File size: 35,578 Bytes
7fd431e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7d5c918
 
7fd431e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7d5c918
7fd431e
 
 
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
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
import concurrent.futures as cf
import glob
import io
import os
import time
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import List, Literal

import gradio as gr

from loguru import logger
from openai import OpenAI
from promptic import llm
from pydantic import BaseModel, ValidationError
from pypdf import PdfReader
from tenacity import retry, retry_if_exception_type

import re

def read_readme():
    readme_path = Path("README.md")
    if readme_path.exists():
        with open(readme_path, "r") as file:
            content = file.read()
            # Use regex to remove metadata enclosed in -- ... --
            content = re.sub(r'--.*?--', '', content, flags=re.DOTALL)
            return content
    else:
        return "README.md not found. Please check the repository for more information."
        
# Define multiple sets of instruction templates
INSTRUCTION_TEMPLATES = {
################# PODCAST ##################
    "podcast": {
        "intro": """Your task is to take the input text provided and turn it into an lively, engaging, informative podcast dialogue, in the style of NPR. The input text may be messy or unstructured, as it could come from a variety of sources like PDFs or web pages. 

Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points, identify definitions, and interesting facts that could be discussed in a podcast. 

Define all terms used carefully for a broad audience of listeners.
""",
        "text_instructions": "First, carefully read through the input text and identify the main topics, key points, and any interesting facts or anecdotes. Think about how you could present this information in a fun, engaging way that would be suitable for a high quality presentation.",
        "scratch_pad": """Brainstorm creative ways to discuss the main topics and key points you identified in the input text. Consider using analogies, examples, storytelling techniques, or hypothetical scenarios to make the content more relatable and engaging for listeners.

Keep in mind that your podcast should be accessible to a general audience, so avoid using too much jargon or assuming prior knowledge of the topic. If necessary, think of ways to briefly explain any complex concepts in simple terms.

Use your imagination to fill in any gaps in the input text or to come up with thought-provoking questions that could be explored in the podcast. The goal is to create an informative and entertaining dialogue, so feel free to be creative in your approach.

Define all terms used clearly and spend effort to explain the background.

Write your brainstorming ideas and a rough outline for the podcast dialogue here. Be sure to note the key insights and takeaways you want to reiterate at the end.

Make sure to make it fun and exciting. 
""",
        "prelude": """Now that you have brainstormed ideas and created a rough outline, it's time to write the actual podcast dialogue. Aim for a natural, conversational flow between the host and any guest speakers. Incorporate the best ideas from your brainstorming session and make sure to explain any complex topics in an easy-to-understand way.
""",
        "dialog": """Write a very long, engaging, informative podcast dialogue here, based on the key points and creative ideas you came up with during the brainstorming session. Use a conversational tone and include any necessary context or explanations to make the content accessible to a general audience. 

Never use made-up names for the hosts and guests, but make it an engaging and immersive experience for listeners. Do not include any bracketed placeholders like [Host] or [Guest]. Design your output to be read aloud -- it will be directly converted into audio.

Make the dialogue as long and detailed as possible, while still staying on topic and maintaining an engaging flow. Aim to use your full output capacity to create the longest podcast episode you can, while still communicating the key information from the input text in an entertaining way.

At the end of the dialogue, have the host and guest speakers naturally summarize the main insights and takeaways from their discussion. This should flow organically from the conversation, reiterating the key points in a casual, conversational manner. Avoid making it sound like an obvious recap - the goal is to reinforce the central ideas one last time before signing off. 

The podcast should have around 20000 words.
""",
    },
################# MATERIAL DISCOVERY SUMMARY ##################
    "SciAgents material discovery summary": {
        "intro": """Your task is to take the input text provided and turn it into a lively, engaging conversation between a professor and a student in a panel discussion that describes a new material. The professor acts like Richard Feynman, but you never mention the name.

The input text is the result of a design developed by SciAgents, an AI tool for scientific discovery that has come up with a detailed materials design.

Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points, identify definitions, and interesting facts that could be discussed in a podcast.

