Update app.py

app.py

@@ -1,10 +1,8 @@
 import streamlit as st
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from gtts import gTTS
-import torch
-import tempfile
-import random
 from pytube import Search
+import random
 
 # Load DialoGPT model and tokenizer from Hugging Face
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
@@ -27,85 +25,77 @@ st.markdown("""
 st.title("Grief and Loss Support Bot 🌿")
 st.subheader("Your compassionate companion in tough times 💚")
 
-# Initialize session state for chat history
-if 'chat_history_ids' not in st.session_state:
-    st.session_state.chat_history_ids = None
-if 'bot_messages' not in st.session_state:
-    st.session_state.bot_messages = []
-
 # User input
 user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)
 
-# Function to generate a response using DialoGPT
-def generate_response(user_input):
-    if not user_input:
-        return "I'm here whenever you're ready to talk."
+# Store previous responses to check for repetition
+if 'previous_responses' not in st.session_state:
+    st.session_state.previous_responses = []
 
-    # Encode the user input and append to chat history
+# Function to generate a more empathetic and focused response using DialoGPT
+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 if st.session_state.chat_history_ids is None else torch.cat([st.session_state.chat_history_ids, new_user_input_ids], dim=-1)
-
-    # Generate the bot's response
-    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
-    )
-
-    # Update the chat history
-    st.session_state.chat_history_ids = chat_history_ids
-    bot_response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
-
-    bot_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 overwhelming."
-
-    return bot_response
-
-# Generate and display bot response if there's user input
-if user_input:
-    response = generate_response(user_input)
-    st.session_state.bot_messages.append(response)
-
-    # Display bot's response
-    for message in st.session_state.bot_messages:
-        st.text_area("Bot's Response:", message, height=150)
-
-    # Text-to-speech output
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
-        tts = gTTS(response, lang='en')
-        tts.save(tmp_file.name)
-        st.audio(tmp_file.name, format="audio/mp3")
-
-    # Activity suggestion based on detected keywords
-    if any(keyword in user_input.lower() for keyword in ["lonely", "lost", "sad", "overwhelmed", "academic", "exam"]):
+    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."
+
+    # 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:")
 
-        # Activities based on user mood and needs
-        activities = {
-            "journaling": "Express your feelings in writing. Journaling is a great way to process emotions.",
-            "yoga": "Yoga helps you relax and refocus. Try some deep breathing exercises or light stretching.",
-            "painting": "Creative expression through painting or drawing can be soothing and help you release pent-up emotions.",
-            "meditation": "Take a moment to calm your mind. Guided meditation can help reduce stress and anxiety.",
-            "exercise": "Physical activity can lift your mood. Even a short walk in nature can make a big difference."
-        }
-
-        # Randomly suggest an activity
-        activity = random.choice(list(activities.keys()))
-        st.write(f"How about {activity}? {activities[activity]}")
-
-        # Search YouTube for related videos using pytube
-        search = Search(activity)
-        search_results = search.results[:3]  # Limit results to 3 videos
-        if search_results:
-            for video in search_results:
-                st.write(f"[{video.title}]({video.watch_url})")
-        else:
-            st.write("Sorry, I couldn't find any relevant videos at the moment.")
-
-    # Crisis resources for distress signals
+        # 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.")
+
+    # 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)")
+
+    return response
+
+# Check if the user has typed something
+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)
+    tts = gTTS(response, lang='en')
+    audio_file = "response.mp3"
+    tts.save(audio_file)
+    st.audio(audio_file, format="audio/mp3")