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HF supported and Demo mode working
Gradio GUI works, API selection + API key input works in Gradio GUI for non-demo, For Demo, have to hardcode the values. (try to hide the complexity)
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Robert
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@@ -3,7 +3,7 @@ anthropic_api_key = <anthropic_api_key
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anthropic_model = claude-3-sonnet-20240229
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cohere_api_key = <your_cohere_api_key>
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cohere_model = command-r-plus
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groq_api_key = idk
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groq_api_key = <your_groq_api_key>
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groq_model = llama3-70b-8192
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openai_api_key = <openai_api_key>
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openai_model = gpt-4-turbo
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82
summarize.py
82
summarize.py
@@ -70,7 +70,7 @@ anthropic_api_key = config.get('API', 'anthropic_api_key', fallback=None)
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cohere_api_key = config.get('API', 'cohere_api_key', fallback=None)
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groq_api_key = config.get('API', 'groq_api_key', fallback=None)
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openai_api_key = config.get('API', 'openai_api_key', fallback=None)
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huggingface_api_token = config.get('API', 'huhuggingface_api_token', fallback=None)
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huggingface_api_key = config.get('API', 'huggingface_api_key', fallback=None)
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# Models
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anthropic_model = config.get('API', 'anthropic_model', fallback='claude-3-sonnet-20240229')
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@@ -331,30 +331,16 @@ def process_local_file(file_path):
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# Video Download/Handling
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#
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a = """def process_url(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model="small.en", offset=0, vad_filter=False, download_video_flag=False):
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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):
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if demo_mode:
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# api_name = "<demo_mode_api>"
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# api_key = "<demo_mode_key>"
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vad_filter = False
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download_video_flag = False
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try:
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results = main(input_path, api_name=api_name, api_key=api_key, num_speakers=num_speakers, whisper_model=whisper_model, offset=offset, vad_filter=vad_filter, download_video_flag=download_video_flag)
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if results:
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transcription_result = results[0]
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json_file_path = transcription_result['audio_file'].replace('.wav', '.segments.json')
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with open(json_file_path, 'r') as file:
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json_data = json.load(file)
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summary = transcription_result.get('summary', '')
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return json_data, summary, json_file_path, json_file_path.replace('.segments.json', '_summary.txt')
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else:
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return None, "No results found.", None, None
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except Exception as e:
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error_message = f"An error occurred: {str(e)}"
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return None, error_message, None, None
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"""
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def process_url(input_path, num_speakers=2, whisper_model="small.en", offset=0):
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try:
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results = main(input_path, num_speakers=num_speakers, whisper_model=whisper_model, offset=offset)
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if results:
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transcription_result = results[0]
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json_file_path = transcription_result['audio_file'].replace('.wav', '.segments.json')
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@@ -1157,35 +1143,40 @@ def save_summary_to_file(summary, file_path):
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# Only to be used when configured with Gradio for HF Space
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def summarize_with_huggingface(api_key, file_path):
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logging.debug(f"huggingface: Summarization process starting...")
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try:
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logging.debug("huggingface: Loading json data for summarization")
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with open(file_path, 'r') as file:
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segments = json.load(file)
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logging.debug("huggingface: Extracting text from the segments")
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text = extract_text_from_segments(segments)
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text = ' '.join([segment['text'] for segment in segments])
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API_TOKEN = huggingface_api_token
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headers = {"Authorization": f"Bearer {API_TOKEN}"}
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logging.debug("huggingface: Creating query...")
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data = query("Can you please let us know more details about your ")
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API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}"
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api_key = os.environ.get('HF_TOKEN')
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headers = {
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"Authorization": f"Bearer {api_key}"
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}
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model = "microsoft/Phi-3-mini-128k-instruct"
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API_URL = f"https://api-inference.huggingface.co/models/{model}"
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data = {
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"inputs": text,
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"parameters": {"max_length": 512, "min_length": 100} # You can adjust max_length and min_length as needed
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}
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logging.debug("huggingface: Submitting request...")
