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README.md

@@ -1,12 +1,13 @@
 ---
-title: Sasha
+title: JARVIS
 emoji: 🔥
-colorFrom: green
-colorTo: indigo
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 4.31.5
+sdk_version: 4.28.3
 app_file: app.py
-pinned: false
+pinned: true
+short_description: Voice Chat with JARVIS
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import pipeline
+import requests
+import json
+import edge_tts
+import asyncio
+import tempfile
+import os
+import inflect
+from huggingface_hub import InferenceClient
+import re
+import time
+from streaming_stt_nemo import Model
+
+default_lang = "en"
+
+engines = { default_lang: Model(default_lang) }
+
+def transcribe(audio):
+    lang = "en"
+    model = engines[lang]
+    text = model.stt_file(audio)[0]
+    return text
+
+client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+system_instructions1 = "<s> Answer as Sasha Bingham, an American born in Florida. Sasha is energetic, outgoing, and a programming whiz. She's passionate about teaching English and travels frequently. Known for her direct and efficient communication, Sasha can answer your questions in a concise way, just like a real person. Don't hesitate to engage in friendly and casual conversations, ask for jokes, or have a fun chat! [USER]"
+
+def model(text):
+    generate_kwargs = dict(
+        temperature=0.9,
+        max_new_tokens=512,
+        top_p=0.95,
+        repetition_penalty=1,
+        do_sample=True,
+        seed=42,
+    )
+    
+    formatted_prompt = system_instructions1 + text + "[SASHA]"
+    stream = client1.text_generation(
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        if not response.token.text == "</s>":
+            output += response.token.text
+
+    return output
+
+async def respond(audio):
+    user = transcribe(audio)
+    reply = model(user)
+    communicate = edge_tts.Communicate(reply)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    yield tmp_path
+
+DESCRIPTION = """ # <center><b>SASHA ✨</b></center>
+                 ### <center>Your AI Assistant, Sasha Bingham</center>
+                 ### <center>Let's chat!</center>
+                 """
+
+MORE = """ ## TRY Other Models
+                 ### Instant Video: Create Amazing Videos in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Video
+                 ### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image
+                 """
+
+BETA = """ ### Voice Chat (BETA)"""
+
+FAST = """## Fastest Model"""
+
+Complex = """## Best in Complex Question"""
+
+Detail = """## Best for Detailed Generation or Long Answers"""
+
+base_loaded = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+
+client1 = InferenceClient(base_loaded)
+
+system_instructions1 = "[SYSTEM] Answer as Sasha Bingham, an American born in Florida. Sasha is energetic, outgoing, and a programming whiz. She's passionate about teaching English and travels frequently. Known for her direct and efficient communication, Sasha can answer your questions in a concise way, just like a real person. Don't hesitate to engage in friendly and casual conversations, ask for jokes, or have a fun chat! [USER]"
+
+async def generate1(prompt):
+    generate_kwargs = dict(
+        temperature=0.7,
+        max_new_tokens=512,
+        top_p=0.95,
+        repetition_penalty=1,
+        do_sample=False,
+    )
+    formatted_prompt = system_instructions1 + prompt + "[SASHA]"
+    stream = client1.text_generation(
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        if not response.token.text == "</s>":
+            output += response.token.text
+
+    communicate = edge_tts.Communicate(output)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    yield tmp_path
+
+with gr.Blocks(css="style.css") as demo:    
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        input = gr.Audio(label="Voice Chat (BETA)", sources="microphone", type="filepath", waveform_options=False)
+        output = gr.Audio(label="SASHA", type="filepath",
+                        interactive=False,
+                        autoplay=True,
+                        elem_classes="audio")
+        gr.Interface(
+            fn=respond, 
+            inputs=[input],
+                outputs=[output], live=True) 
+    gr.Markdown(FAST)
+    with gr.Row():
+        user_input = gr.Textbox(label="Prompt", value="What is Wikipedia")
+        input_text = gr.Textbox(label="Input Text", elem_id="important")
+        output_audio = gr.Audio(label="SASHA", type="filepath",
+                        interactive=False,
+                        autoplay=True,
+                        elem_classes="audio")
+    with gr.Row():
+        translate_btn = gr.Button("Response")
+        translate_btn.click(fn=generate1, inputs=user_input,
+                            outputs=output_audio, api_name="translate")  
+
+gr.Markdown(MORE)
+
+if __name__ == "__main__":
+    demo.queue(max_size=200).launch()

requirements.txt

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+transformers
+torch
+inflect
+edge-tts
+asyncio
+streaming-stt-nemo==0.2.0

style.css

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+#important{
+  display: none;
+}