tahirsher commited on
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fe3ef3e
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1 Parent(s): 16dd26a

Update app.py

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  1. app.py +49 -49
app.py CHANGED
@@ -1,64 +1,64 @@
 
 
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  import gradio as gr
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- from huggingface_hub import InferenceClient
 
 
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- """
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- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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- """
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- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
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- def respond(
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- message,
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- history: list[tuple[str, str]],
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- system_message,
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- max_tokens,
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- temperature,
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- top_p,
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- ):
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- messages = [{"role": "system", "content": system_message}]
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- for val in history:
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- if val[0]:
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- messages.append({"role": "user", "content": val[0]})
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- if val[1]:
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- messages.append({"role": "assistant", "content": val[1]})
 
 
 
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- messages.append({"role": "user", "content": message})
 
 
 
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- response = ""
 
 
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- for message in client.chat_completion(
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- messages,
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- max_tokens=max_tokens,
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- stream=True,
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- temperature=temperature,
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- top_p=top_p,
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- ):
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- token = message.choices[0].delta.content
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- response += token
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- yield response
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- """
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- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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- """
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- demo = gr.ChatInterface(
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- respond,
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- additional_inputs=[
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- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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- gr.Slider(
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- minimum=0.1,
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- maximum=1.0,
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- value=0.95,
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- step=0.05,
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- label="Top-p (nucleus sampling)",
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- ),
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  ],
 
 
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  )
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- if __name__ == "__main__":
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- demo.launch()
 
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+ import whisper
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+ import openai
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  import gradio as gr
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+ from gtts import gTTS
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+ from moviepy.editor import VideoFileClip
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+ import os
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+ def transcribe_video(video_path):
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+ # Extract audio from video file
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+ video = VideoFileClip(video_path)
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+ audio_path = "temp_audio.wav"
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+ video.audio.write_audiofile(audio_path, codec='pcm_s16le')
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+ # Load Whisper model and transcribe audio
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+ model = whisper.load_model("base")
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+ result = model.transcribe(audio_path)
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+ transcription = result["text"]
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+ # Remove temporary audio file
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+ os.remove(audio_path)
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+ return transcription
 
 
 
 
 
 
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+ def summarize_text(text):
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+ response = openai.Completion.create(
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+ engine="text-davinci-003",
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+ prompt=f"Summarize the following text:\n\n{text}",
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+ max_tokens=150
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+ )
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+ summary = response.choices[0].text.strip()
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+ return summary
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+ def text_to_speech(text, language="en"):
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+ tts = gTTS(text=text, lang=language)
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+ tts.save("summary_audio.mp3")
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+ return "summary_audio.mp3"
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+ def process_video(video):
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+ # Transcribe the video
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+ transcription = transcribe_video(video)
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+ # Summarize the transcription
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+ summary = summarize_text(transcription)
 
 
 
 
 
 
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+ # Convert summary to speech
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+ audio_file = text_to_speech(summary)
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+ return transcription, summary, audio_file
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+ # Create Gradio interface
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+ iface = gr.Interface(
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+ fn=process_video,
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+ inputs=gr.Video(label="Upload Video"),
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+ outputs=[
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+ gr.Textbox(label="Transcription"),
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+ gr.Textbox(label="Summary"),
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+ gr.Audio(label="Summary Audio")
 
 
 
 
 
 
 
 
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  ],
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+ title="Video Transcription and Summarization",
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+ description="Upload a video file to transcribe and summarize its content."
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  )
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+ # Launch the interface
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+ iface.launch()
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