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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Initialize the chatbot model and tokenizer
model_name = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Initialize chat history
if 'chat_history_ids' not in st.session_state:
st.session_state['chat_history_ids'] = None
if 'chat_history' not in st.session_state:
st.session_state['chat_history'] = []
# Define the respond function
def respond(user_input):
new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
if st.session_state['chat_history_ids'] is None:
st.session_state['chat_history_ids'] = new_user_input_ids
else:
st.session_state['chat_history_ids'] = torch.cat([st.session_state['chat_history_ids'], new_user_input_ids], dim=-1)
# Emotional detection and response
if "happy" in user_input.lower():
response = "That's wonderful to hear! What made you feel happy today?"
elif "sad" in user_input.lower():
response = "I'm sorry to hear that. Would you like to share what's making you feel sad?"
elif "angry" in user_input.lower():
response = "It's okay to feel angry. What’s bothering you?"
elif "excited" in user_input.lower():
response = "That's great! What are you excited about?"
elif "depressed" in user_input.lower():
response = "I'm really sorry to hear that. It's important to talk about it. Would you like to share more?"
elif "stressed" in user_input.lower():
response = "Stress can be tough. What's been stressing you out?"
else:
# Generate a response from the model for general inquiries
chat_history_ids = model.generate(
st.session_state['chat_history_ids'],
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.7
)
response = tokenizer.decode(chat_history_ids[:, st.session_state['chat_history_ids'].shape[-1]:][0], skip_special_tokens=True)
# Update chat history
st.session_state['chat_history_ids'] = st.session_state['chat_history_ids']
st.session_state['chat_history'].append({"user": user_input, "bot": response})
return response
# Streamlit app layout
st.title("Emotional Support & General Knowledge Chatbot")
st.write("Hello! I'm here to support you emotionally and answer any questions. How are you feeling today?")
# Display chat history
if st.session_state['chat_history']:
for chat in st.session_state['chat_history']:
st.write(f"You: {chat['user']}")
st.write(f"Chatbot: {chat['bot']}")
# User input
user_input = st.text_input("You: ")
if st.button("Send"):
if user_input:
response = respond(user_input)
st.write(f"Chatbot: {response}")
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