Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import torch
-from transformers import pipeline
-from youtubesearchpython import VideosSearch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import pyttsx3
 
 # Set up the page configuration for a welcoming appearance
 st.set_page_config(page_title="Grief and Loss Support Bot", page_icon="🕊️", layout="centered")
@@ -39,49 +39,39 @@ st.markdown("""
 st.title("Grief and Loss Support Bot 🕊️")
 st.subheader("We are here for you. 💚 Your companion in tough times")
 
-# Load the conversational model pipeline
+# Load the model and tokenizer for text generation
+model_name = "microsoft/DialoGPT-medium"
 try:
-    conversational_pipeline = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    text_gen_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
 except Exception as e:
     st.error(f"Error loading the conversational model: {e}")
 
-# Load the TTS pipeline for generating audio
+# Initialize the TTS engine
 try:
-    tts = pipeline(task="text-to-audio", model="espnet/kan-bayashi_ljspeech_vits", device=0 if torch.cuda.is_available() else -1)
+    tts_engine = pyttsx3.init()
+    tts_engine.setProperty('rate', 150)  # Adjust the speed of speech
+    tts_engine.setProperty('voice', tts_engine.getProperty('voices')[0].id)  # Choose the first voice option
 except Exception as e:
-    st.error(f"Error loading the TTS model: {e}")
+    st.error(f"Error initializing the TTS engine: {e}")
 
 # User input for conversation
 user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)
 
 if user_input:
     # Generate a conversational response
-    response = conversational_pipeline(user_input)
-    response_text = response[0]['generated_text']
-    
-    st.write("Bot's Response:")
-    st.write(response_text)
-
-    # Generate audio from the response text
     try:
-        audio_output = tts(response_text)
-        audio_file_path = "response_audio.wav"
-        with open(audio_file_path, "wb") as f:
-            f.write(audio_output["audio"])
-        st.audio(audio_file_path)
-    except Exception as e:
-        st.error(f"Error generating audio: {e}")
+        response = text_gen_pipeline(user_input, max_length=100, num_return_sequences=1)
+        response_text = response[0]['generated_text']
+        
+        st.write("Bot's Response:")
+        st.write(response_text)
 
-    # Provide hobby suggestions
-    st.markdown("### Suggested Hobbies and Activities")
-    hobby_prompt = "Suggest hobbies and activities for emotional well-being:"
-    hobby_response = pipeline("text-generation", model="gpt2")(hobby_prompt, max_length=50, num_return_sequences=1)
-    st.write(hobby_response[0]['generated_text'])
-
-    # Search for coping videos on YouTube
-    st.markdown("### Informational YouTube Videos")
-    video_search = VideosSearch('productive hobbies and coping with grief', limit=3)
-    results = video_search.result()
-    for video in results['result']:
-        st.write(f"[{video['title']}]({video['link']})")
+        # Convert the response text to speech
+        if st.button("Play Response Audio"):
+            tts_engine.say(response_text)
+            tts_engine.runAndWait()
 
+    except Exception as e:
+        st.error(f"Error generating response: {e}")