app.py

import os
import openai
import whisper
import tempfile
import gradio as gr
from pydub import AudioSegment
import fitz  # PyMuPDF for handling PDFs
import docx  # For handling .docx files
import pandas as pd  # For handling .xlsx and .csv files
# from google.colab import userdata  # Import userdata from google.colab
import requests
from bs4 import BeautifulSoup

# Configure your OpenAI API key using Google Colab userdata
# openai.api_key = userdata.get('OPENAI_API_KEY')

# Load environment variables from the Hugging Face environment
openai.api_key = os.getenv("OPENAI_API_KEY")

# Load the highest quality Whisper model once
model = whisper.load_model("large")

def preprocess_audio(audio_file):
    """Preprocess the audio file to improve quality."""
    try:
        audio = AudioSegment.from_file(audio_file)
        audio = audio.apply_gain(-audio.dBFS + (-20))
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
            audio.export(temp_file.name, format="mp3")
            return temp_file.name
    except Exception as e:
        return f"Error preprocessing the audio file: {str(e)}"

def transcribe_audio(audio_file):
    """Transcribe an audio file."""
    try:
        file_path = preprocess_audio(audio_file) if isinstance(audio_file, str) else preprocess_audio(tempfile.NamedTemporaryFile(delete=False, suffix=".mp3", mode='w+b').name)
        result = model.transcribe(file_path)
        return result.get("text", "Error in transcription")
    except Exception as e:
        return f"Error processing the audio file: {str(e)}"

def read_document(document_path):
    """Read the content of a PDF, DOCX, XLSX or CSV document."""
    try:
        if document_path.endswith(".pdf"):
            doc = fitz.open(document_path)
            return "\n".join([page.get_text() for page in doc])
        elif document_path.endswith(".docx"):
            doc = docx.Document(document_path)
            return "\n".join([paragraph.text for paragraph in doc.paragraphs])
        elif document_path.endswith(".xlsx"):
            return pd.read_excel(document_path).to_string()
        elif document_path.endswith(".csv"):
            return pd.read_csv(document_path).to_string()
        else:
            return "Unsupported file type. Please upload a PDF, DOCX, XLSX or CSV document."
    except Exception as e:
        return f"Error reading the document: {str(e)}"

def read_url(url):
    """Read the content of a URL."""
    try:
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, 'html.parser')
        return soup.get_text()
    except Exception as e:
        return f"Error reading the URL: {str(e)}"

def generate_news(instructions, facts, size, tone, urls, *args):
    """Generate a news article based on instructions, facts, URLs, documents, and transcriptions."""
    knowledge_base = {"instructions": instructions, "facts": facts, "document_content": [], "audio_data": [], "url_content": []}
    num_audios = 5 * 3  # 5 audios * 3 fields (audio, name, position)
    audios = args[:num_audios]
    documents = args[num_audios:]

    for url in urls.split():
        if url:
            knowledge_base["url_content"].append(read_url(url))

    for document in documents:
        if document is not None:
            knowledge_base["document_content"].append(read_document(document.name))

    for i in range(0, len(audios), 3):
        audio_file, name, position = audios[i:i+3]
        if audio_file is not None:
            knowledge_base["audio_data"].append({"audio": audio_file, "name": name, "position": position})

    transcriptions_text, raw_transcriptions, total_direct_quotes = "", "", 0

    for idx, data in enumerate(knowledge_base["audio_data"]):
        if data["audio"] is not None:
            transcription = transcribe_audio(data["audio"])
            transcription_text = f'"{transcription}" - {data["name"]}, {data["position"]}'
            raw_transcription = f'[Audio {idx + 1}]: "{transcription}" - {data["name"]}, {data["position"]}'
            if total_direct_quotes < len(knowledge_base["audio_data"]) * 0.8:
                transcriptions_text += transcription_text + "\n"
                total_direct_quotes += 1
            else:
                transcriptions_text += f'{data["name"]} mentioned that {transcription}' + "\n"
            raw_transcriptions += raw_transcription + "\n\n"

    document_content = "\n\n".join(knowledge_base["document_content"])
    url_content = "\n\n".join(knowledge_base["url_content"])

    internal_prompt = """
    Instructions for the model:
    - Follow the principles of news writing: always try to answer the 5 Ws of a news story in the first paragraph (Who?, What?, When?, Where?, Why?).
    - Ensure that at least 80% of the quotes are direct and in quotation marks.
    - The remaining 20% can be indirect quotes.
    - Do not invent new information.
    - Be rigorous with the provided facts.
    - When processing uploaded documents, extract and highlight important quotes and verbatim testimonies from sources.
    - When processing uploaded documents, extract and highlight key figures.
    """

    prompt = f"""
    {internal_prompt}
    Write a news article with the following information, including a title, a 15-word hook (additional information that complements the title), and the body content with a size of {size} words. The tone should be {tone}.
    Instructions: {knowledge_base["instructions"]}
    Facts: {knowledge_base["facts"]}
    Additional content from documents: {document_content}
    Additional content from URLs: {url_content}
    Use the following transcriptions as direct and indirect quotes (without changing or inventing content):
    {transcriptions_text}
    """

    try:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1
        )
        news_article = response['choices'][0]['message']['content']
        return news_article, raw_transcriptions
    except Exception as e:
        return f"Error generating the news article: {str(e)}", ""

with gr.Blocks() as demo:
    gr.Markdown("## All-in-One News Generator")
    with gr.Row():
        with gr.Column(scale=2):
            instructions = gr.Textbox(label="Instructions for the news article", lines=2)
            facts = gr.Textbox(label="Describe the facts of the news", lines=4)
            size = gr.Number(label="Size of the news body (in words)", value=100)
            tone = gr.Dropdown(label="Tone of the news", choices=["serious", "neutral", "lighthearted"], value="neutral")
            urls = gr.Textbox(label="URLs (separated by space)", lines=2)
        with gr.Column(scale=3):
            inputs_list = [instructions, facts, size, tone, urls]
            with gr.Tabs():
                for i in range(1, 6):
                    with gr.TabItem(f"Audio {i}"):
                        audio = gr.Audio(type="filepath", label=f"Audio {i}")
                        name = gr.Textbox(label="Name", scale=1)
                        position = gr.Textbox(label="Position", scale=1)
                        inputs_list.extend([audio, name, position])
                for i in range(1, 6):
                    with gr.TabItem(f"Document {i}"):
                        document = gr.File(label=f"Document {i}", type="filepath", file_count="single")
                        inputs_list.append(document)

    gr.Markdown("---") # Visual separator

    with gr.Row():
        transcriptions_output = gr.Textbox(label="Transcriptions", lines=10)

    gr.Markdown("---") # Visual separator

    with gr.Row():
        generate = gr.Button("Generate draft")
    with gr.Row():
        news_output = gr.Textbox(label="Generated draft", lines=20)

    generate.click(fn=generate_news, inputs=inputs_list, outputs=[news_output, transcriptions_output])

demo.launch(share=True)