Update README.md

README.md

@@ -4,8 +4,343 @@ emoji: 📚
 colorFrom: red
 colorTo: green
 sdk: gradio
-sdk_version: 4.31.5
+sdk_version: 5.23.2
 app_file: app.py
 pinned: false
 license: mit
 ---
+
+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
+import requests
+from bs4 import BeautifulSoup
+from moviepy.editor import VideoFileClip
+import yt_dlp
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Configure your OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Load the highest quality Whisper model once
+model = whisper.load_model("large")
+
+def download_social_media_video(url):
+    """Downloads a video from social media."""
+    ydl_opts = {
+        'format': 'bestaudio/best',
+        'postprocessors': [{
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'mp3',
+            'preferredquality': '192',
+        }],
+        'outtmpl': '%(id)s.%(ext)s',
+    }
+    try:
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            info_dict = ydl.extract_info(url, download=True)
+            audio_file = f"{info_dict['id']}.mp3"
+        logger.info(f"Video successfully downloaded: {audio_file}")
+        return audio_file
+    except Exception as e:
+        logger.error(f"Error downloading video: {str(e)}")
+        raise
+
+def convert_video_to_audio(video_file):
+    """Converts a video file to audio."""
+    try:
+        video = VideoFileClip(video_file)
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
+            video.audio.write_audiofile(temp_file.name)
+            logger.info(f"Video converted to audio: {temp_file.name}")
+            return temp_file.name
+    except Exception as e:
+        logger.error(f"Error converting video to audio: {str(e)}")
+        raise
+
+def preprocess_audio(audio_file):
+    """Preprocesses 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")
+            logger.info(f"Audio preprocessed: {temp_file.name}")
+            return temp_file.name
+    except Exception as e:
+        logger.error(f"Error preprocessing audio file: {str(e)}")
+        raise
+
+def transcribe_audio(file):
+    """Transcribes an audio or video file."""
+    try:
+        if isinstance(file, str) and file.startswith('http'):
+            logger.info(f"Downloading social media video: {file}")
+            file_path = download_social_media_video(file)
+        elif isinstance(file, str) and file.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
+            logger.info(f"Converting local video to audio: {file}")
+            file_path = convert_video_to_audio(file)
+        else:
+            logger.info(f"Preprocessing audio file: {file}")
+            file_path = preprocess_audio(file)
+
+        logger.info(f"Transcribing audio: {file_path}")
+        result = model.transcribe(file_path)
+        transcription = result.get("text", "Error in transcription")
+        logger.info(f"Transcription completed: {transcription[:50]}...")
+        return transcription
+    except Exception as e:
+        logger.error(f"Error processing file: {str(e)}")
+        return f"Error processing file: {str(e)}"
+
+def read_document(document_path):
+    """Reads content from PDF, DOCX, XLSX or CSV documents."""
+    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 document: {str(e)}"
+
+def read_url(url):
+    """Reads content from 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 URL: {str(e)}"
+
+def process_social_content(url):
+    """Processes content from a social media URL, handling both text and video."""
+    try:
+        # First, try to read content as text
+        text_content = read_url(url)
+
+        # Then, try to process as video
+        try:
+            video_content = transcribe_audio(url)
+        except Exception:
+            video_content = None
+
+        return {
+            "text": text_content,
+            "video": video_content
+        }
+    except Exception as e:
+        logger.error(f"Error processing social content: {str(e)}")
+        return None
+
+def generate_news(instructions, facts, size, tone, *args):
+    """Generates a news article from instructions, facts, URLs, documents, transcriptions, and social media content."""
+    knowledge_base = {
+        "instructions": instructions,
+        "facts": facts,
+        "document_content": [],
+        "audio_data": [],
+        "url_content": [],
+        "social_content": []
+    }
+    num_audios = 5 * 3  # 5 audios/videos * 3 fields (file, name, position)
+    num_social_urls = 3 * 3  # 3 social media URLs * 3 fields (URL, name, context)
+    num_urls = 5  # 5 general URLs
+    audios = args[:num_audios]
+    social_urls = args[num_audios:num_audios+num_social_urls]
+    urls = args[num_audios+num_social_urls:num_audios+num_social_urls+num_urls]
+    documents = args[num_audios+num_social_urls+num_urls:]
+
+    for url in urls:
+        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})
+
+    for i in range(0, len(social_urls), 3):
+        social_url, social_name, social_context = social_urls[i:i+3]
+        if social_url:
+            social_content = process_social_content(social_url)
+            if social_content:
+                knowledge_base["social_content"].append({
+                    "url": social_url,
+                    "name": social_name,
+                    "context": social_context,
+                    "text": social_content["text"],
+                    "video": social_content["video"]
+                })
+                logger.info(f"Social media content processed: {social_url}")
+
+    transcriptions_text, raw_transcriptions = "", ""
+
+    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/Video {idx + 1}]: "{transcription}" - {data["name"]}, {data["position"]}'
+            transcriptions_text += transcription_text + "\n"
+            raw_transcriptions += raw_transcription + "\n\n"
+
+    for data in knowledge_base["social_content"]:
+        if data["text"]:
+            transcription_text = f'[Social media text]: "{data["text"][:200]}..." - {data["name"]}, {data["context"]}'
+            transcriptions_text += transcription_text + "\n"
+            raw_transcriptions += transcription_text + "\n\n"
+        if data["video"]:
+            transcription_video = f'[Social media video]: "{data["video"]}" - {data["name"]}, {data["context"]}'
+            transcriptions_text += transcription_video + "\n"
+            raw_transcriptions += transcription_video + "\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 news article principles: answer the 5 Ws in the first paragraph (Who?, What?, When?, Where?, Why?).
