Update app.py

app.py

@@ -1,122 +1,75 @@
-import gradio as gr
-from openai import OpenAI
 import os
+import re
+import gradio as gr
 import edge_tts
 import asyncio
+import time
 import tempfile
+from huggingface_hub import InferenceClient
 
-css = '''
-.gradio-container {
-    max-width: 1000px !important;
-    background-color: #000 !important;
-    color: #0f0 !important;
-    font-family: monospace !important;
-    padding: 20px !important;
-    border-radius: 5px !important;
-    border: 10px solid #333 !important;
-    box-shadow: 0 0 20px #0f0 !important;
-}
-
-h1 {
-    text-align: center;
-    color: #0f0 !important;
-    text-shadow: 0 0 5px #0f0 !important;
-}
-
-footer {
-    visibility: hidden;
-}
-
-textarea, input, .output {
-    background-color: #000 !important;
-    color: #0f0 !important;
-    border: 1px solid #0f0 !important;
-    font-family: monospace !important;
-}
-
-button {
-    background-color: #0f0 !important;
-    color: #000 !important;
-    border: none !important;
-    font-family: monospace !important;
-}
-
-button:hover {
-    background-color: #090 !important;
-}
-
-.audio {
-    width: 100%;
-    margin-top: 20px;
+css= '''
+#important{
+  display: none;
 }
 '''
-
-ACCESS_TOKEN = os.getenv("HF_TOKEN")
-
-client = OpenAI(
-    base_url="https://api-inference.huggingface.co/v1/",
-    api_key=ACCESS_TOKEN,
-)
-
-async def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-    
-    for message in client.chat.completions.create(
-        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-        messages=messages,
-    ):
-        token = message.choices[0].delta.content
-        
-        response += token
-        yield response
-
-    # Convert the response to speech using Edge TTS
-    communicate = edge_tts.Communicate(response)
+DESCRIPTION = """## EDGE TTS
+"""
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+system_instructions = "[INST] Answers by 🔉, Keep conversation very short, clear, friendly and concise."
+
+async def generate(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_instructions + prompt + "[/INST]"
+    stream = client.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
 
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="", label="System message", lines=2),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-P",
-        ),
-    ],
-    css=css,
-    title="Old TV Terminal Chat",
-    description="Welcome to the Old TV Terminal. Type your message below.",
-    additional_outputs=[gr.Audio(label="Generated Speech", autoplay=True)]
-)
+with gr.Blocks(css=css) as demo:    
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        user_input = gr.Textbox(label="Prompt")
+        input_text = gr.Textbox(label="Input Text", elem_id="important")
+        output_audio = gr.Audio(label="Audio", type="filepath",
+                        interactive=False,
+                        autoplay=True,
+                        elem_classes="audio")
+    with gr.Row():
+        translate_btn = gr.Button("Response")
+        translate_btn.click(fn=generate, inputs=user_input,
+                            outputs=output_audio, api_name="translate")        
+    
+    # Add examples
+    gr.Examples(
+        examples=[
+            ["What is AI?"],
+            ["Add 2*3345"],
+            ["Describe Mt. Everest"]
+        ],
+        inputs=user_input,
+        outputs=output_audio,
+        fn=generate,
+        cache_examples=True
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()
+
+
+Use the edge tts along with above and use this to have the generated text to convert to tts