File size: 24,871 Bytes
1b167bf
 
 
 
 
 
 
 
 
 
4e5d878
1b167bf
951c395
 
1b167bf
951c395
ab6abb0
3e010de
 
f1d02c3
 
1b167bf
 
 
 
 
 
 
 
3e010de
 
 
 
 
951c395
 
 
 
 
 
 
 
 
 
 
 
 
7a1615b
951c395
 
3e010de
951c395
3e010de
 
f1d02c3
951c395
7a1615b
d8c6271
f1d02c3
 
 
 
 
 
 
951c395
3e010de
f1d02c3
 
 
 
 
 
 
 
 
 
 
 
7a1615b
3e010de
f1d02c3
3e010de
 
 
 
 
 
1b167bf
3e010de
 
f1d02c3
3e010de
951c395
f1d02c3
c59e337
 
d283cbc
f1d02c3
 
c59e337
3e010de
 
951c395
 
3e010de
951c395
 
3e010de
 
f1d02c3
3e010de
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ab6abb0
f1d02c3
3e010de
 
f1d02c3
3e010de
 
f1d02c3
3e010de
ab6abb0
3e010de
 
 
 
 
 
 
 
 
 
 
 
 
 
951c395
 
ab6abb0
1b167bf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4e5d878
1b167bf
 
4e5d878
 
 
 
 
 
 
 
 
f1d02c3
4e5d878
 
 
 
 
 
1b167bf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3e010de
1b167bf
 
 
3e010de
9b37297
1b167bf
 
 
 
f1d02c3
3e010de
1b167bf
f1d02c3
1b167bf
 
9b37297
 
 
1b167bf
3e010de
9b37297
1b167bf
 
3e010de
 
 
 
 
 
 
1b167bf
 
 
 
 
ab6abb0
1b167bf
 
 
 
 
 
 
 
 
3e010de
1b167bf
 
 
 
 
 
9b37297
1b167bf
 
ab6abb0
1b167bf
 
3e010de
 
 
1b167bf
3e010de
 
 
 
1b167bf
 
3e010de
 
 
 
 
 
 
 
 
 
 
 
 
f1d02c3
1b167bf
9b37297
1b167bf
 
 
 
3e010de
 
 
1b167bf
 
 
 
9b37297
 
1b167bf
 
 
 
 
 
 
 
 
 
3e010de
1b167bf
3e010de
1b167bf
3e010de
 
 
f1d02c3
3e010de
 
9b37297
1b167bf
f7aec95
 
1b167bf
 
 
 
 
 
 
 
 
 
f1d02c3
f7aec95
 
 
 
1b167bf
 
 
 
 
3e010de
1b167bf
3e010de
9b37297
 
 
1b167bf
3e010de
1b167bf
f7aec95
9b37297
 
 
1b167bf
3e010de
1b167bf
f1d02c3
3e010de
f7aec95
3e010de
 
 
 
1b167bf
 
3e010de
 
f1d02c3
3e010de
 
 
 
 
 
 
 
 
 
 
 
1b167bf
 
 
3e010de
1b167bf
 
 
3e010de
 
9b37297
1b167bf
3e010de
9b37297
3e010de
 
 
 
 
9b37297
3e010de
1b167bf
3e010de
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ab6abb0
3e010de
1b167bf
 
3e010de
1b167bf
3e010de
 
 
 
 
 
1b167bf
 
 
 
 
 
 
3e010de
1b167bf
 
 
 
 
 
 
7a1615b
 
 
 
 
f1d02c3
7a1615b
f1d02c3
 
 
 
 
 
 
 
 
 
 
 
7a1615b
 
f1d02c3
7a1615b
 
 
 
f1d02c3
7a1615b
 
fb3575f
7a1615b
 
 
 
 
 
 
1b167bf
 
 
 
 
9b37297
3e010de
 
 
f1d02c3
ab6abb0
1b167bf
3e010de
f1d02c3
 
1b167bf
 
 
f1d02c3
 
 
 
1b167bf
f1d02c3
 
 
 
