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First stab at custom summarization prompts

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Robert
2024-05-10 16:11:18 -07:00
parent 03106dd0fc
commit 0dbd739eca
2 changed files with 83 additions and 66 deletions
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64
HF/app.py
View File

@@ -45,23 +45,22 @@ import yt_dlp
#
#
# Usage:
# Transcribe a single URL:
# python diarize.py https://example.com/video.mp4
#
# Transcribe a single URL and have the resulting transcription summarized:
# python diarize.py https://example.com/video.mp4
# Download Audio only from URL -> Transcribe audio:
# python summarize.py https://www.youtube.com/watch?v=4nd1CDZP21s`
#
# Transcribe a list of files:
# python diarize.py ./path/to/your/text_file.txt
# Download Audio+Video from URL -> Transcribe audio from Video:**
# python summarize.py -v https://www.youtube.com/watch?v=4nd1CDZP21s`
#
# Transcribe a local file:
# python diarize.py /path/to/your/localfile.mp4
#
# Transcribe a local file and have it summarized:
# python diarize.py ./input.mp4 --api_name openai --api_key <your_openai_api_key>
#
# Transcribe a list of files and have them all summarized:
# python diarize.py path_to_your_text_file.txt --api_name <openai> --api_key <your_openai_api_key>
# Download Audio only from URL -> Transcribe audio -> Summarize using (`anthropic`/`cohere`/`openai`/`llama` (llama.cpp)/`ooba` (oobabooga/text-gen-webui)/`kobold` (kobold.cpp)/`tabby` (Tabbyapi)) API:**
# python summarize.py -v https://www.youtube.com/watch?v=4nd1CDZP21s -api <your choice of API>` - Make sure to put your API key into `config.txt` under the appropriate API variable
#
# Download Audio+Video from a list of videos in a text file (can be file paths or URLs) and have them all summarized:**
# python summarize.py ./local/file_on_your/system --api_name <API_name>`
#
# Run it as a WebApp**
# python summarize.py -gui` - This requires you to either stuff your API keys into the `config.txt` file, or pass them into the app every time you want to use it.
# Can be helpful for setting up a shared instance, but not wanting people to perform inference on your server.
#
###
@@ -350,7 +349,7 @@ def process_local_file(file_path):
# Video Download/Handling
#
def process_url(input_path, num_speakers=2, whisper_model="small.en", offset=0, api_name=None, api_key=None, vad_filter=False, download_video_flag=False, demo_mode=True):
def process_url(input_path, num_speakers=2, whisper_model="small.en", offset=0, api_name=None, api_key=None, vad_filter=False, download_video_flag=False,custom_prompt=None, demo_mode=True):
if demo_mode:
api_name = "huggingface"
api_key = os.environ.get(HF_TOKEN)
@@ -793,7 +792,7 @@ def summarize_with_openai(api_key, file_path, model):
}
logging.debug("openai: Preparing data + prompt for submittal")
prompt_text = f"{text} \n\n\n\nPlease provide a detailed, bulleted list of the points made throughout the transcribed video and any supporting arguments made for said points"
openai_prompt = f"{text} \n\n\n\n{prompt_text}"
data = {
"model": model,
"messages": [
@@ -803,7 +802,7 @@ def summarize_with_openai(api_key, file_path, model):
},
{
"role": "user",
"content": prompt_text
"content": openai_prompt
}
],
"max_tokens": 4096, # Adjust tokens as needed
@@ -846,7 +845,7 @@ def summarize_with_claude(api_key, file_path, model):
logging.debug("anthropic: Prepping data + prompt for submittal")
user_message = {
"role": "user",
"content": f"{text} \n\n\n\nPlease provide a detailed, bulleted list of the points made throughout the transcribed video and any supporting arguments made for said points"
"content": f"{text} \n\n\n\n{prompt_text}"
}
data = {
@@ -913,10 +912,10 @@ def summarize_with_cohere(api_key, file_path, model):
'Authorization': f'Bearer {api_key}'
}
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
cohere_prompt = f"{text} \n\n\n\n{prompt_text}"
data = {
"chat_history": [
{"role": "USER", "message": prompt_text}
{"role": "USER", "message": cohere_prompt}
],
"message": "Please provide a summary.",
"model": model,
@@ -964,12 +963,12 @@ def summarize_with_groq(api_key, file_path, model):
'Content-Type': 'application/json'
}
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
groq_prompt = f"{text} \n\n\n\n{prompt_text}"
data = {
"messages": [
{
"role": "user",
"content": prompt_text
"content": groq_prompt
}
],
"model": model
@@ -1021,12 +1020,13 @@ def summarize_with_llama(api_url, file_path, token):
headers['Authorization'] = f'Bearer {token}'
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
llama_prompt = f"{text} \n\n\n\n{prompt_text}"
logging.debug(f"llama: Complete prompt is: {llama_prompt}")
data = {
"prompt": prompt_text
"prompt": llama_prompt
}
logging.debug("llama: Submitting request to API endpoint")
#logging.debug(f"llama: Submitting request to API endpoint {llama_prompt}")
print("llama: Submitting request to API endpoint")
response = requests.post(api_url, headers=headers, json=data)
response_data = response.json()
@@ -1064,13 +1064,13 @@ def summarize_with_kobold(api_url, file_path):
'content-type': 'application/json',
}
# FIXME
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the above text."
logging.debug(prompt_text)
kobold_prompt = f"{text} \n\n\n\n{prompt_text}"
logging.debug(kobold_prompt)
# Values literally c/p from the api docs....
data = {
"max_context_length": 8096,
"max_length": 4096,
"prompt": prompt_text,
"prompt": kobold_prompt,
}
logging.debug("kobold: Submitting request to API endpoint")
@@ -1114,9 +1114,9 @@ def summarize_with_oobabooga(api_url, file_path):
'content-type': 'application/json',
}
prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a french bakery baking cakes. It is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are my favorite."
# prompt_text += f"\n\n{text}" # Uncomment this line if you want to include the text variable
prompt_text += "\n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
#prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a french bakery baking cakes. It is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are my favorite."
#prompt_text += f"\n\n{text}" # Uncomment this line if you want to include the text variable
ooba_prompt = f"{text}\n\n\n\n{prompt_text}"
data = {
"mode": "chat",
@@ -1268,6 +1268,7 @@ def launch_ui(demo_mode=False):
gr.components.Textbox(label="URL of video to be Transcribed/Summarized"),
gr.components.Number(value=2, label="Number of Speakers (for Diarization)"),
gr.components.Dropdown(choices=whisper_models, value="small.en", label="Whisper Model (Can ignore this)"),
gr.components.Textbox(label="Custom Prompt", value="Please provide a detailed, bulleted list of the points made throughout the transcribed video and any supporting arguments made for said points", lines=3),
gr.components.Number(value=0, label="Offset time to start transcribing from\n\n (helpful if you only want part of a video/lecture)")
]
@@ -1316,6 +1317,7 @@ def launch_ui(demo_mode=False):
####################################################################################################################################
# Main()
#
def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model="small.en", offset=0, vad_filter=False, download_video_flag=False, demo_mode=False):
if input_path is None and args.user_interface:
return []

85
summarize.py
View File

@@ -36,23 +36,22 @@ import yt_dlp
#
#
# Usage:
# Transcribe a single URL:
# python diarize.py https://example.com/video.mp4
#
# Transcribe a single URL and have the resulting transcription summarized:
# python diarize.py https://example.com/video.mp4
# Download Audio only from URL -> Transcribe audio:
# python summarize.py https://www.youtube.com/watch?v=4nd1CDZP21s`
#
# Transcribe a list of files:
# python diarize.py ./path/to/your/text_file.txt
# Download Audio+Video from URL -> Transcribe audio from Video:**
# python summarize.py -v https://www.youtube.com/watch?v=4nd1CDZP21s`
#
# Transcribe a local file:
# python diarize.py /path/to/your/localfile.mp4
#
# Transcribe a local file and have it summarized:
# python diarize.py ./input.mp4 --api_name openai --api_key <your_openai_api_key>
#
# Transcribe a list of files and have them all summarized:
# python diarize.py path_to_your_text_file.txt --api_name <openai> --api_key <your_openai_api_key>
# Download Audio only from URL -> Transcribe audio -> Summarize using (`anthropic`/`cohere`/`openai`/`llama` (llama.cpp)/`ooba` (oobabooga/text-gen-webui)/`kobold` (kobold.cpp)/`tabby` (Tabbyapi)) API:**
# python summarize.py -v https://www.youtube.com/watch?v=4nd1CDZP21s -api <your choice of API>` - Make sure to put your API key into `config.txt` under the appropriate API variable
#
# Download Audio+Video from a list of videos in a text file (can be file paths or URLs) and have them all summarized:**
# python summarize.py ./local/file_on_your/system --api_name <API_name>`
#
# Run it as a WebApp**
# python summarize.py -gui` - This requires you to either stuff your API keys into the `config.txt` file, or pass them into the app every time you want to use it.
# Can be helpful for setting up a shared instance, but not wanting people to perform inference on your server.
#
###
@@ -341,7 +340,7 @@ def process_local_file(file_path):
# Video Download/Handling
#
def process_url(input_path, num_speakers=2, whisper_model="small.en", offset=0, api_name=None, api_key=None, vad_filter=False, download_video_flag=False, demo_mode=False):
def process_url(input_path, num_speakers=2, whisper_model="small.en", custom_prompt=None, offset=0, api_name=None, api_key=None, vad_filter=False, download_video_flag=False, demo_mode=False):
if demo_mode:
# api_name = "<demo_mode_api>"
# api_key = "<demo_mode_key>"
@@ -782,7 +781,7 @@ def summarize_with_openai(api_key, file_path, model):
}
logging.debug("openai: Preparing data + prompt for submittal")
prompt_text = f"{text} \n\n\n\nPlease provide a detailed, bulleted list of the points made throughout the transcribed video and any supporting arguments made for said points"
openai_prompt = f"{text} \n\n\n\n{custom_prompt}"
data = {
"model": model,
"messages": [
@@ -792,7 +791,7 @@ def summarize_with_openai(api_key, file_path, model):
},
{
"role": "user",
"content": prompt_text
"content": openai_prompt
}
],
"max_tokens": 4096, # Adjust tokens as needed
@@ -831,11 +830,12 @@ def summarize_with_claude(api_key, file_path, model):
'anthropic-version': '2023-06-01',
'Content-Type': 'application/json'
}
logging.debug("anthropic: Prepping data + prompt for submittal")
anthropic_prompt = custom_prompt
logging.debug("anthropic: Prompt is {anthropic_prompt}")
user_message = {
"role": "user",
"content": f"{text} \n\n\n\nPlease provide a detailed, bulleted list of the points made throughout the transcribed video and any supporting arguments made for said points"
"content": f"{text} \n\n\n\n{anthropic_prompt}"
}
data = {
@@ -902,10 +902,12 @@ def summarize_with_cohere(api_key, file_path, model):
'Authorization': f'Bearer {api_key}'
}
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
cohere_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("cohere: Prompt being sent is {cohere_prompt}")
data = {
"chat_history": [
{"role": "USER", "message": prompt_text}
{"role": "USER", "message": cohere_prompt}
],
"message": "Please provide a summary.",
"model": model,
@@ -953,12 +955,14 @@ def summarize_with_groq(api_key, file_path, model):
'Content-Type': 'application/json'
}
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
groq_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("groq: Prompt being sent is {groq_prompt}")
data = {
"messages": [
{
"role": "user",
"content": prompt_text
"content": groq_prompt
}
],
"model": model
@@ -1010,9 +1014,11 @@ def summarize_with_llama(api_url, file_path, token):
headers['Authorization'] = f'Bearer {token}'
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
llama_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("llama: Prompt being sent is {llama_prompt}")
data = {
"prompt": prompt_text
"prompt": llama_prompt
}
logging.debug("llama: Submitting request to API endpoint")
@@ -1052,14 +1058,16 @@ def summarize_with_kobold(api_url, file_path):
'accept': 'application/json',
'content-type': 'application/json',
}
kobold_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("kobold: Prompt being sent is {kobold_prompt}")
# FIXME
prompt_text = f"{text} \n\nAs a professional summarizer, create a concise and comprehensive summary of the above text."
logging.debug(prompt_text)
# Values literally c/p from the api docs....
data = {
"max_context_length": 8096,
"max_length": 4096,
"prompt": prompt_text,
"prompt": kobold_prompt,
}
logging.debug("kobold: Submitting request to API endpoint")
@@ -1103,14 +1111,15 @@ def summarize_with_oobabooga(api_url, file_path):
'content-type': 'application/json',
}
prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a french bakery baking cakes. It is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are my favorite."
# prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a french bakery baking cakes. It is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are my favorite."
# prompt_text += f"\n\n{text}" # Uncomment this line if you want to include the text variable
prompt_text += "\n\nAs a professional summarizer, create a concise and comprehensive summary of the provided text."
ooba_prompt = "{text}\n\n\n\n{custom_prompt}"
logging.debug("ooba: Prompt being sent is {ooba_prompt}")
data = {
"mode": "chat",
"character": "Example",
"messages": [{"role": "user", "content": prompt_text}]
"messages": [{"role": "user", "content": ooba_prompt}]
}
logging.debug("ooba: Submitting request to API endpoint")
@@ -1166,7 +1175,6 @@ def summarize_with_huggingface(api_key, file_path):
logging.debug(f"huggingface: Segments: {segments}")
text = ' '.join([segment['text'] for segment in segments])
# API KEY ASSIGNMENT HERE
api_key = huggingface_api_key
print(f"huggingface: lets make sure the HF api key exists...\n\t {huggingface_api_key}" )
@@ -1176,6 +1184,10 @@ def summarize_with_huggingface(api_key, file_path):
model = "microsoft/Phi-3-mini-128k-instruct"
API_URL = f"https://api-inference.huggingface.co/models/{model}"
huggingface_prompt = "{text}\n\n\n\n{custom_prompt}"
logging.debug("huggingface: Prompt being sent is {huggingface_prompt}")
data = {
"inputs": text,
"parameters": {"max_length": 512, "min_length": 100} # You can adjust max_length and min_length as needed
@@ -1184,6 +1196,7 @@ def summarize_with_huggingface(api_key, file_path):
print(f"huggingface: lets make sure the HF api key is the same..\n\t {huggingface_api_key}")
logging.debug("huggingface: Submitting request...")
response = requests.post(API_URL, headers=headers, json=data)
if response.status_code == 200:
@@ -1217,6 +1230,7 @@ def launch_ui(demo_mode=False):
gr.components.Textbox(label="URL"),
gr.components.Number(value=2, label="Number of Speakers"),
gr.components.Dropdown(choices=whisper_models, value="small.en", label="Whisper Model"),
gr.components.Textbox(label="Custom Prompt", value="Please provide a detailed, bulleted list of the points made throughout the transcribed video and any supporting arguments made for said points", lines=3),
gr.components.Number(value=0, label="Offset")
]
@@ -1245,7 +1259,8 @@ def launch_ui(demo_mode=False):
theme="bethecloud/storj_theme"
)
iface.launch(share=True)
#iface.launch(share=True)
iface.launch(share=False)
#
#
@@ -1260,7 +1275,7 @@ def launch_ui(demo_mode=False):
####################################################################################################################################
# Main()
#
def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model="small.en", offset=0, vad_filter=False, download_video_flag=False):
def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model="small.en", offset=0, vad_filter=False, download_video_flag=False, custom_prompt=None):
if input_path is None and args.user_interface:
return []
start_time = time.monotonic()