Update app.py

app.py

@@ -1,50 +1,13 @@
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-from gtts import gTTS
-from pytube import Search
-import random
-import os
-
-# Load pretrained models
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-# Load GPT-2 model and tokenizer for story generation
-gpt2_tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
-gpt2_model = AutoModelForCausalLM.from_pretrained("gpt2-medium")
-
-emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", return_all_scores=True)
-
-# Function to generate a comforting story using GPT-2
-def generate_story(theme):
-    # A detailed prompt for generating a comforting story about the selected theme
-    story_prompt = f"Write a comforting, detailed, and heartwarming story about {theme}. The story should include a character who faces a tough challenge, finds hope, and ultimately overcomes the situation with a positive resolution."
-    
-    # Generate story using GPT-2
-    input_ids = gpt2_tokenizer.encode(story_prompt, return_tensors='pt')
-    
-    story_ids = gpt2_model.generate(
-        input_ids,
-        max_length=500,  # Generate longer stories
-        temperature=0.8,  # Balanced creativity
-        top_p=0.9,
-        repetition_penalty=1.2,
-        num_return_sequences=1
-    )
-    
-    # Decode the generated text
-    story = gpt2_tokenizer.decode(story_ids[0], skip_special_tokens=True)
-    return story
-
 def generate_response(user_input):
-    # Refined prompt with a clear and empathetic tone
-    response_prompt = f"You are a kind and empathetic support bot. A user is sharing their feelings: '{user_input}'. Respond with kindness and empathy, offering emotional validation. Keep the tone soft and comforting, and avoid any philosophical or unrelated explanations. Offer support and let them know that their feelings are valid."
+    # Empathy-focused prompt to guide the bot
+    response_prompt = f"The user has shared the following: '{user_input}'. Respond with empathy, compassion, and understanding. Acknowledge their sadness and offer comforting, reassuring words. Show that you care and validate their feelings without giving unsolicited advice."
     
     # Generate the response using the GPT-2 model
     input_ids = gpt2_tokenizer.encode(response_prompt, return_tensors='pt')
     response_ids = gpt2_model.generate(
         input_ids,
         max_length=300,
-        temperature=0.8,
+        temperature=0.85,
         top_k=50,
         repetition_penalty=1.2,
         num_return_sequences=1
@@ -54,116 +17,6 @@ def generate_response(user_input):
     response = gpt2_tokenizer.decode(response_ids[0], skip_special_tokens=True)
     
     # Strip out the prompt portion to get a clean, empathetic message
-    cleaned_response = response.replace(f"You are a kind and empathetic support bot. A user is sharing their feelings: '{user_input}'. Respond with kindness and empathy, offering emotional validation. Keep the tone soft and comforting, and avoid any philosophical or unrelated explanations. Offer support and let them know that their feelings are valid.", "").strip()
+    cleaned_response = response.replace(f"The user has shared the following: '{user_input}'. Respond with empathy, compassion, and understanding. Acknowledge their sadness and offer comforting, reassuring words. Show that you care and validate their feelings without giving unsolicited advice.", "").strip()
     
     return cleaned_response
-
-
-
-# Analyze user input for emotional tone
-def get_emotion(user_input):
-    emotions = emotion_classifier(user_input)
-    emotions_sorted = sorted(emotions[0], key=lambda x: x['score'], reverse=True)
-    return emotions_sorted[0]['label']
-
-# Function to fetch YouTube videos
-def fetch_youtube_videos(activity):
-    search = Search(f"{activity} for mental health relaxation")
-    search_results = search.results[:3]
-    videos = []
-    for video in search_results:
-        video_url = f"https://www.youtube.com/watch?v={video.video_id}"
-        videos.append((video.title, video_url))
-    return videos
-
-# Streamlit page configuration
-st.set_page_config(page_title="Grief and Loss Support Bot 🌿", page_icon="🌿", layout="centered")
-st.markdown("<style>.css-1d391kg { background-color: #F3F7F6; }</style>", unsafe_allow_html=True)
-
-st.title("Grief and Loss Support Bot 🌿")
-st.subheader("Your compassionate companion in tough times 💚")
-
-# Sidebar for Meditation and Story Generation
-with st.sidebar:
-    st.header("🧘 Guided Meditation")
-    if st.button("Play Meditation"):
-        meditation_audio = "meditation.mp3"
-        if not os.path.exists(meditation_audio):
-            tts = gTTS("Take a deep breath. Relax and let go of any tension...", lang='en')
-            tts.save(meditation_audio)
-        st.audio(meditation_audio, format="audio/mp3")
-
-# Generating a comforting story
-st.sidebar.header("📖 Short Comforting Story")
-story_theme = st.selectbox("Choose a theme for your story:", ["courage", "healing", "hope"])
-if st.sidebar.button("Generate Story"):
-    with st.spinner("Generating your story..."):
-        story = generate_story(story_theme)
-    st.text_area("Here's your story:", story, height=300)
-
-
-
-# User input section
-user_input = st.text_input("Share what's on your mind. I am here to listen...", placeholder="Type here...", max_chars=500, key="user_input_1")
-
-
-# Initialize session state
-if 'previous_responses' not in st.session_state:
-    st.session_state.previous_responses = []
-if 'badges' not in st.session_state:
-    st.session_state.badges = []
-
-
-# Initialize session state
-if 'badges' not in st.session_state:
-    st.session_state.badges = []
-
-if user_input:
-    with st.spinner("Analyzing your input..."):
-        # Get the emotion of the user input
-        emotion = get_emotion(user_input)
-        
-        # Generate an empathetic response
-        response = generate_response(user_input)
-        
-        # Display the bot's response
-        st.text_area("Bot's Response:", response, height=250)
-    
-    # Assign badges based on the detected emotion
-    if emotion in ["joy", "optimism"]:
-        badge = "🌟 Positivity Badge"
-        if badge not in st.session_state.badges:
-            st.session_state.badges.append(badge)
-            st.success(f"Congratulations! You've earned a {badge}!")
-
-    # Suggest activities based on emotion
-    st.info("🎨 Try a New Activity")
-    activities = ["exercise", "yoga", "journaling", "painting", "meditation", "swimming"]
-    selected_activity = st.selectbox("Pick an activity:", activities)
-
-    if st.button("Find Videos"):
-        videos = fetch_youtube_videos(selected_activity)
-        if videos:
-            for title, url in videos:
-                st.write(f"[{title}]({url})")
-        else:
-            st.write(f"No results found for '{selected_activity}'.")
-
-# Crisis resources
-if user_input and any(word in user_input.lower() for word in ["suicide", "help", "depressed"]):
-    st.warning("Please reach out to a crisis hotline for immediate support.")
-    st.write("[Find emergency resources here](https://www.helpguide.org/find-help.htm)")
-
-
-# Generate audio response
-if user_input:
-    tts = gTTS(response, lang='en')
-    audio_file = "response.mp3"
-    tts.save(audio_file)
-    st.audio(audio_file, format="audio/mp3")
-
-# Display badgesz
-if st.session_state.badges:
-    st.sidebar.header("🏅 Achievements")
-    for badge in st.session_state.badges:
-        st.sidebar.write(badge)