Define all terms used carefully for a broad audience of listeners.
""",
        "text_instructions": "First, carefully read through the input text and identify the main topics, key points, and any interesting facts or anecdotes. Think about how you could present this information in a fun, engaging way that would be suitable for a high quality presentation.",
        "scratch_pad": """Brainstorm creative ways to discuss the main topics and key points you identified in the material design summary, especially paying attention to design features developed by SciAgents. Consider using analogies, examples, storytelling techniques, or hypothetical scenarios to make the content more relatable and engaging for listeners.

Keep in mind that your description should be accessible to a general audience, so avoid using too much jargon or assuming prior knowledge of the topic. If necessary, think of ways to briefly explain any complex concepts in simple terms.

Use your imagination to fill in any gaps in the input text or to come up with thought-provoking questions that could be explored in the podcast. The goal is to create an informative and entertaining dialogue, so feel free to be creative in your approach.

Define all terms used clearly and spend effort to explain the background.

Write your brainstorming ideas and a rough outline for the podcast dialogue here. Be sure to note the key insights and takeaways you want to reiterate at the end.

Make sure to make it fun and exciting. You never refer to the podcast, you just discuss the discovery and you focus on the new material design only.
""",
        "prelude": """Now that you have brainstormed ideas and created a rough outline, it's time to write the actual podcast dialogue. Aim for a natural, conversational flow between the host and any guest speakers. Incorporate the best ideas from your brainstorming session and make sure to explain any complex topics in an easy-to-understand way.
""",
        "dialog": """Write a very long, engaging, informative dialogue here, based on the key points and creative ideas you came up with during the brainstorming session. The presentation must focus on the novel aspects of the material design, behavior, and all related aspects.

Use a conversational tone and include any necessary context or explanations to make the content accessible to a general audience, but make it detailed, logical, and technical so that it has all necessary aspects for listeners to understand the material and its unexpected properties.

Remember, this describes a design developed by SciAgents, and this must be explicitly stated for the listeners.

Never use made-up names for the hosts and guests, but make it an engaging and immersive experience for listeners. Do not include any bracketed placeholders like [Host] or [Guest]. Design your output to be read aloud -- it will be directly converted into audio.

Make the dialogue as long and detailed as possible with great scientific depth, while still staying on topic and maintaining an engaging flow. Aim to use your full output capacity to create the longest podcast episode you can, while still communicating the key information from the input text in an entertaining way.

At the end of the dialogue, have the host and guest speakers naturally summarize the main insights and takeaways from their discussion. This should flow organically from the conversation, reiterating the key points in a casual, conversational manner. Avoid making it sound like an obvious recap - the goal is to reinforce the central ideas one last time before signing off.

The conversation should have around 20000 words.
"""
    },
################# LECTURE ##################
    "lecture": {
        "intro": """You are Professor Richard Feynman. Your task is to develop a script for a lecture. You never mention your name.

The material covered in the lecture is based on the provided text. 

Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points, identify definitions, and interesting facts that need to be covered in the lecture. 

Define all terms used carefully for a broad audience of students.
""",
        "text_instructions": "First, carefully read through the input text and identify the main topics, key points, and any interesting facts or anecdotes. Think about how you could present this information in a fun, engaging way that would be suitable for a high quality presentation.",
        "scratch_pad": """
Brainstorm creative ways to discuss the main topics and key points you identified in the input text. Consider using analogies, examples, storytelling techniques, or hypothetical scenarios to make the content more relatable and engaging for listeners.

Keep in mind that your lecture should be accessible to a general audience, so avoid using too much jargon or assuming prior knowledge of the topic. If necessary, think of ways to briefly explain any complex concepts in simple terms.

Use your imagination to fill in any gaps in the input text or to come up with thought-provoking questions that could be explored in the podcast. The goal is to create an informative and entertaining dialogue, so feel free to be creative in your approach.

Define all terms used clearly and spend effort to explain the background.

Write your brainstorming ideas and a rough outline for the lecture here. Be sure to note the key insights and takeaways you want to reiterate at the end.

Make sure to make it fun and exciting. 
""",
        "prelude": """Now that you have brainstormed ideas and created a rough outline, it's time to write the actual podcast dialogue. Aim for a natural, conversational flow between the host and any guest speakers. Incorporate the best ideas from your brainstorming session and make sure to explain any complex topics in an easy-to-understand way.
""",
        "dialog": """Write a very long, engaging, informative script here, based on the key points and creative ideas you came up with during the brainstorming session. Use a conversational tone and include any necessary context or explanations to make the content accessible to the students.