1b167bf
3e010de
 
f1d02c3
 
3e010de
 
 
 
 
f1d02c3
 
 
3e010de
 
1b167bf
 
f7aec95
f1d02c3
1b167bf
 
f1d02c3
 
 
 
 
f7aec95
f1d02c3
f7aec95
f1d02c3
 
3e010de
 
f1d02c3
3e010de
f1d02c3
3e010de
 
f1d02c3
3e010de
 
f1d02c3
 
 
3e010de
f1d02c3
 
 
3e010de
f1d02c3
3e010de
 
f1d02c3
1b167bf
 
f1d02c3
1b167bf
 
3e010de
f1d02c3
 
3e010de
f1d02c3
3e010de
f1d02c3
 
3e010de
 
f1d02c3
 
 
3e010de
f1d02c3
 
 
3e010de
f1d02c3
1b167bf
 
f1d02c3
 
3e010de
 
f1d02c3
 
 
3e010de
f1d02c3
3e010de
 
f1d02c3
 
 
3e010de
f1d02c3
3e010de
1b167bf
f1d02c3
1b167bf
 
f1d02c3
1b167bf
3e010de
 
f1d02c3
3e010de
f1d02c3
3e010de
f7aec95
f1d02c3
3e010de
1b167bf
 
 
 
3e010de
 
 
7a1615b
3e010de
1b167bf
f1d02c3
1b167bf
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
import spaces
import gradio as gr
import logging
import os
import tempfile
import pandas as pd
import requests
from bs4 import BeautifulSoup
import torch
import whisper
import subprocess
from pydub import AudioSegment
import fitz
import docx
import yt_dlp
from functools import lru_cache
import gc
import time
from huggingface_hub import login
from unsloth import FastLanguageModel
from transformers import AutoTokenizer

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Login to Hugging Face Hub if token is available
HUGGINGFACE_TOKEN = os.environ.get('HUGGINGFACE_TOKEN')
if HUGGINGFACE_TOKEN:
    login(token=HUGGINGFACE_TOKEN)

class ModelManager:
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(ModelManager, cls).__new__(cls)
            cls._instance._initialized = False
        return cls._instance
    
    def __init__(self):
        if not self._initialized:
            self.tokenizer = None
            self.model = None
            self.pipeline = None
            self.whisper_model = None
            self._initialized = True
            self.last_used = time.time()
    
    @spaces.GPU()
    def initialize_llm(self):
        """Initialize LLM model with Unsloth optimization"""
        try:
            MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
            
            logger.info("Loading Unsloth-optimized model...")
            self.model, self.tokenizer = FastLanguageModel.from_pretrained(
                model_name = MODEL_NAME,
                max_seq_length = 2048,
                dtype = torch.float16,
                load_in_4bit = True,
                token = HUGGINGFACE_TOKEN,
            )
            
            # Enable LoRA for better ZeroGPU performance
            self.model = FastLanguageModel.get_peft_model(
                self.model,
                r = 16,
                target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                                 "gate_proj", "up_proj", "down_proj"],
                lora_alpha = 16,
                lora_dropout = 0,
                bias = "none",
                use_gradient_checkpointing = True,
                random_state = 3407,
                max_seq_length = 2048,
            )
            
            logger.info("LLM initialized successfully with Unsloth")
            self.last_used = time.time()
            return True
            
        except Exception as e:
            logger.error(f"Error initializing LLM: {str(e)}")
            raise

    @spaces.GPU()
    def initialize_whisper(self):
        """Initialize Whisper model with safety fix"""
        try:
            logger.info("Loading Whisper model...")
            # Load with weights_only=True for security
            self.whisper_model = whisper.load_model(
                "tiny",
                device="cuda" if torch.cuda.is_available() else "cpu",
                download_root="/tmp/whisper",
                weights_only=True  # Security fix
            )
            logger.info("Whisper model initialized successfully")
            self.last_used = time.time()
            return True
        except Exception as e:
            logger.error(f"Error initializing Whisper: {str(e)}")
            raise

    def check_llm_initialized(self):
        """Check if LLM is initialized and initialize if needed"""
        if self.tokenizer is None or self.model is None:
            logger.info("LLM not initialized, initializing...")
            self.initialize_llm()
        self.last_used = time.time()
    
    def check_whisper_initialized(self):
        """Check if Whisper model is initialized and initialize if needed"""
        if self.whisper_model is None:
            logger.info("Whisper model not initialized, initializing...")
            self.initialize_whisper()
        self.last_used = time.time()
    
    def reset_models(self, force=False):
        """Reset models to free memory if they haven't been used recently"""
        current_time = time.time()
        if force or (current_time - self.last_used > 600):  
            try:
                logger.info("Resetting models to free memory...")
                
                if self.model is not None:
                    del self.model
                    
                if self.tokenizer is not None:
                    del self.tokenizer
                    
                if self.whisper_model is not None:
                    del self.whisper_model
                
                self.tokenizer = None
                self.model = None
                self.whisper_model = None
                
