File size: 6,374 Bytes
ea36423
 
614c1d4
8a4e769
 
ea36423
614c1d4
 
 
 
ea36423
 
 
 
 
 
 
8a4e769
 
 
 
 
83c6221
 
 
 
3fcc7dc
ea36423
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
614c1d4
 
 
 
 
 
 
cf5d3de
614c1d4
ea36423
 
 
 
 
 
 
 
 
cf5d3de
614c1d4
 
 
 
 
29a5603
614c1d4
 
 
 
 
ea36423
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
614c1d4
 
ea36423
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83c6221
614c1d4
83c6221
 
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
160
161
162
163
164
import os
import logging
import streamlit as st
#import google.generativeai
from google.cloud import aiplatform
from streamlit_chat import message
import whisper
from gtts import gTTS
import tempfile
from pydub import AudioSegment
from groq import Groq, GroqError

# Securely configure API keys
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

# Configure Google Generative AI API
#genai.configure(api_key=GOOGLE_API_KEY)

# Initialize the AI Platform client
aiplatform.init(project="Generative Language Client", location="your-region")


# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize Groq Client
if not GROQ_API_KEY:
    raise ValueError("GROQ_API_KEY is not set.")
try:
    groq_client = Groq(api_key=GROQ_API_KEY)
    logger.info("Groq API key is set and client is initialized.")
except GroqError as e:
    logger.error(f"Failed to initialize Groq client: {e}")
    raise

# Load Whisper model for audio transcription
try:
    whisper_model = whisper.load_model("base")
    logger.info("Whisper model loaded successfully.")
except Exception as e:
    logger.error(f"Failed to load Whisper model: {e}")
    raise

# Initialize Google Generative Model for chatbot
model = genai.GenerativeModel(
    'gemini-1.5-flash',
    system_instruction=(
        "Persona: You are Dr. Assad Siddiqui, a heart specialist. Only provide information related to heart health, symptoms, and advice. "
        "Ask users about their heart-related symptoms and provide consultation and guidance based on their input. "
        "Always provide brief answers. If the inquiry is not related to heart health, politely say that you can only provide heart-related information. "
        "Responses should be in Urdu written in English and in English."
    )
)

# Function to get chatbot response
def get_chatbot_response(user_input):
    response = model.generate_content(user_input)
    return response.text.strip()

# Function to process audio using Whisper and Groq API
def process_audio(audio_file):
    try:
        result = whisper_model.transcribe(audio_file)
        user_text = result['text']
        logger.info(f"Transcription successful: {user_text}")
    except Exception as e:
        logger.error(f"Error in transcribing audio: {e}")
        return "Error in transcribing audio.", None

    try:
        chat_completion = groq_client.chat.completions.create(
            messages=[{"role": "user", "content": user_text}],
            model="llama3-8b-8192",
        )
        response_text = chat_completion.choices[0].message.content
        logger.info(f"Received response from Groq API: {response_text}")
    except GroqError as e:
        logger.error(f"Error in generating response with Groq API: {e}")
        return "Error in generating response with Groq API.", None

    try:
        tts = gTTS(text=response_text, lang='en')
        audio_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3')
        tts.save(audio_file.name)
        logger.info("Text-to-speech conversion successful.")
    except Exception as e:
        logger.error(f"Error in text-to-speech conversion: {e}")
        return "Error in text-to-speech conversion.", None

    return response_text, audio_file.name

# Streamlit page configuration
st.set_page_config(page_title="Heart Health Chatbot", page_icon="👨‍⚕️", layout="centered")

# Background and header
st.markdown("""
    <style>
        .stApp { background-image: url('https://cdn.wallpapersafari.com/29/34/8Ak1Sf.png'); background-size: cover; }
        .chat-bubble { background-color: #128c7E; color: white; padding: 10px; border-radius: 10px; max-width: 70%; }
        .user-bubble { background-color: #075e54; color: white; padding: 10px; border-radius: 10px; max-width: 70%; }
        img.avatar { width: 50px; height: 50px; border-radius: 50%; }
    </style>
    <div style="padding:10px;text-align:center;color:white;">
        <h1>Heart Health Chatbot 🫀</h1>
        <p>Ask me anything about heart diseases!</p>
    </div>
""", unsafe_allow_html=True)

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

user_avatar_url = "https://img.freepik.com/free-photo/sad-cartoon-anatomical-heart_23-2149767987.jpg"
bot_avatar_url = "https://img.freepik.com/premium-photo/3d-render-man-doctor-avatar-round-sticker-with-cartoon-character-face-user-id-thumbnail.jpg"

# Function to display chat history
def display_chat_history():
    for chat in st.session_state.history:
        if chat["role"] == "user":
            st.markdown(f"""
                <div style="display: flex; justify-content: flex-end; margin-bottom: 10px;">
                    <div class="user-bubble"><p><b>You:</b> {chat['content']}</p></div>
                    <img src="{user_avatar_url}" class="avatar"/>
                </div>
            """, unsafe_allow_html=True)
        else:
            st.markdown(f"""
                <div style="display: flex; margin-bottom: 10px;">
                    <img src="{bot_avatar_url}" class="avatar"/>
                    <div class="chat-bubble"><p><b>Bot:</b> {chat['content']}</p></div>
                </div>
            """, unsafe_allow_html=True)

# Main application layout
def main():
    display_chat_history()
    with st.container():
        with st.form(key="user_input_form", clear_on_submit=True):
            user_input = st.text_input("Type your message...", placeholder="Ask about heart health...", max_chars=500)
            submit_button = st.form_submit_button("Send")

            if submit_button and user_input.strip():
                with st.spinner("Thinking..."):
                    bot_response = get_chatbot_response(user_input)

                # Update chat history
                st.session_state.history.append({"role": "user", "content": user_input})
                st.session_state.history.append({"role": "bot", "content": bot_response})

                display_chat_history()

# Footer
st.markdown("""
    <p style="text-align:center; color:white; margin-top:50px;">
        Check out the <a href="https://live-appointment-chatbot20.zapier.app/" target="_blank" style="color:#34c759;">Live Appointment</a>.
    </p>
""", unsafe_allow_html=True)

# Run the app
if __name__ == "__main__":
    main()