Include clear definitions and terms, and examples. 

Do not include any bracketed placeholders like [Host] or [Guest]. Design your output to be read aloud -- it will be directly converted into audio.

There is only one speaker, you, the professor. Stay on topic and maintaining an engaging flow. Aim to use your full output capacity to create the longest lecture you can, while still communicating the key information from the input text in an engaging way.

At the end of the lecture, naturally summarize the main insights and takeaways from the lecture. This should flow organically from the conversation, reiterating the key points in a casual, conversational manner. 

Avoid making it sound like an obvious recap - the goal is to reinforce the central ideas covered in this lecture one last time before class is over. 

The lecture should have around 20000 words.
""",
    },
################# SUMMARY ##################
        "summary": {
        "intro": """Your task is to develop a summary of a paper. You never mention your name.

Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points, identify definitions, and interesting facts that need to be summarized.

Define all terms used carefully for a broad audience.
""",
        "text_instructions": "First, carefully read through the input text and identify the main topics, key points, and key facts. Think about how you could present this information in an accurate summary.",
        "scratch_pad": """Brainstorm creative ways to present the main topics and key points you identified in the input text. Consider using analogies, examples, or hypothetical scenarios to make the content more relatable and engaging for listeners.

Keep in mind that your summary should be accessible to a general audience, so avoid using too much jargon or assuming prior knowledge of the topic. If necessary, think of ways to briefly explain any complex concepts in simple terms. Define all terms used clearly and spend effort to explain the background.

Write your brainstorming ideas and a rough outline for the summary here. Be sure to note the key insights and takeaways you want to reiterate at the end.

Make sure to make it engaging and exciting. 
""",
        "prelude": """Now that you have brainstormed ideas and created a rough outline, it is time to write the actual summary. Aim for a natural, conversational flow between the host and any guest speakers. Incorporate the best ideas from your brainstorming session and make sure to explain any complex topics in an easy-to-understand way.
""",
        "dialog": """Write a a script here, based on the key points and creative ideas you came up with during the brainstorming session. Use a conversational tone and include any necessary context or explanations to make the content accessible to the the audience.

Start your script by stating that this is a summary, referencing the title or headings in the input text. If the input text has no title, come up with a succinct summary of what is covered to open.

Include clear definitions and terms, and examples, of all key issues. 

Do not include any bracketed placeholders like [Host] or [Guest]. Design your output to be read aloud -- it will be directly converted into audio.

There is only one speaker, you. Stay on topic and maintaining an engaging flow. 

Naturally summarize the main insights and takeaways from the summary. This should flow organically from the conversation, reiterating the key points in a casual, conversational manner. 

The summary should have around 1024 words.
""",
    },
################# SHORT SUMMARY ##################
        "short summary": {
        "intro": """Your task is to develop a summary of a paper. You never mention your name.

Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points, identify definitions, and interesting facts that need to be summarized.

Define all terms used carefully for a broad audience.
""",
        "text_instructions": "First, carefully read through the input text and identify the main topics, key points, and key facts. Think about how you could present this information in an accurate summary.",
        "scratch_pad": """Brainstorm creative ways to present the main topics and key points you identified in the input text. Consider using analogies, examples, or hypothetical scenarios to make the content more relatable and engaging for listeners.

Keep in mind that your summary should be accessible to a general audience, so avoid using too much jargon or assuming prior knowledge of the topic. If necessary, think of ways to briefly explain any complex concepts in simple terms. Define all terms used clearly and spend effort to explain the background.

Write your brainstorming ideas and a rough outline for the summary here. Be sure to note the key insights and takeaways you want to reiterate at the end.

Make sure to make it engaging and exciting. 
""",
        "prelude": """Now that you have brainstormed ideas and created a rough outline, it is time to write the actual summary. Aim for a natural, conversational flow between the host and any guest speakers. Incorporate the best ideas from your brainstorming session and make sure to explain any complex topics in an easy-to-understand way.
""",
        "dialog": """Write a a script here, based on the key points and creative ideas you came up with during the brainstorming session. Keep it concise, and use a conversational tone and include any necessary context or explanations to make the content accessible to the the audience.

