app.py

import gradio as gr
import requests
from fpdf import FPDF
import nltk
import os
import tempfile
from nltk.tokenize import sent_tokenize
import random
from groq import Groq

# Ensure no unexpected indentation here
api_key = os.environ.get("GROQ_API_KEY")

# Attempt to download punkt tokenizer
try:
    nltk.download("punkt")
except:
    print("NLTK punkt tokenizer download failed. Using custom tokenizer.")

def custom_sent_tokenize(text):
    return text.split(". ")

def transcribe(audio_path):
    with open(audio_path, "rb") as audio_file:
        audio_data = audio_file.read()

    groq_api_endpoint = "https://api.groq.com/openai/v1/audio/transcriptions"
    headers = {
        "Authorization": f"Bearer {api_key}",  # Fix: api_key is used properly
    }
    files = {
        'file': ('audio.wav', audio_data, 'audio/wav'),
    }
    data = {
        'model': 'whisper-large-v3-turbo',
        'response_format': 'json',
        'language': 'en',
    }

    response = requests.post(groq_api_endpoint, headers=headers, files=files, data=data)

    if response.status_code == 200:
        result = response.json()
        transcript = result.get("text", "No transcription available.")
        return generate_notes(transcript)
    else:
        error_msg = response.json().get("error", {}).get("message", "Unknown error.")
        print(f"API Error: {error_msg}")
        return create_error_pdf(f"API Error: {error_msg}")

def generate_notes(transcript):
    client = Groq(api_key=api_key)  # Use the api_key here

    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": "you are expert question generator from content. Generate one long question, possible number of short questions and mcqs. plz also provide the notes"
            },
            {
                "role": "user",
                "content": transcript,
            }
        ],
        model="llama3-8b-8192",
        temperature=0.5,
        max_tokens=1024,
        top_p=1,
        stop=None,
        stream=False,
    )

    res = chat_completion.choices[0].message.content
     
    # Generate and save a structured PDF
    pdf_path = create_pdf(res, transcript)
    return pdf_path

def create_pdf(question, transcript):
    pdf = FPDF()
    pdf.add_page()
    
    # Add title
    pdf.set_font("Arial", "B", 16)
    pdf.cell(200, 10, "Transcription Notes and Questions", ln=True, align="C")

    # Add transcription content
    pdf.set_font("Arial", "", 12)
    pdf.multi_cell(0, 10, f"Transcription:\n{transcript.encode('latin1', 'replace').decode('latin1')}\n\n")

    # Add long questions
    pdf.set_font("Arial", "B", 14)
    pdf.cell(200, 10, "Questions", ln=True)
    pdf.set_font("Arial", "", 12)
    
    pdf.multi_cell(0, 10, f"- {question.encode('latin1', 'replace').decode('latin1')}\n")

    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
        pdf.output(temp_pdf.name)
        pdf_path = temp_pdf.name
    
    return pdf_path

def create_error_pdf(message):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", "B", 16)
    pdf.cell(200, 10, "Error Report", ln=True, align="C")
    pdf.set_font("Arial", "", 12)
    pdf.multi_cell(0, 10, message.encode('latin1', 'replace').decode('latin1'))
    
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
        pdf.output(temp_pdf.name)
        error_pdf_path = temp_pdf.name
    
    return error_pdf_path

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath"),
    outputs=gr.File(label="Download PDF with Notes or Error Report"),
    title="Voice to Text Converter and Notes Generator",
    description="This app converts audio to text and generates academic questions including long, short, and multiple-choice questions."
)

iface.launch()