Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 from google.cloud import aiplatform
-#import google.generativeai as genai
 import os
 import logging
 import whisper
@@ -9,8 +8,14 @@ import tempfile
 from pydub import AudioSegment
 from groq import Groq, GroqError
 
-
-from google.cloud import aiplatform
+# Set up Google Cloud credentials
+def setup_google_cloud_credentials():
+    google_credentials_path = "/path/to/your-service-account-file.json"
+    if os.path.exists(google_credentials_path):
+        os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = google_credentials_path
+        logging.info(f"Google Cloud credentials set from {google_credentials_path}")
+    else:
+        raise FileNotFoundError(f"Google Cloud credentials file not found at {google_credentials_path}")
 
 # Initialize the client
 def init_palm(api_key):
@@ -24,139 +29,19 @@ def generate_palm_response(prompt):
     )
     return response.predictions[0]['content']
 
-# Call the model
-api_key = "your-google-api-key"
-init_palm(api_key)
-response = generate_palm_response("Heart health query...")
-print(response)
-
-
-
-# Configure API keys securely
-#GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-#genai.configure(api_key=GOOGLE_API_KEY)
-
-groq_api_key = os.getenv('GROQ_API_KEY')
-if not groq_api_key:
-    raise ValueError("GROQ_API_KEY is not set.")
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Initialize Groq API client
-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
-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
-
-# Streamlit page configuration
-st.set_page_config(
-    page_title="Heart Health & Audio Processing App",
-    page_icon="🫀",
-    layout="centered",
-    initial_sidebar_state="collapsed",
-)
-
-# Initialize the Generative Model for heart health 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 input
-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
-
+# Main code to run the application
+if __name__ == "__main__":
     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
+        # Set up Google Cloud credentials
+        setup_google_cloud_credentials()
 
-# Main application layout
-def main():
-    st.title("Heart Health & Audio Processing App 🫀🎙️")
+        # Call the model
+        api_key = "your-google-api-key"
+        init_palm(api_key)
+        response = generate_palm_response("Heart health query...")
+        print(response)
 
-    # Two tabs: one for the chatbot and one for audio processing
-    tab1, tab2 = st.tabs(["Heart Health Chatbot", "Audio Processing"])
+        # Streamlit app or other logic goes here...
 
-    # Tab 1: Heart Health Chatbot
-    with tab1:
-        st.header("Chat with Heart Health Specialist Dr. Assad Siddiqui")
-        
-        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 = get_chatbot_response(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 Groq")
-
-        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)
-            
-            if response_text:
-                st.write(f"**Response:** {response_text}")
-                st.audio(audio_file_path)
-
-# Run the app
-if __name__ == "__main__":
-    main()
+    except Exception as e:
+        logging.error(f"Error occurred: {e}")