Start your script by stating that this is a summary, referencing the title or headings in the input text. If the input text has no title, come up with a succinct summary of what is covered to open.

Include clear definitions and terms, and examples, of all key issues. 

Do not include any bracketed placeholders like [Host] or [Guest]. Design your output to be read aloud -- it will be directly converted into audio.

There is only one speaker, you. Stay on topic and maintaining an engaging flow. 

Naturally summarize the main insights and takeaways from the short summary. This should flow organically from the conversation, reiterating the key points in a casual, conversational manner. 

The summary should have around 256 words.
""",
    },
}

# Function to update instruction fields based on template selection
def update_instructions(template):
    return (
        INSTRUCTION_TEMPLATES[template]["intro"],
        INSTRUCTION_TEMPLATES[template]["text_instructions"],
        INSTRUCTION_TEMPLATES[template]["scratch_pad"],
        INSTRUCTION_TEMPLATES[template]["prelude"],
        INSTRUCTION_TEMPLATES[template]["dialog"]
           )

import concurrent.futures as cf
import glob
import io
import os
import time
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import List, Literal

import gradio as gr

from loguru import logger
from openai import OpenAI
from promptic import llm
from pydantic import BaseModel, ValidationError
from pypdf import PdfReader
from tenacity import retry, retry_if_exception_type

# Define standard values
STANDARD_TEXT_MODELS = [
    "o1-preview-2024-09-12",
    "o1-preview",
    "gpt-4o-2024-08-06",
    "gpt-4o-mini",
    "o1-mini-2024-09-12",
    "o1-mini",
    "chatgpt-4o-latest",
    "gpt-4-turbo",
    "openai/custom_model",
]

STANDARD_AUDIO_MODELS = [
    "tts-1",
    "tts-1-hd",
]

STANDARD_VOICES = [
    "alloy",
    "echo",
    "fable",
    "onyx",
    "nova",
    "shimmer",
]

class DialogueItem(BaseModel):
    text: str
    speaker: Literal["speaker-1", "speaker-2"]

class Dialogue(BaseModel):
    scratchpad: str
    dialogue: List[DialogueItem]

def get_mp3(text: str, voice: str, audio_model: str, api_key: str = None) -> bytes:
    client = OpenAI(
        api_key=api_key or os.getenv("OPENAI_API_KEY"),
    )

    with client.audio.speech.with_streaming_response.create(
        model=audio_model,
        voice=voice,
        input=text,
    ) as response:
        with io.BytesIO() as file:
            for chunk in response.iter_bytes():
                file.write(chunk)
            return file.getvalue()


from functools import wraps

def conditional_llm(model, api_base=None, api_key=None):
    """
    Conditionally apply the @llm decorator based on the api_base parameter.
    If api_base is provided, it applies the @llm decorator with api_base.
    Otherwise, it applies the @llm decorator without api_base.
    """
    def decorator(func):
        if api_base:
            return llm(model=model, api_base=api_base)(func)
        else:
            return llm(model=model, api_key=api_key)(func)
    return decorator

def generate_audio(
    files: list,
    openai_api_key: str = None,
    text_model: str = "o1-preview-2024-09-12",
    audio_model: str = "tts-1",
    speaker_1_voice: str = "alloy",
    speaker_2_voice: str = "echo",
    api_base: str = None,
    intro_instructions: str = '',
    text_instructions: str = '',
    scratch_pad_instructions: str = '',
    prelude_dialog: str = '',
    podcast_dialog_instructions: str = '',
    edited_transcript: str = None,
    user_feedback: str = None,
    original_text: str = None,
    debug = False,
) -> tuple:
    # Validate API Key
    if not os.getenv("OPENAI_API_KEY") and not openai_api_key:
        raise gr.Error("OpenAI API key is required")

    combined_text = original_text or ""

    # If there's no original text, extract it from the uploaded files
    if not combined_text:
        for file in files:
            with Path(file).open("rb") as f:
                reader = PdfReader(f)
                text = "\n\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
                combined_text += text + "\n\n"