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
                    torch.cuda.synchronize()
                
                gc.collect()
                logger.info("Models reset successfully")
                
            except Exception as e:
                logger.error(f"Error resetting models: {str(e)}")

model_manager = ModelManager()

@lru_cache(maxsize=32)
def download_social_media_video(url):
    """Download 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 downloaded successfully: {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):
    """Convert a video file to audio using ffmpeg directly."""
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
            output_file = temp_file.name
        
        command = [
            "ffmpeg", 
            "-i", video_file, 
            "-q:a", "0",
            "-map", "a",
            "-vn",
            output_file,
            "-y"
        ]
        
        subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        
        logger.info(f"Video converted to audio: {output_file}")
        return output_file
    except Exception as e:
        logger.error(f"Error converting video: {str(e)}")
        raise

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")
            logger.info(f"Audio preprocessed: {temp_file.name}")
            return temp_file.name
    except Exception as e:
        logger.error(f"Error preprocessing audio: {str(e)}")
        raise

@spaces.GPU()
def transcribe_audio(file):
    """Transcribe an audio or video file."""
    try:
        model_manager.check_whisper_initialized()
        
        if isinstance(file, str) and file.startswith('http'):
            file_path = download_social_media_video(file)
        elif isinstance(file, str) and file.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
            file_path = convert_video_to_audio(file)
        elif file is not None:
            file_path = preprocess_audio(file.name)
        else:
            return ""

        logger.info(f"Transcribing audio: {file_path}")
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"Audio file not found: {file_path}")

        with torch.inference_mode():
            result = model_manager.whisper_model.transcribe(file_path)
                
        transcription = result.get("text", "Error in transcription")
        logger.info(f"Transcription completed: {transcription[:50]}...")
        
        try:
            if os.path.exists(file_path):
                os.remove(file_path)
        except Exception as e:
            logger.warning(f"Could not remove temp file {file_path}: {str(e)}")
            
        return transcription
    except Exception as e:
        logger.error(f"Error transcribing: {str(e)}")
        return f"Error processing the file: {str(e)}"

@lru_cache(maxsize=32)
def read_document(document_path):
    """Read the content of a 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", ".xls")):
            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:
        logger.error(f"Error reading document: {str(e)}")
        return f"Error reading document: {str(e)}"

@lru_cache(maxsize=32)
def read_url(url):
    """Read the content of a URL."""
    if not url or url.strip() == "":
        return ""
        
    try:
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        }
        response = requests.get(url, headers=headers, timeout=15)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, 'html.parser')
        
        for element in soup(["script", "style", "meta", "noscript", "iframe", "header", "footer", "nav"]):
            element.extract()
            
        main_content = soup.find("main") or soup.find("article") or soup.find("div", class_=["content", "main", "article"])
        if main_content:
            text = main_content.get_text(separator='\n', strip=True)
        else:
            text = soup.get_text(separator='\n', strip=True)
            
        lines = [line.strip() for line in text.split('\n') if line.strip()]
        text = '\n'.join(lines)
        
        return text[:10000]
    except Exception as e:
        logger.error(f"Error reading URL: {str(e)}")
        return f"Error reading URL: {str(e)}"

def process_social_content(url):
    """Process social media content."""
    if not url or url.strip() == "":
        return None
        
    try:
        text_content = read_url(url)
        try:
            video_content = transcribe_audio(url)
        except Exception as e:
            logger.error(f"Error processing video content: {str(e)}")
            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

@spaces.GPU()
def generate_news(instructions, facts, size, tone, *args):
    """Generate a news article based on provided data"""
    try:
        if isinstance(size, float):
            size = int(size)
        elif not isinstance(size, int):
            size = 250
            
        model_manager.check_llm_initialized()
        
        knowledge_base = {
            "instructions": instructions or "",
            "facts": facts or "",
            "document_content": [],
            "audio_data": [],
            "url_content": [],
            "social_content": []
        }

        num_audios = 5 * 3
        num_social_urls = 3 * 3
        num_urls = 5

        args = list(args)
        
        while len(args) < (num_audios + num_social_urls + num_urls + 5):
            args.append("")
            
