Update app.py

app.py

@@ -1,15 +1,20 @@
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 from gtts import gTTS
 from pytube import Search
 import random
+import os
+import datetime
+import torch
 
-# Load DialoGPT model and tokenizer from Hugging Face
+# Load models
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
 
 # Set up 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; }
@@ -21,81 +26,69 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# Title and introduction to the bot
+# Title and introduction
 st.title("Grief and Loss Support Bot 🌿")
 st.subheader("Your compassionate companion in tough times 💚")
 
-# User input
-user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)
-
-# Store previous responses to check for repetition
-if 'previous_responses' not in st.session_state:
-    st.session_state.previous_responses = []
-
-# Function to generate a more empathetic and focused response using DialoGPT
+# Function to generate a response
 def generate_response(user_input):
-    # Encode the input text and generate a response
-    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
-    bot_input_ids = new_user_input_ids
-    chat_history_ids = model.generate(bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, temperature=0.7, top_k=50, repetition_penalty=1.2)
-    
-    # Decode the response to text
-    chat_history_ids = chat_history_ids[:, bot_input_ids.shape[-1]:]  # remove the input from the response
-    bot_output = tokenizer.decode(chat_history_ids[0], skip_special_tokens=True)
-    
-    # Build a more empathetic and thoughtful response
-    response = f"I’m really sorry you're feeling like this. It’s completely normal to feel overwhelmed when you're facing a heavy workload. It’s important to acknowledge how you feel and not keep it bottled up. Sometimes, stress and emotional exhaustion can build up, and it’s okay to let yourself feel those emotions."
-
-    # Add coping strategies based on the situation
-    if "workload" in user_input.lower():
-        response += "\n\nWhen the workload feels too heavy, it can be helpful to break tasks down into smaller, more manageable steps. Focus on one thing at a time, and remember that it’s okay to take breaks when needed. Asking for support from colleagues or friends is also a good way to lighten the load."
-
-    # Add general supportive message
-    response += "\n\nYou're doing your best, and that’s all anyone can ask for. Please take care of yourself and know that it’s okay to take a step back when things feel too much. Your well-being is the most important thing."
+    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+    chat_history_ids = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, temperature=0.7, top_k=50, repetition_penalty=1.2)
+    bot_output = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return bot_output
 
-    # Suggest a productive activity based on detected keywords
-    if any(keyword in user_input.lower() for keyword in ["lonely", "lost", "sad", "overwhelmed"]):
-        st.info("Here's a suggestion to help you cope:")
+# Function to generate a comforting story based on a theme
+def generate_story(theme):
+    prompt = f"Tell me a short, comforting story about {theme}."
+    input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors='pt')
+    
+    # Generate story
+    story_ids = model.generate(input_ids, max_length=150, temperature=0.7, top_p=0.9, repetition_penalty=1.2, do_sample=True, num_return_sequences=1)
+    story = tokenizer.decode(story_ids[0], skip_special_tokens=True)
+    return story
 
-        # List of activities
-        hobbies = ["journaling", "yoga", "painting", "exercise", "meditation"]
-        activity = st.selectbox("Choose an activity you'd like to try:", hobbies)
-        
-        # Search YouTube for videos related to the selected activity
-        try:
-            search = Search(activity)
-            search_results = search.results[:3]  # limit results to 3 videos
-            st.write(f"Searching for videos related to {activity}...")  # Debugging line
-            
-            if not search_results:
-                st.write(f"No results found for '{activity}'. Please try again.")
-            else:
-                st.write(f"Found {len(search_results)} video(s) related to '{activity}'!")
-                for video in search_results:
-                    st.write(f"[{video.title}]({video.watch_url})")
-        except Exception as e:
-            st.write(f"An error occurred while searching for videos: {str(e)}")
-            st.write("Sorry, I couldn't fetch videos at the moment.")
+# Mindfulness Meditation
+st.write("### 🧘 Guided Meditation")
+if st.button("Play a 5-minute Guided Meditation"):
+    meditation_text = "Focus on your breath. Inhale deeply, hold for a moment, and exhale slowly. Let go of any tension."
+    tts_meditation = gTTS(meditation_text, lang='en')
+    tts_meditation.save("meditation.mp3")
+    st.audio("meditation.mp3", format="audio/mp3")
 
-    # Crisis resources
-    crisis_keywords = ["help", "suicide", "depressed", "emergency", "hurt", "lost"]
-    if any(keyword in user_input.lower() for keyword in crisis_keywords):
-        st.warning("It seems like you might be in distress. Please reach out to a crisis hotline or a trusted individual.")
-        st.write("[Find emergency resources here](https://www.helpguide.org/find-help.htm)")
+# Interactive Storytelling
+st.write("### 📖 Short Comforting Story")
 
-    return response
+# User selects a theme for the story
+story_theme = st.selectbox("Choose a theme for your story:", ["hope", "courage", "healing", "friendship", "resilience"])
+if st.button("Generate Story"):
+    story = generate_story(story_theme)
+    st.text_area("Here’s your comforting story:", story, height=250)
+    
+    # Convert story to speech
+    tts_story = gTTS(story, lang='en')
+    tts_story.save("story.mp3")
+    st.audio("story.mp3", format="audio/mp3")
 
-# Check if the user has typed something
+# User input for conversational support
+user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)
 if user_input:
-    # Generate the empathetic response
     response = generate_response(user_input)
-    
-    # Store and show the new response
-    st.session_state.previous_responses.append(response)
     st.text_area("Bot's Response:", response, height=250)
-
-    # Text-to-speech output (optional)
+    
+    # Text-to-speech for response
     tts = gTTS(response, lang='en')
-    audio_file = "response.mp3"
-    tts.save(audio_file)
-    st.audio(audio_file, format="audio/mp3")
+    tts.save("response.mp3")
+    st.audio("response.mp3", format="audio/mp3")
+
+# Enhanced Activity Suggestions
+st.write("### 🎨 Try a New Activity")
+activities = ["journaling", "yoga", "painting", "exercise", "meditation"]
+activity_choice = st.selectbox("Pick an activity:", activities)
+if st.button("Find Videos"):
+    try:
+        search = Search(activity_choice)
+        results = search.results[:3]
+        for video in results:
+            st.write(f"[{video.title}]({video.watch_url})")
+    except Exception as e:
+        st.write("Error fetching videos.")