+    - Ensure at least 80% of quotes are direct and in quotation marks.
+    - The remaining 20% can be indirect quotes.
+    - Don't invent new information.
+    - Be rigorous with provided facts.
+    - When processing uploaded documents, extract and highlight important quotes and testimonials from sources.
+    - When processing uploaded documents, extract and highlight key figures.
+    - Avoid using the date at the beginning of the news body. Start directly with the 5Ws.
+    - Include social media content relevantly, citing the source and providing proper context.
+    - Make sure to relate the provided context for social media content with its corresponding transcription or text.
+    """
+
+    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 content body with {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-4o-mini",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.1
+        )
+        news = response['choices'][0]['message']['content']
+        return news, raw_transcriptions
+    except Exception as e:
+        logger.error(f"Error generating news article: {str(e)}")
+        return f"Error generating news article: {str(e)}", ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("## All-in-One News Generator")
+    
+    # Add tool description and attribution
+    gr.Markdown("""
+    ### About this tool
+    
+    This AI-powered news generator helps journalists and content creators produce news articles by processing multiple types of input:
+    - Audio and video files with automatic transcription
+    - Social media content
+    - Documents (PDF, DOCX, XLSX, CSV)
+    - Web URLs
+    
+    The tool uses advanced AI to generate well-structured news articles following journalistic principles and maintaining the integrity of source quotes.
+    
+    Created by [Camilo Vega](https://www.linkedin.com/in/camilo-vega-169084b1/), AI Consultant
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            instructions = gr.Textbox(label="News article instructions", lines=2)
+            facts = gr.Textbox(label="Describe the news facts", lines=4)
+            size = gr.Number(label="Content body size (in words)", value=100)
+            tone = gr.Dropdown(label="News tone", choices=["serious", "neutral", "lighthearted"], value="neutral")
+        with gr.Column(scale=3):
+            inputs_list = [instructions, facts, size, tone]
+            with gr.Tabs():
+                for i in range(1, 6):
+                    with gr.TabItem(f"Audio/Video {i}"):
+                        file = gr.File(label=f"Audio/Video {i}", type="filepath", file_types=["audio", "video"])
+                        name = gr.Textbox(label="Name", scale=1)
+                        position = gr.Textbox(label="Position", scale=1)
+                        inputs_list.extend([file, name, position])
+                for i in range(1, 4):
+                    with gr.TabItem(f"Social Media {i}"):
+                        social_url = gr.Textbox(label=f"Social media URL {i}", lines=1)
+                        social_name = gr.Textbox(label=f"Person/account name {i}", scale=1)
+                        social_context = gr.Textbox(label=f"Content context {i}", lines=2)
+                        inputs_list.extend([social_url, social_name, social_context])
+                for i in range(1, 6):
+                    with gr.TabItem(f"URL {i}"):
+                        url = gr.Textbox(label=f"URL {i}", lines=1)
+                        inputs_list.append(url)
+                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])
+
+# Add description about how to use the app
+gr.Markdown("""
+### How to Use This App
+
+1. **Input your requirements:**
+   - Enter your news article instructions
+   - Describe the key facts of your news story
+   - Set the desired word count and tone
+
+2. **Add your sources:**
+   - Upload audio/video files for automatic transcription
+   - Add social media URLs to extract content
+   - Include web URLs for additional information
+   - Upload documents (PDF, DOCX, XLSX, CSV) to extract relevant data
+
+3. **Generate your draft:**
+   - Click "Generate Draft" to create your news article
+   - Review the transcriptions to verify source accuracy
+   - Use the generated draft as a starting point for your news story
+
+This tool helps streamline the news writing process by automatically gathering, organizing, and synthesizing information from multiple sources into a cohesive article that follows journalistic best practices.
+
+Created by [Camilo Vega](https://www.linkedin.com/in/camilo-vega-169084b1/), AI Consultant
+""")
+
+demo.launch(share=True)