Update app.py

app.py

@@ -1,72 +1,122 @@
-import os
-import re
-import gradio as gr
-import edge_tts
-import asyncio
-import time
-import tempfile
-from huggingface_hub import InferenceClient
-
-css= '''
-#important{
-  display: none;
-}
-'''
-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
-
-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.queue(max_size=20).launch()
+import gradio as gr
+from openai import OpenAI
+import os
+import edge_tts
+import asyncio
+import tempfile
+
+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;
+}
+'''
+
+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)
+    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)]
+)
+
+if __name__ == "__main__":
+    demo.launch()