Update app.py

app.py

@@ -11,6 +11,23 @@ tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", return_all_scores=True)
 
+# Function to generate a comforting story using the pretrained model
+def generate_story(theme):
+    # Construct a prompt for generating a story based on the user's selected theme
+    story_prompt = f"Tell a comforting and inspiring story about {theme}. Make it detailed and heartwarming."
+    input_ids = tokenizer.encode(story_prompt, return_tensors='pt')
+    story_ids = model.generate(
+        input_ids,
+        max_length=350,  # Longer story length for richer content
+        temperature=0.9,  # Encourage creative storytelling
+        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
+
+
 # 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)
@@ -32,11 +49,9 @@ with st.sidebar:
     st.header("📖 Short Comforting Story")
     story_theme = st.selectbox("Choose a theme for your story:", ["courage", "healing", "hope"])
     if st.button("Generate Story"):
-        story_prompt = f"Tell me a comforting story about {story_theme}."
-        input_ids = tokenizer.encode(story_prompt, return_tensors='pt')
-        story_ids = model.generate(input_ids, max_length=150, temperature=0.8, repetition_penalty=1.1)
-        story = tokenizer.decode(story_ids[0], skip_special_tokens=True)
-        st.text_area("Here's your story:", story, height=200)
+        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...", placeholder="Type here...", max_chars=500)
@@ -46,13 +61,20 @@ if 'previous_responses' not in st.session_state:
 if 'badges' not in st.session_state:
     st.session_state.badges = []
 
-# Generate empathetic response
 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=150, temperature=0.7, top_k=50, repetition_penalty=1.2)
+    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)
@@ -80,22 +102,31 @@ if user_input:
     activities = ["exercise", "yoga", "journaling", "painting", "meditation"]
     selected_activity = st.selectbox("Pick an activity:", activities)
     
-    # Fetch YouTube video suggestions
-    if st.button("Find Videos"):
-        search = Search(selected_activity)
-        search_results = search.results[:3]
-        if not search_results:
-            st.write(f"No results found for '{selected_activity}'.")
-        else:
-            for video in search_results:
-                st.write(f"[{video.title}]({video.watch_url})")
-
-    # Crisis resources
-    if 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
+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})")
+
+
+# Crisis resources
+if 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)