Hugging Face's logo

Spaces:
Gopikanth123
/
check
Sleeping

App Files Community
check
File size: 4,886 Bytes 
9f50d19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
65ea836
9f50d19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
65ea836
 
 
9f50d19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
65ea836
9f50d19
65ea836
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
 
import streamlit as st
import sounddevice as sd
import numpy as np
import speech_recognition as sr
import pyttsx3
import threading
import io
from gradio_client import Client

# Initialize session state
if "messages" not in st.session_state:
    st.session_state["messages"] = []  # Store chat history

# Function to generate a response using Gradio client
def generate_response(query):
    try:
        client = Client("Gopikanth123/llama2")
        result = client.predict(query=query, api_name="/predict")
        return result
    except Exception as e:
        return f"Error communicating with the Gradio backend: {e}"

# Function to handle user input and bot response
def handle_user_input(user_input):
    if user_input:
        # Add user message to session state
        st.session_state["messages"].append({"user": user_input})

        # Generate bot response
        response = generate_response(user_input)
        st.session_state["messages"].append({"bot": response})

        # Speak out bot response in a new thread to avoid blocking
        threading.Thread(target=speak_text, args=(response,), daemon=True).start()

        # Update chat history after each interaction
        # update_chat_history()

# Function to speak text (Voice Output)
def speak_text(text):
    engine = pyttsx3.init()
    engine.stop()  # Ensure no previous loop is running
    engine.say(text)
    engine.runAndWait()

# Function to update chat history dynamically
def update_chat_history():
    chat_history = st.session_state["messages"]
    for msg in chat_history:
        if "user" in msg:
            st.markdown(f"<div class='chat-bubble user-message'><strong>You:</strong> {msg['user']}</div>", unsafe_allow_html=True)
        if "bot" in msg:
            st.markdown(f"<div class='chat-bubble bot-message'><strong>Bot:</strong> {msg['bot']}</div>", unsafe_allow_html=True)

# Function to recognize speech using sounddevice
def recognize_speech_sounddevice():
    st.info("Listening... Speak into the microphone.")
    fs = 16000  # Sample rate in Hz
    duration = 5  # Duration in seconds

    # Record the audio using sounddevice
    audio_data = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype='int16')
    sd.wait()

    # Convert the audio data to the format expected by speech_recognition
    recognizer = sr.Recognizer()
    audio = sr.AudioData(audio_data.tobytes(), fs, 2)

    try:
        recognized_text = recognizer.recognize_google(audio)
        st.session_state["user_input"] = recognized_text
        st.success(f"Recognized Text: {recognized_text}")
        handle_user_input(recognized_text)
    except sr.UnknownValueError:
        st.error("Sorry, I couldn't understand the audio.")
    except sr.RequestError:
        st.error("Could not request results; please check your internet connection.")


# Main Streamlit app
st.set_page_config(page_title="Llama2 Chatbot", page_icon="🤖", layout="wide")
st.markdown(
    """
    <style>
    .stButton>button {
        background-color: #6C63FF;
        color: white;
        font-size: 16px;
        border-radius: 10px;
        padding: 10px 20px;
    }
    .stTextInput>div>input {
        border: 2px solid #6C63FF;
        border-radius: 10px;
        padding: 10px;
    }
    .chat-container {
        background-color: #F7F9FC;
        padding: 20px;
        border-radius: 15px;
        max-height: 400px;
        overflow-y: auto;
    }
    .chat-bubble {
        padding: 10px 15px;
        border-radius: 15px;
        margin: 5px 0;
        max-width: 80%;
        display: inline-block;
    }
    .user-message {
        background-color: #D1C4E9;
        text-align: left;
        margin-left: auto;
    }
    .bot-message {
        background-color: #BBDEFB;
        text-align: left;
        margin-right: auto;
    }
    .input-container {
        display: flex;
        justify-content: space-between;
        gap: 10px;
        padding: 10px 0;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.title("🤖 Chat with Llama2 Bot")
st.markdown(
    """
    Welcome to the *Llama2 Chatbot*!   
    - *Type* your message below, or   
    - *Use the microphone* to speak to the bot.   
    """
)

# Display chat history
chat_history_container = st.container()
with chat_history_container:
    # Add input field within a form
    with st.form(key='input_form', clear_on_submit=True):
        user_input = st.text_input("Type your message here...", placeholder="Hello, how are you?")
        submit_button = st.form_submit_button("Send")

        # Handle form submission
        if submit_button:
            handle_user_input(user_input)

    # Separate button for speech recognition outside of the form
    if st.button("Speak"):
        recognize_speech_sounddevice()

    st.markdown("### Chat History")
    # Update chat history on every interaction
    update_chat_history()