    # Configure the LLM based on selected model and api_base
    @retry(retry=retry_if_exception_type(ValidationError))
    @conditional_llm(model=text_model, api_base=api_base, api_key=openai_api_key)
    def generate_dialogue(text: str, intro_instructions: str, text_instructions: str, scratch_pad_instructions: str, 
                          prelude_dialog: str, podcast_dialog_instructions: str,
                          edited_transcript: str = None, user_feedback: str = None, ) -> Dialogue:
        """
        {intro_instructions}
        
        Here is the original input text:
        
        <input_text>
        {text}
        </input_text>

        {text_instructions}
        
        <scratchpad>
        {scratch_pad_instructions}
        </scratchpad>
        
        {prelude_dialog}
        
        <podcast_dialogue>
        {podcast_dialog_instructions}
        </podcast_dialogue>
        {edited_transcript}{user_feedback}
        """

    instruction_improve='Based on the original text, please generate an improved version of the dialogue by incorporating the edits, comments and feedback.'
    edited_transcript_processed="\nPreviously generated edited transcript, with specific edits and comments that I want you to carefully address:\n"+"<edited_transcript>\n"+edited_transcript+"</edited_transcript>" if edited_transcript !="" else ""
    user_feedback_processed="\nOverall user feedback:\n\n"+user_feedback if user_feedback !="" else ""

    if edited_transcript_processed.strip()!='' or user_feedback_processed.strip()!='':
        user_feedback_processed="<requested_improvements>"+user_feedback_processed+"\n\n"+instruction_improve+"</requested_improvements>" 
    
    if debug:
        logger.info (edited_transcript_processed)
        logger.info (user_feedback_processed)
    
    # Generate the dialogue using the LLM
    llm_output = generate_dialogue(
        combined_text,
        intro_instructions=intro_instructions,
        text_instructions=text_instructions,
        scratch_pad_instructions=scratch_pad_instructions,
        prelude_dialog=prelude_dialog,
        podcast_dialog_instructions=podcast_dialog_instructions,
        edited_transcript=edited_transcript_processed,
        user_feedback=user_feedback_processed
    )

    # Generate audio from the transcript
    audio = b""
    transcript = ""
    characters = 0

    with cf.ThreadPoolExecutor() as executor:
        futures = []
        for line in llm_output.dialogue:
            transcript_line = f"{line.speaker}: {line.text}"
            voice = speaker_1_voice if line.speaker == "speaker-1" else speaker_2_voice
            future = executor.submit(get_mp3, line.text, voice, audio_model, openai_api_key)
            futures.append((future, transcript_line))
            characters += len(line.text)

        for future, transcript_line in futures:
            audio_chunk = future.result()
            audio += audio_chunk
            transcript += transcript_line + "\n\n"

    logger.info(f"Generated {characters} characters of audio")

    temporary_directory = "./gradio_cached_examples/tmp/"
    os.makedirs(temporary_directory, exist_ok=True)

    # Use a temporary file -- Gradio's audio component doesn't work with raw bytes in Safari
    temporary_file = NamedTemporaryFile(
        dir=temporary_directory,
        delete=False,
        suffix=".mp3",
    )
    temporary_file.write(audio)
    temporary_file.close()

    # Delete any files in the temp directory that end with .mp3 and are over a day old
    for file in glob.glob(f"{temporary_directory}*.mp3"):
        if os.path.isfile(file) and time.time() - os.path.getmtime(file) > 24 * 60 * 60:
            os.remove(file)

    return temporary_file.name, transcript, combined_text

def validate_and_generate_audio(*args):
    files = args[0]
    if not files:
        return None, None, None, "Please upload at least one PDF file before generating audio."
    try:
        audio_file, transcript, original_text = generate_audio(*args)
        return audio_file, transcript, original_text, None  # Return None as the error when successful
    except Exception as e:
        # If an error occurs during generation, return None for the outputs and the error message
        return None, None, None, str(e)

def edit_and_regenerate(edited_transcript, user_feedback, *args):
    # Replace the original transcript and feedback in the args with the new ones
    #new_args = list(args)
    #new_args[-2] = edited_transcript  # Update edited transcript
    #new_args[-1] = user_feedback  # Update user feedback
    return validate_and_generate_audio(*new_args)

