Delete app.py

app.py

@@ -1,107 +0,0 @@
-import os
-import re
-import gradio as gr
-import edge_tts
-import asyncio
-import time
-import tempfile
-from huggingface_hub import InferenceClient
-
-class JarvisModels:
-    def __init__(self):
-        self.client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-        self.client2 = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
-        self.client3 = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
-    
-    async def generate_model1(self, prompt):
-        generate_kwargs = dict(
-            temperature=0.6,
-            max_new_tokens=256,
-            top_p=0.95,
-            repetition_penalty=1,
-            do_sample=True,
-            seed=42,
-        )
-        formatted_prompt = system_instructions1 + prompt + "[JARVIS]"
-        stream = self.client1.text_generation(
-            formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
-        output = ""
-        for response in stream:
-            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
-    
-    async def generate_model2(self, prompt):
-        generate_kwargs = dict(
-            temperature=0.6,
-            max_new_tokens=512,
-            top_p=0.95,
-            repetition_penalty=1,
-            do_sample=True,
-        )    
-        formatted_prompt = system_instructions2 + prompt + "[ASSISTANT]"
-        stream = self.client2.text_generation(
-            formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
-        output = ""
-        for response in stream:
-            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
-    
-    async def generate_model3(self, prompt):
-        generate_kwargs = dict(
-            temperature=0.6,
-            max_new_tokens=2048,
-            top_p=0.95,
-            repetition_penalty=1,
-            do_sample=True,
-        )    
-        formatted_prompt = system_instructions3 + prompt + "[ASSISTANT]"
-        stream = self.client3.text_generation(
-            formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
-        output = ""
-        for response in stream:
-            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
-
-class JarvisApp:
-    def __init__(self):
-        self.models = JarvisModels()
-    
-    def launch_app(self):
-        with gr.Blocks(css="style.css") as demo:    
-            gr.Markdown(DESCRIPTION)
-            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="JARVIS", type="filepath",
-                                interactive=False,
-                                autoplay=True,
-                                elem_classes="audio")
-            with gr.Row():
-                translate_btn = gr.Button("Response")
-                translate_btn.click(fn=self.models.generate_model1, inputs=user_input,
-                                    outputs=output_audio, api_name="translate")  
-
-        gr.Markdown(MORE)
-
-        if __name__ == "__main__":
-            demo.queue(max_size=200).launch()
-
-if __name__ == "__main__":
-    app = JarvisApp()
-    app.launch_app()
-