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" 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
+
+system_instructions1 = """
+[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'])
+
+def save_history():
+    history_df.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')
+    else:
+        history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response'])
+    return history_df
+
+def models(text, model="Mixtral 8x7B", seed=42):
+    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_instructions1 + 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
+    
+    # Add the current interaction to the history DataFrame
+    new_row = pd.DataFrame({
+        'Timestamp': [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],  # Convert to string
+        'Request': [text],
+        'Response': [output]
+    })
+    history_df = pd.concat([history_df, new_row], ignore_index=True)
+    save_history()
+    
+    return output
+
+async def respond(audio, model, seed):
+    user = transcribe(audio)
+    reply = models(user, model, seed)
+    communicate = edge_tts.Communicate(reply)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    return tmp_path
+
+def display_history():
+    return load_history()
+
+def download_history():
+    csv_buffer = io.StringIO()
+    history_df.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>')
+
+DESCRIPTION = """ # <center><b>Dr. Nova Quantum⚡</b></center>
+        ### <center>Your Personal Guide to the Frontiers of Science and Technology</center>
+        ### <center>Engage in Voice Chat with a Visionary Scientist</center>
+        """
+
+with gr.Blocks(css="style.css") as demo:    
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        select = gr.Dropdown([
+            'Mixtral 8x7B',
+            'Llama 3 8B',
+            'Mistral 7B v0.3',
+            'Phi 3 mini',
+        ],
+        value="Mistral 7B v0.3",
+        label="Model"
+        )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=999999,
+            step=1,
+            value=0,
+            visible=False
+        )
+    
+    input_audio = gr.Audio(label="User", sources="microphone", type="filepath")
+    output_audio = gr.Audio(label="Dr. Nova Quantum", type="filepath", autoplay=True)
+    
+    # Add a DataFrame to display the history
+    history_display = gr.DataFrame(label="Conversation History")
+    
+    # Add a download button for the history
+    download_button = gr.Button("Download Conversation History")
+    download_link = gr.HTML()
+    
+    def process_audio(audio, model, seed):
+        response = asyncio.run(respond(audio, model, seed))
+        return response, display_history()
+
+    input_audio.change(
+        fn=process_audio,
+        inputs=[input_audio, select, seed],
+        outputs=[output_audio, history_display]
+    )
+    
+    # Connect the download button to the download function
+    download_button.click(fn=download_history, outputs=[download_link])
+
+    # Load history when the page is loaded or refreshed
+    demo.load(fn=display_history, outputs=[history_display])
+
+if __name__ == "__main__":
+    # Load history at startup
+    load_history()
+    demo.queue(max_size=200).launch(share=True)