Update app.py

app.py

@@ -1,15 +1,16 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
 from gtts import gTTS
 from pytube import Search
 import os
 import random
 
-# Initialize conversational model for empathetic dialogue
-conversational_bot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+# Initialize GPT-2 model and tokenizer from Hugging Face
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
 
-# Initialize sentiment analysis
-sentiment_analysis = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+# Create a text generation pipeline using GPT-2
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 # Set up Streamlit page
 st.set_page_config(page_title="Grief and Loss Support Bot", page_icon="🌿", layout="centered")
@@ -37,14 +38,11 @@ if 'previous_responses' not in st.session_state:
 
 # Check if user has entered text
 if user_input:
-    # Run sentiment analysis to check for distress
-    sentiment = sentiment_analysis(user_input)[0]
-
-    # Generate multiple responses with increased randomness (top_k_sampling)
-    responses = conversational_bot(user_input, max_length=300, temperature=1.0, top_k=50, num_return_sequences=3, do_sample=True)
+    # Run the text generation model to generate a response based on user input
+    generated_responses = generator(user_input, max_length=300, num_return_sequences=3, temperature=0.9)
 
     # Filter out any responses that are too similar to previous responses or user input
-    new_responses = [response['generated_text'] for response in responses]
+    new_responses = [response['generated_text'] for response in generated_responses]
     new_responses = [resp for resp in new_responses if resp.lower() not in [prev.lower() for prev in st.session_state.previous_responses] and resp.lower() != user_input.lower()]
 
     # If there are valid new responses, pick one, otherwise fallback
@@ -80,11 +78,22 @@ if user_input:
     st.audio(audio_file, format="audio/mp3")
 
     # Suggest a productive activity based on detected keywords
-    if any(keyword in user_input.lower() for keyword in ["lonely", "lost", "sad"]):
+    if any(keyword in user_input.lower() for keyword in ["lonely", "lost", "sad", "overwhelmed", "academic", "exam"]):
         st.info("Here's a suggestion to help you cope:")
-        hobbies = ["journaling", "yoga", "painting"]
-        activity = st.selectbox("Choose an activity you'd like to try:", hobbies)
-        
+
+        # Providing a variety of 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 select an activity category to suggest
+        activity = random.choice(list(activities.keys()))
+        st.write(f"How about {activity}? {activities[activity]}")
+
         # Search YouTube for videos related to the selected activity
         search = Search(activity)
         search_results = search.results[:3]  # limit results to 3 videos