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Update app.py
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app.py
CHANGED
@@ -1,6 +1,6 @@
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import gradio as gr
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from langchain.prompts import PromptTemplate
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from
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from transformers import pipeline
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from bs4 import BeautifulSoup
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import requests
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@@ -21,11 +21,12 @@ Summarize the following article content in a clear, warm, and motivational tone
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Summary:
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""")
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# Updated chaining method
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summary_chain = summary_prompt | llm
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# TTS model setup
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tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
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def extract_main_content(url):
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try:
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@@ -39,13 +40,13 @@ def extract_main_content(url):
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except Exception as e:
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return f"Error extracting article content: {str(e)}"
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def generate_human_like_audio(text):
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try:
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temp_dir = tempfile.mkdtemp()
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wav_path = os.path.join(temp_dir, "summary.wav")
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mp3_path = os.path.join(temp_dir, "summary.mp3")
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tts_model.tts_to_file(text=text, file_path=wav_path)
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os.system(f"ffmpeg -y -i {wav_path} -codec:a libmp3lame -qscale:a 4 {mp3_path}")
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@@ -57,7 +58,7 @@ def generate_human_like_audio(text):
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print(f"TTS ERROR: {e}")
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return None, None
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def url_to_audio_summary(url):
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try:
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article_text = extract_main_content(url)
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if article_text.startswith("Error"):
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@@ -69,13 +70,13 @@ def url_to_audio_summary(url):
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summary = summary_chain.invoke({"text": article_text})
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summary = summary["text"] if isinstance(summary, dict) and "text" in summary else summary
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wav_path, mp3_path = generate_human_like_audio(summary)
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return summary, wav_path, mp3_path
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except Exception as e:
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return f"Error: {str(e)}", None, None
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def interface_wrapper(url):
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summary, wav_path, mp3_path = url_to_audio_summary(url)
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download_html = ""
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if mp3_path and os.path.exists(mp3_path):
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download_html = f'<a href="file/{os.path.basename(mp3_path)}" download target="_blank">Click to download MP3</a>'
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@@ -83,7 +84,10 @@ def interface_wrapper(url):
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iface = gr.Interface(
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fn=interface_wrapper,
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inputs=
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outputs=[
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gr.Textbox(label="Summary"),
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gr.Audio(label="Preacher-style Audio Summary", type="filepath"),
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import gradio as gr
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from langchain.prompts import PromptTemplate
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from langchain_huggingface import HuggingFacePipeline
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from transformers import pipeline
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from bs4 import BeautifulSoup
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import requests
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Summary:
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""")
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summary_chain = summary_prompt | llm
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# TTS model setup
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tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
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SPEAKER_LIST = tts_model.speakers
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DEFAULT_SPEAKER = SPEAKER_LIST[0] if SPEAKER_LIST else None
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def extract_main_content(url):
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try:
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except Exception as e:
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return f"Error extracting article content: {str(e)}"
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def generate_human_like_audio(text, speaker):
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try:
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temp_dir = tempfile.mkdtemp()
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wav_path = os.path.join(temp_dir, "summary.wav")
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mp3_path = os.path.join(temp_dir, "summary.mp3")
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tts_model.tts_to_file(text=text, speaker=speaker, file_path=wav_path)
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os.system(f"ffmpeg -y -i {wav_path} -codec:a libmp3lame -qscale:a 4 {mp3_path}")
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print(f"TTS ERROR: {e}")
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return None, None
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def url_to_audio_summary(url, speaker):
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try:
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article_text = extract_main_content(url)
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if article_text.startswith("Error"):
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summary = summary_chain.invoke({"text": article_text})
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summary = summary["text"] if isinstance(summary, dict) and "text" in summary else summary
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wav_path, mp3_path = generate_human_like_audio(summary, speaker)
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return summary, wav_path, mp3_path
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except Exception as e:
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return f"Error: {str(e)}", None, None
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def interface_wrapper(url, speaker):
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summary, wav_path, mp3_path = url_to_audio_summary(url, speaker)
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download_html = ""
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if mp3_path and os.path.exists(mp3_path):
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download_html = f'<a href="file/{os.path.basename(mp3_path)}" download target="_blank">Click to download MP3</a>'
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iface = gr.Interface(
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fn=interface_wrapper,
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inputs=[
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gr.Textbox(label="Article URL", placeholder="Paste a news/blog URL here..."),
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gr.Dropdown(choices=SPEAKER_LIST, value=DEFAULT_SPEAKER, label="Select Speaker")
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],
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outputs=[
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gr.Textbox(label="Summary"),
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gr.Audio(label="Preacher-style Audio Summary", type="filepath"),
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