        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:]

        logger.info("Processing URLs...")
        for url in urls:
            if url and isinstance(url, str) and url.strip():
                content = read_url(url)
                if content and not content.startswith("Error"):
                    knowledge_base["url_content"].append(content)

        logger.info("Processing documents...")
        for document in documents:
            if document and hasattr(document, 'name'):
                content = read_document(document.name)
                if content and not content.startswith("Error"):
                    knowledge_base["document_content"].append(content)

        logger.info("Processing audio/video files...")
        for i in range(0, len(audios), 3):
            if i+2 < len(audios):
                audio_file, name, position = audios[i:i+3]
                if audio_file and hasattr(audio_file, 'name'):
                    knowledge_base["audio_data"].append({
                        "audio": audio_file,
                        "name": name or "Unknown",
                        "position": position or "Not specified"
                    })

        logger.info("Processing social media content...")
        for i in range(0, len(social_urls), 3):
            if i+2 < len(social_urls):
                social_url, social_name, social_context = social_urls[i:i+3]
                if social_url and isinstance(social_url, str) and social_url.strip():
                    social_content = process_social_content(social_url)
                    if social_content:
                        knowledge_base["social_content"].append({
                            "url": social_url,
                            "name": social_name or "Unknown",
                            "context": social_context or "Not specified",
                            "text": social_content.get("text", ""),
                            "video": social_content.get("video", "")
                        })

        transcriptions_text = ""
        raw_transcriptions = ""

        logger.info("Transcribing audio...")
        for idx, data in enumerate(knowledge_base["audio_data"]):
            if data["audio"] is not None:
                transcription = transcribe_audio(data["audio"])
                if transcription and not transcription.startswith("Error"):
                    transcriptions_text += f'"{transcription}" - {data["name"]}, {data["position"]}\n\n'
                    raw_transcriptions += f'[Audio/Video {idx + 1}]: "{transcription}" - {data["name"]}, {data["position"]}\n\n'

        for idx, data in enumerate(knowledge_base["social_content"]):
            if data["text"] and not str(data["text"]).startswith("Error"):
                text_excerpt = data["text"][:500] + "..." if len(data["text"]) > 500 else data["text"]
                social_text = f'[Social media {idx+1} - text]: "{text_excerpt}" - {data["name"]}, {data["context"]}\n\n'
                transcriptions_text += social_text
                raw_transcriptions += social_text
                
            if data["video"] and not str(data["video"]).startswith("Error"):
                video_transcription = f'[Social media {idx+1} - video]: "{data["video"]}" - {data["name"]}, {data["context"]}\n\n'
                transcriptions_text += video_transcription
                raw_transcriptions += video_transcription

        document_summaries = []
        for idx, doc in enumerate(knowledge_base["document_content"]):
            if len(doc) > 1000:
                doc_excerpt = doc[:1000] + "... [document continues]"
            else:
                doc_excerpt = doc
            document_summaries.append(f"[Document {idx+1}]: {doc_excerpt}")
        
        document_content = "\n\n".join(document_summaries)
        
        url_summaries = []
        for idx, url_content in enumerate(knowledge_base["url_content"]):
            if len(url_content) > 1000:
                url_excerpt = url_content[:1000] + "... [content continues]"
            else:
                url_excerpt = url_content
            url_summaries.append(f"[URL {idx+1}]: {url_excerpt}")
            
        url_content = "\n\n".join(url_summaries)

        prompt = f"""<s>[INST] You are a professional news writer. Write a news article based on the following information:

Instructions: {knowledge_base["instructions"]}

Facts: {knowledge_base["facts"]}

Additional content from documents: 
{document_content}

Additional content from URLs: 
{url_content}

Use these transcriptions as direct and indirect quotes:
{transcriptions_text}

Follow these requirements:
- Write a title
- Write a 15-word hook that complements the title
- Write the body with approximately {size} words
- Use a {tone} tone
- Answer the 5 Ws (Who, What, When, Where, Why) in the first paragraph
- Use at least 80% direct quotes (in quotation marks)
- Use proper journalistic style
- Do not invent information
- Be rigorous with the provided facts [/INST]"""

        try:
            logger.info("Generating news article...")
            
            max_length = min(len(prompt.split()) + size * 2, 2048)
            
            inputs = model_manager.tokenizer(
                prompt,
                return_tensors = "pt",
                padding = True,
                truncation = True,
                max_length = 2048,
            ).to("cuda")

            outputs = model_manager.model.generate(
                **inputs,
                max_new_tokens = size + 100,
                temperature = 0.7,
                do_sample = True,
                pad_token_id = model_manager.tokenizer.eos_token_id,
            )
            
            generated_text = model_manager.tokenizer.decode(outputs[0], skip_special_tokens = True)
            
            if "[/INST]" in generated_text:
                news_article = generated_text.split("[/INST]")[1].strip()
            else:
                prompt_fragment = " ".join(prompt.split()[:50])
                if prompt_fragment in generated_text:
                    news_article = generated_text[generated_text.find(prompt_fragment) + len(prompt_fragment):].strip()
                else:
                    news_article = generated_text
            
            logger.info(f"News generation completed: {len(news_article)} chars")
            
        except Exception as gen_error:
            logger.error(f"Error in text generation: {str(gen_error)}")
            raise
        
        return news_article, raw_transcriptions

    except Exception as e:
        logger.error(f"Error generating news: {str(e)}")
        try:
            model_manager.reset_models(force=True)
        except Exception as reset_error:
            logger.error(f"Failed to reset models: {str(reset_error)}")
        return f"Error generating news: {str(e)}", "Error processing transcriptions."