# New function to handle user feedback and regeneration
def process_feedback_and_regenerate(feedback, *args):
    # Add user feedback to the args
    new_args = list(args)
    new_args.append(feedback)  # Add user feedback as a new argument
    return validate_and_generate_audio(*new_args)

with gr.Blocks(title="PDF to Audio", css="""
    #header {
        display: flex;
        align-items: center;
        justify-content: space-between;
        padding: 20px;
        background-color: transparent;
        border-bottom: 1px solid #ddd;
    }
    #title {
        font-size: 24px;
        margin: 0;
    }
    #logo_container {
        width: 200px;
        height: 200px;
        display: flex;
        justify-content: center;
        align-items: center;
    }
    #logo_image {
        max-width: 100%;
        max-height: 100%;
        object-fit: contain;
    }
    #main_container {
        margin-top: 20px;
    }
""") as demo:
    
    with gr.Row(elem_id="header"):
        with gr.Column(scale=4):
            gr.Markdown("# Convert PDFs into an audio podcast, lecture, summary and others\n\nFirst, upload one or more PDFs, select options, then push Generate Audio.\n\nYou can also select a variety of custom option and direct the way the result is generated.", elem_id="title")
        with gr.Column(scale=1):
            gr.HTML('''
                <div id="logo_container">
                    <img src="https://huggingface.co/spaces/lamm-mit/PDF2Audio/resolve/main/logo.png" id="logo_image" alt="Logo">
                </div>
            ''')
    #gr.Markdown("")    
    submit_btn = gr.Button("Generate Audio", elem_id="submit_btn")

    with gr.Row(elem_id="main_container"):
        with gr.Column(scale=2):
            files = gr.Files(label="PDFs", file_types=["pdf"], )
            
            openai_api_key = gr.Textbox(
                label="OpenAI API Key",
                visible=True,  # Always show the API key field
                placeholder="Enter your OpenAI API Key here...",
                type="password"  # Hide the API key input
            )
            text_model = gr.Dropdown(
                label="Text Generation Model",
                choices=STANDARD_TEXT_MODELS,
                value="o1-preview-2024-09-12", #"gpt-4o-mini",
                info="Select the model to generate the dialogue text.",
            )
            audio_model = gr.Dropdown(
                label="Audio Generation Model",
                choices=STANDARD_AUDIO_MODELS,
                value="tts-1",
                info="Select the model to generate the audio.",
            )
            speaker_1_voice = gr.Dropdown(
                label="Speaker 1 Voice",
                choices=STANDARD_VOICES,
                value="alloy",
                info="Select the voice for Speaker 1.",
            )
            speaker_2_voice = gr.Dropdown(
                label="Speaker 2 Voice",
                choices=STANDARD_VOICES,
                value="echo",
                info="Select the voice for Speaker 2.",
            )
            api_base = gr.Textbox(
                label="Custom API Base",
                placeholder="Enter custom API base URL if using a custom/local model...",
                info="If you are using a custom or local model, provide the API base URL here, e.g.: http://localhost:8080/v1 for llama.cpp REST server.",
            )

        with gr.Column(scale=3):
            template_dropdown = gr.Dropdown(
                label="Instruction Template",
                choices=list(INSTRUCTION_TEMPLATES.keys()),
                value="podcast",
                info="Select the instruction template to use. You can also edit any of the fields for more tailored results.",
            )
            intro_instructions = gr.Textbox(
                label="Intro Instructions",
                lines=10,
                value=INSTRUCTION_TEMPLATES["podcast"]["intro"],
                info="Provide the introductory instructions for generating the dialogue.",
            )
            text_instructions = gr.Textbox(
                label="Standard Text Analysis Instructions",
                lines=10,
                placeholder="Enter text analysis instructions...",
                value=INSTRUCTION_TEMPLATES["podcast"]["text_instructions"],
                info="Provide the instructions for analyzing the raw data and text.",
            )
            scratch_pad_instructions = gr.Textbox(
                label="Scratch Pad Instructions",
                lines=15,
                value=INSTRUCTION_TEMPLATES["podcast"]["scratch_pad"],
                info="Provide the scratch pad instructions for brainstorming presentation/dialogue content.",
            )
            prelude_dialog = gr.Textbox(
                label="Prelude Dialog",
                lines=5,
                value=INSTRUCTION_TEMPLATES["podcast"]["prelude"],
                info="Provide the prelude instructions before the presentation/dialogue is developed.",
            )
            podcast_dialog_instructions = gr.Textbox(
                label="Podcast Dialog Instructions",
                lines=20,
                value=INSTRUCTION_TEMPLATES["podcast"]["dialog"],
                info="Provide the instructions for generating the presentation or podcast dialogue.",
            )

    audio_output = gr.Audio(label="Audio", format="mp3")
    transcript_output = gr.Textbox(label="Transcript", lines=20, show_copy_button=True)
    original_text_output = gr.Textbox(label="Original Text", lines=10, visible=False)
    error_output = gr.Textbox(visible=False)  # Hidden textbox to store error message

    use_edited_transcript = gr.Checkbox(label="Use Edited Transcript (check if you want to make edits to the initially generated transcript)", value=False)
    edited_transcript = gr.Textbox(label="Edit Transcript Here. E.g., mark edits in the text with clear instructions. E.g., '[ADD DEFINITION OF MATERIOMICS]'.", lines=20, visible=False,
                                   show_copy_button=True, interactive=False)

    user_feedback = gr.Textbox(label="Provide Feedback or Notes", lines=10, #placeholder="Enter your feedback or notes here..."
                              )
    regenerate_btn = gr.Button("Regenerate Audio with Edits and Feedback")
    # Function to update the interactive state of edited_transcript
    def update_edit_box(checkbox_value):
        return gr.update(interactive=checkbox_value, lines=20 if checkbox_value else 20, visible=True if checkbox_value else False)

    # Update the interactive state of edited_transcript when the checkbox is toggled
    use_edited_transcript.change(
        fn=update_edit_box,
        inputs=[use_edited_transcript],
        outputs=[edited_transcript]
    )
    # Update instruction fields when template is changed
    template_dropdown.change(
        fn=update_instructions,
        inputs=[template_dropdown],
        outputs=[intro_instructions, text_instructions, scratch_pad_instructions, prelude_dialog, podcast_dialog_instructions]
    )
    
    submit_btn.click(
        fn=validate_and_generate_audio,
        inputs=[
            files, openai_api_key, text_model, audio_model, 
            speaker_1_voice, speaker_2_voice, api_base,
            intro_instructions, text_instructions, scratch_pad_instructions, 
            prelude_dialog, podcast_dialog_instructions, 
            edited_transcript,  # placeholder for edited_transcript
            user_feedback,  # placeholder for user_feedback
        ],
        outputs=[audio_output, transcript_output, original_text_output, error_output]
    ).then(
        fn=lambda audio, transcript, original_text, error: (
            transcript if transcript else "",
            error if error else None
        ),
        inputs=[audio_output, transcript_output, original_text_output, error_output],
        outputs=[edited_transcript, error_output]
    ).then(
        fn=lambda error: gr.Warning(error) if error else None,
        inputs=[error_output],
        outputs=[]
    )

    regenerate_btn.click(
        fn=lambda use_edit, edit, *args: validate_and_generate_audio(
            *args[:12],  # All inputs up to podcast_dialog_instructions
            edit if use_edit else "",  # Use edited transcript if checkbox is checked, otherwise empty string
            *args[12:]  # user_feedback and original_text_output
        ),
        inputs=[
            use_edited_transcript, edited_transcript,
            files, openai_api_key, text_model, audio_model, 
            speaker_1_voice, speaker_2_voice, api_base,
            intro_instructions, text_instructions, scratch_pad_instructions, 
            prelude_dialog, podcast_dialog_instructions,
            user_feedback, original_text_output
        ],
        outputs=[audio_output, transcript_output, original_text_output, error_output]
    ).then(
        fn=lambda audio, transcript, original_text, error: (
            transcript if transcript else "",
            error if error else None
        ),
        inputs=[audio_output, transcript_output, original_text_output, error_output],
        outputs=[edited_transcript, error_output]
    ).then(
        fn=lambda error: gr.Warning(error) if error else None,
        inputs=[error_output],
        outputs=[]
    )

    # Add README content at the bottom
    gr.Markdown("---")  # Horizontal line to separate the interface from README
    gr.Markdown(read_readme())
    
# Enable queueing for better performance
demo.queue(max_size=20, default_concurrency_limit=32)

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()