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6646a66 f72ae3e 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 6646a66 b12fe89 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from textblob import TextBlob
import os
from huggingface_hub import login
# Hugging Face Authentication
hf_token = os.getenv("H_API_KEY")
if not hf_token:
st.error("HUGGINGFACE_TOKEN not found. Please set your Hugging Face token.")
st.stop()
login(token=hf_token)
# Load Model & Tokenizer
model_name = "meta-llama/Meta-Llama-3-8B"
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_token)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto",
use_auth_token=hf_token
)
return tokenizer, model
tokenizer, model = load_model()
# Chatbot with Sentiment
def chatbot_with_sentiment(user_input):
sentiment = TextBlob(user_input).sentiment.polarity
emotion = "😃 Positive" if sentiment > 0 else "😞 Negative" if sentiment < 0 else "😐 Neutral"
inputs = tokenizer(user_input, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=150)
generated_tokens = output[0][inputs['input_ids'].shape[-1]:]
response = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
return emotion, response
# Streamlit UI
st.title("🤖 LLaMA 3 Chatbot with Sentiment Analysis")
st.write("Ask anything, and see the sentiment of your input.")
# Clear Chat Button
if st.button("🧹 Clear Chat"):
st.session_state.messages = []
# Chat History
if "messages" not in st.session_state:
st.session_state.messages = []
for msg in st.session_state.messages:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
# User Input
user_input = st.chat_input("Type your message here...")
if user_input:
# Add user message
st.session_state.messages.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(user_input)
# Generate response
emotion, ai_response = chatbot_with_sentiment(user_input)
final_response = f"{emotion}\n\n{ai_response}"
# Add assistant message
st.session_state.messages.append({"role": "assistant", "content": final_response})
with st.chat_message("assistant"):
st.markdown(final_response)
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