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response = requests.post(API_URL, headers=headers, json=data)
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if response.status_code == 200:
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summary = response.json()['choices'][0]['message']['content'].strip()
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summary = response.json()[0]['summary_text']
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logging.debug("huggingface: Summarization successful")
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print("Summarization successful.")
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return summary
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else:
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logging.debug("huggingface: Summarization failed")
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print("Failed to process summary:", response.text)
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return None
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logging.error(f"huggingface: Summarization failed with status code {response.status_code}: {response.text}")
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return f"Failed to process summary, status code {response.status_code}: {response.text}"
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except Exception as e:
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logging.debug("huggingface: Error in processing: %s", str(e))
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print("Error occurred while processing summary with huggingface:", str(e))
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logging.error("huggingface: Error in processing: %s", str(e))
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print(f"Error occurred while processing summary with huggingface: {str(e)}")
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return None
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@@ -1218,7 +1209,7 @@ def launch_ui(demo_mode=False):
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])
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iface = gr.Interface(
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fn=process_url,
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fn=lambda *args: process_url(*args, demo_mode=demo_mode),
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inputs=inputs,
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outputs=[
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gr.components.Textbox(label="Transcription", value=lambda: "", max_lines=10),
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@@ -1306,24 +1297,28 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
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if api_name.lower() == 'openai':
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api_key = openai_api_key
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try:
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logging.debug(f"MAIN: trying to summarize with openAI")
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summary = summarize_with_openai(api_key, json_file_path, openai_model)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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elif api_name.lower() == 'anthropic':
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api_key = anthropic_api_key
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try:
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logging.debug(f"MAIN: Trying to summarize with anthropic")
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summary = summarize_with_claude(api_key, json_file_path, anthropic_model)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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elif api_name.lower() == 'cohere':
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api_key = cohere_api_key
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try:
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logging.debug(f"MAIN: Trying to summarize with cohere")
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summary = summarize_with_cohere(api_key, json_file_path, cohere_model)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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elif api_name.lower() == 'groq':
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api_key = groq_api_key
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try:
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logging.debug(f"MAIN: Trying to summarize with Groq")
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summary = summarize_with_groq(api_key, json_file_path, groq_model)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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@@ -1331,6 +1326,7 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
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token = llama_api_key
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llama_ip = llama_api_IP
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try:
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logging.debug(f"MAIN: Trying to summarize with Llama.cpp")
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summary = summarize_with_llama(llama_ip, json_file_path, token)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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@@ -1338,6 +1334,7 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
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token = kobold_api_key
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kobold_ip = kobold_api_IP
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try:
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logging.debug(f"MAIN: Trying to summarize with kobold.cpp")
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summary = summarize_with_kobold(kobold_ip, json_file_path)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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@@ -1345,9 +1342,18 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
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token = ooba_api_key
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ooba_ip = ooba_api_IP
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try:
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logging.debug(f"MAIN: Trying to summarize with oobabooga")
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summary = summarize_with_oobabooga(ooba_ip, json_file_path)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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if api_name.lower() == 'huggingface':
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api_key = huggingface_api_key
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try:
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logging.debug(f"MAIN: Trying to summarize with huggingface")
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summarize_with_huggingface(api_key, json_file_path)
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except requests.exceptions.ConnectionError:
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r.status_code = "Connection: "
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else:
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logging.warning(f"Unsupported API: {api_name}")
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summary = None
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@@ -1374,7 +1380,7 @@ if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Transcribe and summarize videos.')
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parser.add_argument('input_path', type=str, help='Path or URL of the video', nargs='?')
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parser.add_argument('-v','--video', action='store_true', help='Download the video instead of just the audio')
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parser.add_argument('-name', '--api_name', type=str, help='API name for summarization (optional)')
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parser.add_argument('-api', '--api_name', type=str, help='API name for summarization (optional)')
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parser.add_argument('-key', '--api_key', type=str, help='API key for summarization (optional)')
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parser.add_argument('-ns', '--num_speakers', type=int, default=2, help='Number of speakers (default: 2)')
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parser.add_argument('-wm', '--whisper_model', type=str, default='small.en', help='Whisper model (default: small.en)')
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