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Step4_voice.py

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+# Library
+import openai
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+from TTS.api import TTS
+import whisper
+from audio_recorder import record
+
+# Custom Streamlit app title and icon
+st.set_page_config(
+    page_title="VietAI Bot",
+    page_icon=":robot_face:",
+)
+
+# Set the title
+st.title("[VietAI-NTI] ChatGPT")
+
+# Sidebar Configuration
+st.sidebar.title(":gear: Model Configuration")
+
+# Set OPENAI API
+openai.api_key = st.sidebar.text_input('Your OpenAI API key here:')
+
+# User Input and AI Response
+user_input_type = st.sidebar.selectbox("Choose input type:", ["Chat", "Record Audio"])
+
+# Model Name Selector
+model_name = st.sidebar.selectbox(
+    "Select a Model",
+    ["gpt-3.5-turbo", "gpt-4"],  # Add more model names as needed
+    key="model_name",
+)
+
+# Temperature Slider
+temperature = st.sidebar.slider(
+    ":thermometer: Temperature",
+    min_value=0.2,
+    max_value=2.0,
+    value=1.0,
+    step=0.1,
+    key="temperature",
+)
+
+# Max tokens Slider
+max_tokens = st.sidebar.slider(
+    ":straight_ruler: Max Tokens",
+    min_value=1,
+    max_value=4095,
+    value=256,
+    step=1,
+    key="max_tokens",
+)
+
+# Top p Slider
+# top_p = st.sidebar.slider(
+#     "🎯 Top P",
+#     min_value=0.00,
+#     max_value=1.00,
+#     value=1.00,
+#     step=0.01,
+#     key="top_p",
+# )
+
+# Presence penalty Slider
+# presence_penalty = st.sidebar.slider(
+#     "🚫 Presence penalty",
+#     min_value=0.00,
+#     max_value=2.00,
+#     value=0.00,
+#     step=0.01,
+#     key="presence_penalty",
+# )
+
+# Frequency penalty Slider
+# frequency_penalty = st.sidebar.slider(
+#     "🤐 Frequency penalty",
+#     min_value=0.00,
+#     max_value=2.00,
+#     value=0.00,
+#     step=0.01,
+#     key="frequency_penalty",
+# )
+
+# TEXT2SPEECH MODEL
+# Instantiate the TTS class
+tts = TTS(TTS().list_models()[13])
+def convert_2_speech(given_text):
+    tts.tts_to_file(text=given_text, file_path="response.wav")
+    return("response.wav")
+
+# SPEECH2TEXT MODEL
+model_whisper = whisper.load_model("tiny.en")
+def convert_2_text(speech):
+    user_message = model_whisper.transcribe(speech)["text"]
+    return user_message
+
+# CHAT MODEL
+# Initialize DataFrame to store chat history
+chat_history_df = pd.DataFrame(columns=["Timestamp", "Chat"])
+
+# Reset Button
+if st.sidebar.button(":arrows_counterclockwise: Reset Chat"):
+    # Save the chat history to the DataFrame before clearing it
+    if st.session_state.messages:
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        chat_history = "\n".join([f"{m['role']}: {m['content']}" for m in st.session_state.messages])
+        new_entry = pd.DataFrame({"Timestamp": [timestamp], "Chat": [chat_history]})
+        chat_history_df = pd.concat([chat_history_df, new_entry], ignore_index=True)
+
+        # Save the DataFrame to a CSV file
+        chat_history_df.to_csv("chat_history.csv", index=False)
+
+    # Clear the chat messages and reset the full response
+    st.session_state.messages = []
+    full_response = ""
+    
+# Initialize Chat Messages
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Initialize full_response outside the user input check
+full_response = ""
+
+# Display Chat History
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# User Input and AI Response
+# if st.sidebar.button(":microphone: Record Audio"):
+#     st.audio(None, format="audio/wav", start_time=0)
+#     recorded_audio = st.audio_recorder(key="audio_recorder")
+
+#     if recorded_audio:
+#         st.success("Recording complete. Click 'Generate Response' to transcribe and generate AI response.")
+        
+
+
+# User Input and AI Response
+# For "Chat mode"
+if user_input_type == "Chat":
+    if prompt := st.chat_input("What is up?"):
+        # System
+        st.session_state.messages.append({"role": "system", "content": "You are a helpful assistant named Jarvis"})
+        
+        # User
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        # Assistant 
+        with st.chat_message("assistant"):
+            with st.status("Generating response..."):
+                message_placeholder = st.empty()
+                for response in openai.ChatCompletion.create(
+                    model=model_name,  # Use the selected model name
+                    messages=[
+                        {"role": m["role"], "content": m["content"]}
+                        for m in st.session_state.messages
+                    ],
+                    temperature=temperature,  # Set temperature
+                    max_tokens=max_tokens,  # Set max tokens
+                    top_p=top_p, # Set top p
+                    frequency_penalty=frequency_penalty, # Set frequency penalty
+                    presence_penalty=presence_penalty, # Set presence penalty
+                    stream=True,
+                ):
+                    full_response += response.choices[0].delta.get("content", "")
+                    message_placeholder.markdown(full_response + "▌")
+                message_placeholder.markdown(full_response)
+            
+            st.session_state.messages.append({"role": "assistant", "content": full_response})
+            st.audio(convert_2_speech(full_response))
+
+elif user_input_type == "Record Audio":
+    # Record audio when the "Record Audio" button is clicked
+    if st.button("Record Audio"):
+        st.write("Recording... Please speak for 10 seconds.")
+        output = record(seconds=10, filename='my_recording.wav')
+        st.write("Recording complete!")
+
+        # Convert the recorded audio to text using the Whisper model
+        user_message = convert_2_text(output)
+
+        # Display the transcribed text as user input
+        st.session_state.messages.append({"role": "user", "content": user_message})
+        with st.chat_message("user"):
+            st.markdown(user_message)
+        
+        # Assistant 
+        with st.chat_message("assistant"):
+            with st.status("Generating response..."):
+                message_placeholder = st.empty()
+                for response in openai.ChatCompletion.create(
+                    model=model_name,  # Use the selected model name
+                    messages=[
+                        {"role": m["role"], "content": m["content"]}
+                        for m in st.session_state.messages
+                    ],
+                    temperature=temperature,  # Set temperature
+                    max_tokens=max_tokens,  # Set max tokens
+                    top_p=top_p, # Set top p
+                    frequency_penalty=frequency_penalty, # Set frequency penalty
+                    presence_penalty=presence_penalty, # Set presence penalty
+                    stream=True,
+                ):
+                    full_response += response.choices[0].delta.get("content", "")
+                    message_placeholder.markdown(full_response + "▌")
+                message_placeholder.markdown(full_response)
+            
+            st.session_state.messages.append({"role": "assistant", "content": full_response})
+            st.audio(convert_2_speech(full_response))

requirement.txt

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+openai
+openai-whisper
+streamlit
+TTS == 0.17.4