-recognition", 
                                  model="openai/whisper-base",
                                  device=self.device)
        self.generator = pipeline("text-generation", 
                                model="gpt2",
                                device=self.device)

    def process_audio(self, 
                     audio_path: str, 
                     path_name: str,
                     difficulty: str = "intermediate",
                     include_resources: bool = True) -> tuple:
        try:
            # Transcribe audio
            transcription = self.transcriber(audio_path)["text"]
            
            # Generate learning path
            prompt = f"""
            Based on the following text, create a detailed learning path 
            for {difficulty} level:
            {transcription}
            
            Learning path:
            """
            
            analysis = self.generator(prompt, 
                                   max_length=300, 
                                   num_return_sequences=1)[0]["generated_text"]
            
            if include_resources:
                resources = self._generate_resources()
                analysis += "\n\n" + resources

            return (
                gr.Textbox.update(value=transcription, visible=True),
                gr.Textbox.update(value=analysis, visible=True),
                gr.Markdown.update(visible=True, value="✅ Learning path generated successfully!"),
                gr.Button.update(interactive=True)
            )
        except Exception as e:
            return (
                gr.Textbox.update(value=f"Error: {str(e)}", visible=True),
                gr.Textbox.update(value="Could not generate analysis.", visible=True),
                gr.Markdown.update(visible=True, value="❌ An error occurred"),
                gr.Button.update(interactive=True)
            )

    def _generate_resources(self) -> str:
        return """
        📚 Recommended Resources:
        
        1. Books:
           - "Essential Guide"
           - "Advanced Techniques"
           
        2. Online Courses:
           - Coursera: "Topic Specialization"
           - edX: "Advanced Course"
           
        3. Practical Resources:
           - Interactive tutorials
           - Practice exercises
           - Real-world projects
        """

def create_interface():
    with gr.Blocks(theme=gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="purple",
        neutral_hue="gray"
    )) as app:
        gr.Markdown("""
        # 🎓 Learning Path Generator
        
        Upload an audio file describing your learning goals and receive a personalized learning path!
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                # Input components
                with gr.Group():
                    gr.Markdown("### 📝 Input")
                    audio_input = gr.Audio(
                        type="filepath",
                        label="Audio Input",
                        description="Record or upload an audio describing your goals",
                        sources=["microphone", "upload"],
                    )
                    
                    with gr.Row():
                        path_name = gr.Textbox(
                            label="Path Name",
                            placeholder="Give your learning path a name",
                            scale=2
                        )
                        difficulty = gr.Dropdown(
                            choices=["beginner", "intermediate", "advanced"],
                            value="intermediate",
                            label="Difficulty Level",
                            scale=1
                        )
                    
                    include_resources = gr.Checkbox(
                        label="Include Recommended Resources",
                        value=True,
                        info="Add curated learning resources to your path"
                    )
                    
                    process_btn = gr.Button(
                        "🚀 Generate Learning Path",
                        variant="primary",
                        scale=2
                    )

            # Output components
            with gr.Column(scale=2):
                with gr.Group():
                    gr.Markdown("### 📊 Output")
                    status = gr.Markdown(visible=False)
                    
                    with gr.Accordion("Audio Transcription", open=False):
                        transcription = gr.Textbox(
                            label="What we heard",
                            lines=4,
                            visible=False
                        )
                    
                    analysis = gr.Textbox(
                        label="Your Learning Path",
                        lines=10,
                        visible=False
                    )
        
        # Event handlers
        process_btn.click(
            fn=LearningPathGenerator().process_audio,
            inputs=[audio_input, path_name, difficulty, include_resources],
            outputs=[transcription, analysis, status, process_btn],
            api_name="generate_path"
        )
        
        # Examples
        gr.Examples(
            examples=[
                ["path_audio.mp3", "Python Programming", "beginner", True],
                ["path_audio2.mp3", "Data Science", "intermediate", True],
            ],
            inputs=[audio_input, path_name, difficulty, include_resources],
            outputs=[transcription, analysis, status, process_btn],
            cache_examples=True,
        )
        
        # Add additional info
        gr.Markdown("""
        ### 📌 Tips
        - Speak clearly and describe your learning goals in detail
        - Mention any previous experience in the subject
        - Include any specific areas you want to focus on
        """)

    return app

if __name__ == "__main__":
    app = create_interface()
    app.queue()
    app.launch()