Update app.py

app.py

@@ -1,22 +1,170 @@
-def generate_response(user_input):
-    # 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."
+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')
     
-    # Generate the response using the GPT-2 model
+    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):
+    response_prompt = (
+        f"A user is feeling very sad and overwhelmed: '{user_input}'. "
+        "You are a compassionate support bot. Respond with empathy, encouragement, and reassurance."
+    )
     input_ids = gpt2_tokenizer.encode(response_prompt, return_tensors='pt')
     response_ids = gpt2_model.generate(
         input_ids,
-        max_length=300,
-        temperature=0.85,
-        top_k=50,
-        repetition_penalty=1.2,
+        max_length=150,
+        temperature=0.8,
+        top_k=40,
+        repetition_penalty=1.1,
         num_return_sequences=1
+
     )
     
     # Decode the response and clean it up by removing the prompt
     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"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()
+    cleaned_response = response.replace(f"A user is feeling very sad and overwhelmed:{user_input}.You are a compassionate support bot. Respond with empathy, encouragement, and reassurance", "").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)