Update app.py

app.py

@@ -1,66 +1,107 @@
-# Import necessary libraries
+import streamlit as st
+from transformers import pipeline
+import whisper
+from gtts import gTTS
+import tempfile
 import os
 import logging
-from transformers import pipeline
-from google.cloud import aiplatform
-from google.oauth2 import service_account
+from pydub import AudioSegment
+import openai
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# 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 for Google Cloud AI Platform
-def init_google_ai_platform(api_key):
-    aiplatform.init(api_key=api_key)
-
-# Function to generate a response using Hugging Face
-def generate_hugging_face_response(prompt):
-    # Load a Hugging Face model for text generation
-    generator = pipeline('text-generation', model='gpt2')
-    # Generate text based on the prompt
-    response = generator(prompt, max_length=50, num_return_sequences=1)
-    return response[0]['generated_text']
-
-# Function to generate a response using Google Cloud AI Platform
-def generate_google_ai_response(prompt):
-    response = aiplatform.Model.predict(
-        model_name="gemini-1.5-flash",
-        instances=[{"prompt": prompt}]
-    )
-    return response.predictions[0]['content']
-
-# Main function to run the application
-def main():
-    try:
-        # Set up Google Cloud credentials
-        setup_google_cloud_credentials()
+# 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")
 
-        # Initialize Google Cloud AI Platform
-        api_key = "AIzaSyD-49IyRzS6Ok_zymcEdv1QADw0rWQJFI4"
-        init_google_ai_platform(api_key)
+# Load Whisper model for transcription
+def load_whisper_model():
+    return whisper.load_model("base")
 
-        # Example prompt
-        prompt = "Tell me about heart health."
+# 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"]
 
-        # Generate response from Hugging Face
-        hf_response = generate_hugging_face_response(prompt)
-        logger.info(f"Hugging Face Response: {hf_response}")
+# Function to process audio input using Whisper and Hugging Face
+def process_audio(audio_file, hf_model, whisper_model):
+    try:
+        # Transcribe audio using Whisper
+        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
 
-        # Generate response from Google Cloud AI Platform
-        google_response = generate_google_ai_response(prompt)
-        logger.info(f"Google AI Response: {google_response}")
+    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
 
+    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)
+        logger.info("Text-to-speech conversion successful.")
     except Exception as e:
-        logger.error(f"Error occurred: {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
+
+# 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)
 
+# Run the app
 if __name__ == "__main__":
     main()