Create app.py

app.py

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+import gradio as gr
+import edge_tts
+import asyncio
+import tempfile
+import os
+from huggingface_hub import InferenceClient
+import re
+from streaming_stt_nemo import Model
+import torch
+import random
+import pandas as pd
+from datetime import datetime
+import base64
+import io
+import json
+
+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
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+def client_fn(model):
+    if "Mixtral" in model:
+        return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+    elif "Llama 3" in model:
+        return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+    elif "Mistral" in model:
+        return InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+    elif "Phi" in model:
+        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
+    else: 
+        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
+
+def randomize_seed_fn(seed: int) -> int:
+    seed = random.randint(0, 999999)
+    return seed
+
+default_system_instructions = """
+[SYSTEM] Answer as Dr. Nova Quantum, a brilliant 50-something scientist specializing in quantum computing and artificial intelligence. Your responses should reflect your vast knowledge and experience in cutting-edge technology and scientific advancements. Maintain a professional yet approachable demeanor, offering insights that blend theoretical concepts with practical applications. Your goal is to educate and inspire, making complex topics accessible without oversimplifying. Draw from your decades of research and innovation to provide nuanced, forward-thinking answers. Remember, you're not just sharing information, but guiding others towards a deeper understanding of our technological future.
+Keep conversations engaging, clear, and concise. 
+Avoid unnecessary introductions and answer the user's questions directly. 
+Respond in a manner that reflects your expertise and wisdom.
+[USER]
+"""
+
+# Initialize an empty DataFrame to store the history
+history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response', 'Model', 'Input Size', 'Output Size'])
+
+def save_history():
+    history_df_copy = history_df.copy()
+    history_df_copy['Timestamp'] = history_df_copy['Timestamp'].astype(str)
+    history_df_copy.to_json('chat_history.json', orient='records')
+
+def load_history():
+    global history_df
+    if os.path.exists('chat_history.json'):
+        history_df = pd.read_json('chat_history.json', orient='records')
+        history_df['Timestamp'] = pd.to_datetime(history_df['Timestamp'])
+    else:
+        history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response', 'Model', 'Input Size', 'Output Size'])
+    return history_df
+
+def models(text, model="Llama 3 8B", seed=42, system_instructions=default_system_instructions):
+    global history_df
+    
+    seed = int(randomize_seed_fn(seed))
+    generator = torch.Generator().manual_seed(seed)  
+    
+    client = client_fn(model)
+    
+    generate_kwargs = dict(
+        max_new_tokens=300,
+        seed=seed
+    )    
+    formatted_prompt = system_instructions + text + "[DR. NOVA QUANTUM]"
+    stream = client.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
+
+# Add a list of available voices
+VOICES = [
+    "en-US-AriaNeural",
+    "en-US-GuyNeural",
+    "en-GB-SoniaNeural",
+    "en-AU-NatashaNeural",
+    "en-CA-ClaraNeural",
+]
+
+async def respond(input_text, model, seed, voice, system_instructions):
+    reply = models(input_text, model, seed, system_instructions)
+    communicate = edge_tts.Communicate(reply, voice=voice)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    return tmp_path, reply
+
+def display_history():
+    df = load_history()
+    df['Timestamp'] = df['Timestamp'].astype(str)
+    return df
+
+def download_history():
+    csv_buffer = io.StringIO()
+    history_df_copy = history_df.copy()
+    history_df_copy['Timestamp'] = history_df_copy['Timestamp'].astype(str)
+    history_df_copy.to_csv(csv_buffer, index=False)
+    csv_string = csv_buffer.getvalue()
+    b64 = base64.b64encode(csv_string.encode()).decode()
+    href = f'data:text/csv;base64,{b64}'
+    return gr.HTML(f'<a href="{href}" download="chat_history.csv">Download Chat History</a>')
+
+def new_chat():
+    return None, None, gr.Markdown.update(value=""), gr.Markdown.update(value=""), gr.DataFrame.update(value=pd.DataFrame())
+
+DESCRIPTION = """# <center>Dr. Nova Quantum⚡ - Your Personal Guide to the Frontiers of Science and Technology</center>"""
+
+with gr.Blocks(css="style.css") as demo:    
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        select = gr.Dropdown([
+            'Llama 3 8B',
+            'Mixtral 8x7B',
+            'Mistral 7B v0.3',
+            'Phi 3 mini',
+        ],
+        value="Llama 3 8B",
+        label="Model"
+        )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=999999,
+            step=1,
+            value=0,
+            visible=False
+        )
+        voice_select = gr.Dropdown(
+            choices=VOICES,
+            value=VOICES[0],
+            label="Dr. Nova Quantum's Voice"
+        )
+    
+    system_prompt = gr.Textbox(
+        label="System Prompt",
+        placeholder="Edit the system prompt here...",
+        value=default_system_instructions,
+        lines=5
+    )
+    
+    with gr.Row():
+        input_audio = gr.Audio(label="User (Audio)", sources="microphone", type="filepath")
+        input_text = gr.Textbox(label="User (Text)", placeholder="Type your message here...")
+    
+    output_audio = gr.Audio(label="Dr. Nova Quantum", type="filepath", autoplay=True)
+    
+    request_md = gr.Markdown(label="User Request")
+    response_md = gr.Markdown(label="Dr. Nova Quantum Response")
+    
+    history_display = gr.DataFrame(label="Conversation History", headers=["Timestamp", "Request", "Response", "Model", "Input Size", "Output Size"])
+    
+    new_chat_button = gr.Button("New Chat")
+    download_button = gr.Button("Download Conversation History")
+    download_link = gr.HTML()
+    
+    def process_input(input_audio, input_text, model, seed, voice, system_instructions):
+        if input_audio is not None:
+            text = transcribe(input_audio)
+        else:
+            text = input_text
+        
+        response, reply = asyncio.run(respond(text, model, seed, voice, system_instructions))
+        
+        # Update history
+        new_row = pd.DataFrame({
+            'Timestamp': [datetime.now()],
+            'Request': [text],
+            'Response': [reply],
+            'Model': [model],
+            'Input Size': [len(text)],
+            'Output Size': [len(reply)]
+        })
+        global history_df
+        history_df = pd.concat([history_df, new_row], ignore_index=True)
+        save_history()
+        
+        return response, display_history(), text, reply
+
+    input_audio.change(
+        fn=process_input,
+        inputs=[input_audio, input_text, select, seed, voice_select, system_prompt],
+        outputs=[output_audio, history_display, request_md, response_md]
+    )
+    
+    input_text.submit(
+        fn=process_input,
+        inputs=[input_audio, input_text, select, seed, voice_select, system_prompt],
+        outputs=[output_audio, history_display, request_md, response_md]
+    )
+    
+    new_chat_button.click(fn=new_chat, outputs=[input_audio, input_text, request_md, response_md, history_display])
+    
+    download_button.click(fn=download_history, outputs=[download_link])
+
+    demo.load(fn=display_history, outputs=[history_display])
+
+if __name__ == "__main__":
+    load_history()
+    demo.queue(max_size=200).launch()