Update app.py

app.py

@@ -8,48 +8,58 @@ import os
 # Load pretrained models
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-# Load GPT-2 model and tokenizer for story generation
-gpt2_tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
-gpt2_model = AutoModelForCausalLM.from_pretrained("gpt2-medium")
+# Load Llama 2 model
+llama_model_name = "meta-llama/Llama-2-7b-hf"
+llama_tokenizer = AutoTokenizer.from_pretrained(llama_model_name)
+llama_model = AutoModelForCausalLM.from_pretrained(llama_model_name)
 emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", return_all_scores=True)
 
-# Function to generate a comforting story using GPT-2
 def generate_story(theme):
     # A detailed prompt for generating a comforting story about the selected theme
     story_prompt = f"Write a comforting, detailed, and heartwarming story about {theme}. The story should include a character who faces a tough challenge, finds hope, and ultimately overcomes the situation with a positive resolution."
     
-    # Generate story using GPT-2
-    input_ids = gpt2_tokenizer.encode(story_prompt, return_tensors='pt')
+    # Generate story using Llama 2
+    input_ids = llama_tokenizer.encode(story_prompt, return_tensors='pt')
     
-    story_ids = gpt2_model.generate(
+    story_ids = llama_model.generate(
         input_ids,
-        max_length=500,  # Generate longer stories
-        temperature=0.8,  # Balanced creativity
+        max_length=600,  # Generate longer stories
+        temperature=0.7,  # Balanced creativity
         top_p=0.9,
-        repetition_penalty=1.2,
+        repetition_penalty=1.1,
         num_return_sequences=1
     )
     
     # Decode the generated text
-    story = gpt2_tokenizer.decode(story_ids[0], skip_special_tokens=True)
+    story = llama_tokenizer.decode(story_ids[0], skip_special_tokens=True)
     return story
 
 
+
 # Function to generate an empathetic response
+# Function to generate an empathetic response using Llama 2
 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(
+    # Create a prompt for the Llama 2 model to generate an empathetic response
+    response_prompt = f"You are a compassionate support bot. A user has shared: '{user_input}'. Respond with empathy, understanding, and encouragement in a supportive manner."
+    
+    # Tokenize the prompt
+    input_ids = llama_tokenizer.encode(response_prompt, return_tensors='pt')
+    
+    # Generate the response using Llama 2
+    response_ids = llama_model.generate(
         input_ids,
-        max_length=300,
-        temperature=0.85,
-        top_k=50,
-        repetition_penalty=1.2,
+        max_length=300,        # Generate a longer response
+        temperature=0.7,        # Adjust temperature for coherent and empathetic responses
+        top_p=0.9,              # Use top-p sampling for balanced creativity
+        repetition_penalty=1.1, # Avoid repetitive responses
         num_return_sequences=1
     )
-    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    
+    # Decode the generated text
+    response = llama_tokenizer.decode(response_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)
@@ -83,14 +93,14 @@ with st.sidebar:
             tts.save(meditation_audio)
         st.audio(meditation_audio, format="audio/mp3")
 
-# Sidebar for additional features
-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"):
-        with st.spinner("Generating your story..."):
-            story = generate_story(story_theme)
-        st.text_area("Here's your story:", story, height=300)
+# Generating a comforting story
+st.sidebar.header("📖 Short Comforting Story")
+story_theme = st.selectbox("Choose a theme for your story:", ["courage", "healing", "hope"])
+if st.sidebar.button("Generate Story"):
+    with st.spinner("Generating your story..."):
+        story = generate_story(story_theme)
+    st.text_area("Here's your story:", story, height=300)
+
 
 
 # User input section
@@ -103,12 +113,12 @@ if 'badges' not in st.session_state:
     st.session_state.badges = []
 
 if user_input:
-    emotion = get_emotion(user_input)
     with st.spinner("Thinking..."):
         response = generate_response(user_input)
     
-    st.session_state.previous_responses.append(response)
+    # Display the bot's response
     st.text_area("Bot's Response:", response, height=250)
+
     
     # Assign motivational badges
     if emotion in ["joy", "optimism"]: