Create app.py

app.py

@@ -0,0 +1,107 @@
+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()
+