def create_demo():
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("# πŸ“° NewsIA - AI News Generator")
        gr.Markdown("Create professional news articles from multiple sources.")
        
        with gr.Row():
            with gr.Column(scale=2):
                instructions = gr.Textbox(
                    label="News Instructions",
                    placeholder="Enter specific instructions for news generation",
                    lines=2
                )
                facts = gr.Textbox(
                    label="Key Facts",
                    placeholder="Describe the most important facts to include",
                    lines=4
                )
                
                with gr.Row():
                    size = gr.Slider(
                        label="Approximate Length (words)",
                        minimum=100,
                        maximum=500,
                        value=250,
                        step=50
                    )
                    tone = gr.Dropdown(
                        label="News Tone",
                        choices=["serious", "neutral", "funny", "formal", "informal", "urgent"],
                        value="neutral"
                    )

            with gr.Column(scale=3):
                inputs_list = []
                inputs_list.extend([instructions, facts, size, tone])

                with gr.Tabs():
                    with gr.TabItem("πŸ“ Documents"):
                        documents = []
                        for i in range(1, 6):
                            doc = gr.File(
                                label=f"Document {i}",
                                file_types=["pdf", "docx", "xlsx", "csv"],
                                file_count="single"
                            )
                            documents.append(doc)
                            inputs_list.append(doc)

                    with gr.TabItem("πŸ”Š Audio/Video"):
                        for i in range(1, 6):
                            with gr.Group():
                                gr.Markdown(f"**Source {i}**")
                                file = gr.File(
                                    label=f"Audio/Video {i}",
                                    file_types=["audio", "video"]
                                )
                                with gr.Row():
                                    name = gr.Textbox(
                                        label="Name",
                                        placeholder="Interviewee name"
                                    )
                                    position = gr.Textbox(
                                        label="Position/Role",
                                        placeholder="Position or role"
                                    )
                                inputs_list.extend([file, name, position])

                    with gr.TabItem("🌐 URLs"):
                        for i in range(1, 6):
                            url = gr.Textbox(
                                label=f"URL {i}",
                                placeholder="https://..."
                            )
                            inputs_list.append(url)

                    with gr.TabItem("πŸ“± Social Media"):
                        for i in range(1, 4):
                            with gr.Group():
                                gr.Markdown(f"**Social Media {i}**")
                                social_url = gr.Textbox(
                                    label="URL",
                                    placeholder="https://..."
                                )
                                with gr.Row():
                                    social_name = gr.Textbox(
                                        label="Account/Name",
                                        placeholder="Account or person name"
                                    )
                                    social_context = gr.Textbox(
                                        label="Context",
                                        placeholder="Relevant context"
                                    )
                                inputs_list.extend([social_url, social_name, social_context])

        with gr.Row():
            generate_btn = gr.Button("✨ Generate News", variant="primary")
            reset_btn = gr.Button("πŸ”„ Clear All")

        with gr.Tabs():
            with gr.TabItem("πŸ“„ Generated News"):
                news_output = gr.Textbox(
                    label="News Draft",
                    lines=15,
                    show_copy_button=True
                )
            
            with gr.TabItem("πŸŽ™οΈ Transcriptions"):
                transcriptions_output = gr.Textbox(
                    label="Source Transcriptions",
                    lines=10,
                    show_copy_button=True
                )

        generate_btn.click(
            fn=generate_news,
            inputs=inputs_list,
            outputs=[news_output, transcriptions_output]
        )
        
        def reset_all():
            return [None]*len(inputs_list) + ["", ""]
        
        reset_btn.click(
            fn=reset_all,
            inputs=None,
            outputs=inputs_list + [news_output, transcriptions_output]
        )

    return demo

if __name__ == "__main__":
    try:
        model_manager.initialize_whisper()
    except Exception as e:
        logger.warning(f"Initial whisper model loading failed: {str(e)}")
    
    demo = create_demo()
    demo.queue(max_size=5)
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )