Spaces:
Running
on
Zero
Running
on
Zero
Working streaming and cool looking app
Browse files- app.py +28 -23
- prompts.py +3 -5
app.py
CHANGED
@@ -7,12 +7,14 @@ import soundfile as sf
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import gradio as gr
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import numpy as np
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import time
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import torch
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from huggingface_hub import InferenceClient
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from kokoro import KModel, KPipeline
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# -----------------------------------------------------------------------------
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# Get podcast
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# -----------------------------------------------------------------------------
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from papers import PaperManager
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@@ -33,12 +35,12 @@ client = InferenceClient(
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)
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def
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"""Ask the LLM for a script of a podcast given by two hosts."""
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"""Here is the topic: it's the top trending paper on Hugging Face daily papers today. You will need to analyze it by bringing profound insights.
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{subject[:
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]
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if steering_question and len(steering_question) > 0:
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messages.append({"role": "user", "content": f"You could focus on this question: {steering_question}"})
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@@ -58,7 +60,7 @@ def generate_podcast_text(subject: str, steering_question: str | None = None) ->
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# -----------------------------------------------------------------------------
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CUDA_AVAILABLE = torch.cuda.is_available()
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kmodel = KModel().to("cuda" if CUDA_AVAILABLE else "cpu").eval()
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kpipeline = KPipeline(lang_code="a") # English voices
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MALE_VOICE = "am_michael" # [MIKE]
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@@ -68,14 +70,23 @@ FEMALE_VOICE = "af_heart" # [JANE]
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for v in (MALE_VOICE, FEMALE_VOICE):
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kpipeline.load_voice(v)
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# -----------------------------------------------------------------------------
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# Audio generation system with queue
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# -----------------------------------------------------------------------------
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@spaces.GPU
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def generate_podcast(
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pipeline = kpipeline
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pipeline_voice_female = pipeline.load_voice(FEMALE_VOICE)
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@@ -114,28 +125,22 @@ demo = gr.Interface(
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If you do not specify any source materials below, the podcast will be about the top trending [Daily paper](https://huggingface.co/papers/), '**{list(top_papers.keys())[0]}**'""",
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fn=generate_podcast,
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inputs=[
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gr.File(
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label="
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file_types=[".pdf"],
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file_count="single",
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),
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gr.Textbox(
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label="Optional - Type a URL to read its page",
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),
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gr.Textbox(label="Do you have a more specific topic or question on the materials?"),
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# gr.Dropdown(
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# label=UI_INPUTS["length"]["label"],
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# choices=UI_INPUTS["length"]["choices"],
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# value=UI_INPUTS["length"]["value"],
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# ),
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],
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outputs=[
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gr.Audio(
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label="Listen to your podcast",
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format="wav",
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streaming=True,
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),
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# gr.Markdown(label=UI_OUTPUTS["transcript"]["label"]),
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],
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theme=gr.themes.Soft(),
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submit_btn="Generate podcast ποΈ",
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import gradio as gr
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import numpy as np
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import time
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import pymupdf
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import requests
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import torch
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from huggingface_hub import InferenceClient
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from kokoro import KModel, KPipeline
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# -----------------------------------------------------------------------------
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# Get default podcast materials from Daily papers
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# -----------------------------------------------------------------------------
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from papers import PaperManager
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)
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def generate_podcast_script(subject: str, steering_question: str | None = None) -> str:
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"""Ask the LLM for a script of a podcast given by two hosts."""
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"""Here is the topic: it's the top trending paper on Hugging Face daily papers today. You will need to analyze it by bringing profound insights.
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{subject[:10000]}"""},
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]
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if steering_question and len(steering_question) > 0:
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messages.append({"role": "user", "content": f"You could focus on this question: {steering_question}"})
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# -----------------------------------------------------------------------------
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CUDA_AVAILABLE = torch.cuda.is_available()
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kmodel = KModel(repo_id='hexgrad/Kokoro-82M').to("cuda" if CUDA_AVAILABLE else "cpu").eval()
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kpipeline = KPipeline(lang_code="a") # English voices
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MALE_VOICE = "am_michael" # [MIKE]
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for v in (MALE_VOICE, FEMALE_VOICE):
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kpipeline.load_voice(v)
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@spaces.GPU
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def generate_podcast(url: str, pdf_path: str, topic: str):
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if pdf_path:
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with pymupdf.open(pdf_path) as pdf_doc:
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material_text = ""
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for page in pdf_doc:
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material_text += page.get_text()
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elif url:
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response = requests.get(f'https://r.jina.ai/{url}')
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material_text = response.text
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else:
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material_text = PODCAST_SUBJECT
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# Generate podcast script!
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podcast_script = generate_podcast_script(material_text, topic)
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lines = [l for l in podcast_script.strip().splitlines() if l.strip()]
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pipeline = kpipeline
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pipeline_voice_female = pipeline.load_voice(FEMALE_VOICE)
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If you do not specify any source materials below, the podcast will be about the top trending [Daily paper](https://huggingface.co/papers/), '**{list(top_papers.keys())[0]}**'""",
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fn=generate_podcast,
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inputs=[
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gr.Textbox(
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label="π Type a Webpage URL to discuss this page (Optional)",
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),
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gr.File(
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label="π Upload a PDF to use it as discussion material (Optional)",
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file_types=[".pdf"],
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file_count="single",
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),
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gr.Textbox(label="π€ Do you have a more specific topic or question on the materials?", placeholder="You can leave this blank."),
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],
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outputs=[
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gr.Audio(
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label="Listen to your podcast π",
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format="wav",
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streaming=True,
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),
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],
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theme=gr.themes.Soft(),
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submit_btn="Generate podcast ποΈ",
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prompts.py
CHANGED
@@ -46,10 +46,8 @@ Ensure the dialogue has a natural ebb and flow:
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IMPORTANT RULE: Each line of dialogue should go in a new line [JANE] or [MIKE], as follows:
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[JANE] Hello, how are you?
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[MIKE] I'm good
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[JANE] I'm good, thank you.
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[MIKE] Great.
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Remember: Each
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"""
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IMPORTANT RULE: Each line of dialogue should go in a new line [JANE] or [MIKE], as follows:
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[JANE] Hello Mike, how are you?
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[MIKE] Nice to see you again, Jane. I'm very good. Today's topic is fascinating, because...
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Remember: Each turn from a host should be on the same line.
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"""
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