Create app.py

app.py

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+import gradio as gr
+import speech_recognition as sr
+from huggingface_hub import InferenceClient
+import random
+import textwrap
+import pyttsx3
+
+# Initialize the speech recognition and TTS engine
+recognizer = sr.Recognizer()
+tts_engine = pyttsx3.init()
+
+# Define the model to be used
+model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+client = InferenceClient(model)
+
+# Embedded system prompt
+system_prompt_text = (
+    "You are a smart and helpful co-worker of Thailand based multi-national company PTT, "
+    "and PTTEP. You help with any kind of request and provide a detailed answer to the question. "
+    "But if you are asked about something unethical or dangerous, you must refuse and provide a safe and respectful way to handle that."
+)
+
+# Read the content of the info.md file with UTF-8 encoding
+with open("info.md", "r", encoding="utf-8") as file:
+    info_md_content = file.read()
+
+# Chunk the info.md content into smaller sections
+chunk_size = 2500  # Adjust this size as needed
+info_md_chunks = textwrap.wrap(info_md_content, chunk_size)
+
+def get_all_chunks(chunks):
+    return "\n\n".join(chunks)
+
+def format_prompt_mixtral(message, history, info_md_chunks):
+    prompt = "<s>"
+    all_chunks = get_all_chunks(info_md_chunks)
+    prompt += f"{all_chunks}\n\n"  # Add all chunks of info.md at the beginning
+    prompt += f"{system_prompt_text}\n\n"  # Add the system prompt
+
+    if history:
+        for user_prompt, bot_response in history:
+            prompt += f"[INST] {user_prompt} [/INST]"
+            prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
+    generate_kwargs = dict(
+        temperature=temp,
+        max_new_tokens=tokens,
+        top_p=top_p,
+        repetition_penalty=rep_p,
+        do_sample=True,
+        seed=seed,
+    )
+
+    formatted_prompt = format_prompt_mixtral(prompt, history, info_md_chunks)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield [(prompt, output)]
+    history.append((prompt, output))
+    yield history
+
+def clear_fn():
+    return None, None
+
+rand_val = random.randint(1, 1111111111111111)
+
+def check_rand(inp, val):
+    if inp:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
+    else:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
+
+def recognize_speech(audio):
+    with sr.AudioFile(audio) as source:
+        audio_data = recognizer.record(source)  # Record the audio
+        try:
+            # Recognize the speech using Google's API
+            text = recognizer.recognize_google(audio_data)
+            return text
+        except sr.UnknownValueError:
+            return "Sorry, I could not understand the audio."
+        except sr.RequestError:
+            return "Error: Could not request results from the speech recognition service."
+
+def speak_text(text):
+    # Convert text to speech using pyttsx3
+    tts_engine.save_to_file(text, 'output.mp3')  # Save the TTS audio
+    tts_engine.runAndWait()  # Wait until TTS is done
+
+with gr.Blocks() as app:  
+    gr.HTML("""<center><h1 style='font-size:xx-large;'>PTT Chatbot</h1><br><h3>running on Huggingface Inference</h3><br><h7>EXPERIMENTAL</center>""")
+    
+    with gr.Row():
+        chat = gr.Chatbot(height=500)
+    
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(scale=3):
+                inp = gr.Audio(type="filepath")  # Audio input
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        btn = gr.Button("Chat")
+                    with gr.Column(scale=1):
+                        with gr.Group():
+                            stop_btn = gr.Button("Stop")
+                            clear_btn = gr.Button("Clear")
+            with gr.Column(scale=1):
+                with gr.Group():
+                    rand = gr.Checkbox(label="Random Seed", value=True)
+                    seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
+                    tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
+                    temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
+
+    hid1 = gr.Number(value=1, visible=False)
+
+    output_audio = gr.Audio(label="Output Audio", type="filepath", interactive=False)  # Create an output audio component
+
+    def handle_chat(audio_input, chat_history, seed, temp, tokens, top_p, rep_p):
+        user_message = recognize_speech(audio_input)  # Recognize speech input
+        if "Sorry" in user_message:  # Check for error in recognition
+            return chat_history, user_message, None
+        response_gen = chat_inf(user_message, chat_history, seed, temp, tokens, top_p, rep_p)
+        response = next(response_gen)[0][-1][1]  # Get the response text
+        speak_text(response)  # Speak the response text
+        return chat_history + [(user_message, response)], response, 'output.mp3'  # Return the filename for audio output
+
+    go = btn.click(handle_chat, [inp, chat, seed, temp, tokens, top_p, rep_p], [chat, inp, output_audio])  # Use output_audio instead of "output.mp3"
+
+    stop_btn.click(None, None, None, cancels=[go])
+    clear_btn.click(clear_fn, None, [inp, chat])
+
+app.queue(default_concurrency_limit=10).launch(share=True, auth=("admin", "0112358"))
+