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, 
                     difficulty: str = "intermediate") -> dict:
        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"]
            
            return {
                "transcription": transcription,
                "analysis": analysis
            }
        except Exception as e:
            return {
                "transcription": f"Error: {str(e)}",
                "analysis": "Could not generate analysis."
            }

def create_interface():
    app = gr.Interface(
        fn=LearningPathGenerator().process_audio,
        inputs=[
            gr.Audio(type="filepath", label="Upload Audio"),
            gr.Dropdown(
                choices=["beginner", "intermediate", "advanced"],
                value="intermediate",
                label="Difficulty Level"
            )
        ],
        outputs=[
            gr.Textbox(label="Audio Transcription"),
            gr.Textbox(label="Learning Path")
        ],
        title="🎓 Learning Path Generator",
        description="Upload an audio file describing your learning goals and receive a personalized learning path!"
    )
    return app

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