Update app.py

app.py

@@ -1,35 +1,62 @@
+import os
+import logging
 import streamlit as st
-from transformers import pipeline
+import google.generativeai as genai
+from streamlit_chat import message
 import whisper
 from gtts import gTTS
 import tempfile
-import os
-import logging
 from pydub import AudioSegment
-import openai
+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)
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Load Hugging Face model for text generation (instead of Google Cloud)
-def load_hf_model():
-    # Load a model for heart health-related questions
-    return pipeline("text-generation", model="gpt2")
-
-# Load Whisper model for transcription
-def load_whisper_model():
-    return whisper.load_model("base")
-
-# Function to generate response using Hugging Face model
-def generate_hf_response(model, prompt):
-    result = model(prompt, max_length=100, num_return_sequences=1)
-    return result[0]["generated_text"]
-
-# Function to process audio input using Whisper and Hugging Face
-def process_audio(audio_file, hf_model, whisper_model):
+# 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:
-        # Transcribe audio using Whisper
         result = whisper_model.transcribe(audio_file)
         user_text = result['text']
         logger.info(f"Transcription successful: {user_text}")
@@ -38,15 +65,17 @@ def process_audio(audio_file, hf_model, whisper_model):
         return "Error in transcribing audio.", None
 
     try:
-        # Generate response using Hugging Face model
-        response_text = generate_hf_response(hf_model, user_text)
-        logger.info(f"Generated response: {response_text}")
-    except Exception as e:
-        logger.error(f"Error in generating response: {e}")
-        return "Error in generating response.", None
+        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:
-        # Convert the response text to speech
         tts = gTTS(text=response_text, lang='en')
         audio_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3')
         tts.save(audio_file.name)
@@ -57,50 +86,72 @@ def process_audio(audio_file, hf_model, whisper_model):
 
     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():
-    st.title("Heart Health & Audio Processing App 🫀🎙️ (Hugging Face Edition)")
-
-    # Load models
-    hf_model = load_hf_model()
-    whisper_model = load_whisper_model()
-
-    # Two tabs: one for the chatbot and one for audio processing
-    tab1, tab2 = st.tabs(["Heart Health Chatbot", "Audio Processing"])
-
-    # Tab 1: Heart Health Chatbot
-    with tab1:
-        st.header("Chat with Heart Health Specialist")
-        
-        if "history" not in st.session_state:
-            st.session_state.history = []
-
-        user_input = st.text_input("Ask about heart health:", placeholder="Type here...")
-
-        if st.button("Send") and user_input:
-            bot_response = generate_hf_response(hf_model, user_input)
-            st.session_state.history.append({"role": "user", "content": user_input})
-            st.session_state.history.append({"role": "bot", "content": bot_response})
-        
-        for chat in st.session_state.history:
-            if chat["role"] == "user":
-                st.write(f"**You:** {chat['content']}")
-            else:
-                st.write(f"**Bot:** {chat['content']}")
-
-    # Tab 2: Audio Processing
-    with tab2:
-        st.header("Audio Processing with Whisper and Hugging Face")
-
-        uploaded_audio = st.file_uploader("Upload an audio file for transcription and response", type=["mp3", "wav", "ogg"])
-        
-        if uploaded_audio:
-            with st.spinner("Processing audio..."):
-                response_text, audio_file_path = process_audio(uploaded_audio, hf_model, whisper_model)
-            
-            if response_text:
-                st.write(f"**Response:** {response_text}")
-                st.audio(audio_file_path)
+    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__":