Update app.py

app.py

@@ -4,7 +4,6 @@ from gtts import gTTS
 from pytube import Search
 import random
 import os
-import time
 
 # Load pretrained models
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
@@ -13,31 +12,57 @@ emotion_classifier = pipeline("text-classification", model="bhadresh-savani/dist
 
 # Function to generate a comforting story using the pretrained model
 def generate_story(theme):
-    # A more detailed prompt for generating a story about courage
-    story_prompt = f"Tell me a detailed, comforting, and heartwarming story about {theme}. The story should include a character facing a tough challenge, showing immense courage, and ultimately overcoming it with a positive resolution. Include specific moments of struggle and inspiration."
+    story_prompt = f"Tell a detailed, comforting, and heartwarming story about {theme}. Include a character facing a tough challenge, showing courage, and overcoming it with a positive resolution."
     input_ids = tokenizer.encode(story_prompt, return_tensors='pt')
     story_ids = model.generate(
         input_ids,
-        max_length=500,  # Increase length for more detailed content
-        temperature=0.9,  # Encourage creative storytelling
+        max_length=500,
+        temperature=0.9,
         repetition_penalty=1.1,
         num_return_sequences=1
     )
-    # Decode the generated story text
     story = tokenizer.decode(story_ids[0], skip_special_tokens=True)
     return story
 
+# Function to generate an empathetic response
+def generate_response(user_input):
+    response_prompt = f"You are a compassionate support bot. A user has shared: '{user_input}'. Respond with empathy and encouragement."
+    input_ids = tokenizer.encode(response_prompt, return_tensors='pt')
+    chat_history_ids = model.generate(
+        input_ids,
+        max_length=300,
+        temperature=0.85,
+        top_k=50,
+        repetition_penalty=1.2,
+        num_return_sequences=1
+    )
+    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return 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)
 
-# Title and welcome text
 st.title("Grief and Loss Support Bot 🌿")
 st.subheader("Your compassionate companion in tough times 💚")
 
-# Sidebar for additional features
+# Sidebar for Meditation and Story Generation
 with st.sidebar:
     st.header("🧘 Guided Meditation")
     if st.button("Play Meditation"):
@@ -46,7 +71,7 @@ with st.sidebar:
             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")
-    
+
     st.header("📖 Short Comforting Story")
     story_theme = st.selectbox("Choose a theme for your story:", ["courage", "healing", "hope"])
     if st.button("Generate Story"):
@@ -57,37 +82,17 @@ with st.sidebar:
 # User input section
 user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)
 
+# 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 = []
 
-def generate_response(user_input):
-    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
-    chat_history_ids = model.generate(
-        input_ids,
-        max_length=350,  # Increase length for more detailed responses
-        temperature=0.85,  # Adjust temperature for creative responses
-        top_k=50,
-        repetition_penalty=1.2,
-        num_return_sequences=1
-    )
-    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
-    return 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']
-
-# Provide a response if user input is provided
 if user_input:
     emotion = get_emotion(user_input)
-    response = generate_response(user_input)
+    with st.spinner("Thinking..."):
+        response = generate_response(user_input)
     
-    # Display the bot's response
     st.session_state.previous_responses.append(response)
     st.text_area("Bot's Response:", response, height=250)
     
@@ -98,28 +103,18 @@ if user_input:
             st.session_state.badges.append(badge)
             st.success(f"Congratulations! You've earned a {badge}!")
     
-    # Suggest coping activities
+    # Suggest activities
     st.info("🎨 Try a New Activity")
     activities = ["exercise", "yoga", "journaling", "painting", "meditation"]
     selected_activity = st.selectbox("Pick an activity:", activities)
-    
-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
-
-if st.button("Find Videos"):
-    videos = fetch_youtube_videos(selected_activity)
-    if not videos:
-        st.write(f"No results found for '{selected_activity}'.")
-    else:
-        for title, url in videos:
-            st.write(f"[{title}]({url})")
 
+    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 any(word in user_input.lower() for word in ["suicide", "help", "depressed"]):
@@ -133,7 +128,7 @@ if user_input:
     tts.save(audio_file)
     st.audio(audio_file, format="audio/mp3")
 
-# Display badges and achievements
+# Display badges
 if st.session_state.badges:
     st.sidebar.header("🏅 Achievements")
     for badge in st.session_state.badges: