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Update app.py
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app.py
CHANGED
@@ -4,24 +4,24 @@ import soundfile as sf
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from transformers import pipeline
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import torch
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# Initialize the client for the text generation model
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client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
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# Initialize the TTS pipeline from Huggingface
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synthesizer = pipeline("text-to-speech", "Futuresony/output")
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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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# Prepare the messages for the chatbot
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messages = [{"role": "system", "content": system_message}]
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# Add history of previous conversation
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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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@@ -32,7 +32,7 @@ def respond(
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response = ""
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# Generate the response from the model
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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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@@ -44,17 +44,16 @@ def respond(
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response += token
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yield response
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# Convert the generated text to speech
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speech = synthesizer(response)
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# Save the generated speech to a file
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sf.write("generated_speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
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# Return both the text and the audio for playback
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return response, "generated_speech.wav"
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# Create the Gradio interface with a textbox for the user to input a message
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demo = gr.Interface(
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fn=respond,
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inputs=[
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@@ -68,4 +67,4 @@ demo = gr.Interface(
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)
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if __name__ == "__main__":
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demo.launch()
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from transformers import pipeline
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import torch
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# Initialize the client for the text generation model.
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client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
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# Initialize the TTS pipeline from Huggingface.
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synthesizer = pipeline("text-to-speech", model="Futuresony/output")
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def respond(
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message,
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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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history=[]
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):
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# Prepare the messages for the chatbot.
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messages = [{"role": "system", "content": system_message}]
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# Add history of previous conversation.
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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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response = ""
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# Generate the response from the model.
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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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response += token
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yield response
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# Convert the generated text to speech.
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speech = synthesizer(response)
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# Save the generated speech to a file.
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sf.write("generated_speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
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# Return both the text and the audio for playback.
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return response, "generated_speech.wav"
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# Create the Gradio interface with a textbox for the user to input a message.
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demo = gr.Interface(
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fn=respond,
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inputs=[
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)
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if __name__ == "__main__":
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demo.launch()
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