File size: 6,328 Bytes
484e942 |
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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
import os
import torch
from accelerate import Accelerator
from PIL import Image
import random
import requests
import streamlit as st
from transformers import BlipProcessor, BlipForConditionalGeneration
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
# Define the model IDs
llm_model_id = "mistralai/Mistral-7B-Instruct-v0.3"
blip_model_id = "Salesforce/blip-image-captioning-large"
# Initialize BLIP processor and model
processor = BlipProcessor.from_pretrained(blip_model_id)
model = BlipForConditionalGeneration.from_pretrained(blip_model_id)
# Initialize the accelerator
accelerator = Accelerator()
def get_llm_hf_inference(model_id=llm_model_id, max_new_tokens=128, temperature=0.1):
try:
llm = HuggingFaceEndpoint(
repo_id=model_id,
max_new_tokens=max_new_tokens,
temperature=temperature,
token=os.getenv("HF_TOKEN")
)
except Exception as e:
st.error(f"Error loading model: {e}")
llm = None
return llm
def generate_caption(image, min_len=30, max_len=100):
try:
inputs = processor(image, return_tensors="pt")
out = model.generate(**inputs, min_length=min_len, max_length=max_len)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
except Exception as e:
st.error(f"Error generating caption: {e}")
return 'Unable to generate caption.'
# Configure the Streamlit app
st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
st.title("Personal HuggingFace ChatBot")
st.markdown(f"*This is a simple chatbot using the HuggingFace transformers library with {llm_model_id}.*")
# Initialize session state
if "avatars" not in st.session_state:
st.session_state.avatars = {'user': None, 'assistant': None}
if 'user_text' not in st.session_state:
st.session_state.user_text = None
if "max_response_length" not in st.session_state:
st.session_state.max_response_length = 256
if "system_message" not in st.session_state:
st.session_state.system_message = "friendly AI conversing with a human user"
if "starter_message" not in st.session_state:
st.session_state.starter_message = "Hello, there! How can I help you today?"
if "uploaded_image_path" not in st.session_state:
st.session_state.uploaded_image_path = None
# Sidebar for settings
with st.sidebar:
st.header("System Settings")
st.session_state.system_message = st.text_area(
"System Message", value="You are a friendly AI conversing with a human user."
)
st.session_state.starter_message = st.text_area(
'First AI Message', value="Hello, there! How can I help you today?"
)
st.session_state.max_response_length = st.number_input(
"Max Response Length", value=128
)
st.markdown("*Select Avatars:*")
col1, col2 = st.columns(2)
with col1:
st.session_state.avatars['assistant'] = st.selectbox(
"AI Avatar", options=["🤗", "💬", "🤖"], index=0
)
with col2:
st.session_state.avatars['user'] = st.selectbox(
"User Avatar", options=["👤", "👱♂️", "👨🏾", "👩", "👧🏾"], index=0
)
reset_history = st.button("Reset Chat History")
# Initialize or reset chat history
if "chat_history" not in st.session_state or reset_history:
st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
def get_response(system_message, chat_history, user_text, max_new_tokens=256):
hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
if hf is None:
return "Error with model inference.", chat_history
prompt = PromptTemplate.from_template(
"[INST] {system_message}\nCurrent Conversation:\n{chat_history}\n\nUser: {user_text}.\n [/INST]\nAI:"
)
chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
response = response.split("AI:")[-1]
chat_history.append({'role': 'user', 'content': user_text})
chat_history.append({'role': 'assistant', 'content': response})
return response, chat_history
# Chat interface
chat_interface = st.container()
with chat_interface:
output_container = st.container()
# Image upload and captioning
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_image and st.session_state.uploaded_image_path is None:
# Save the uploaded image to a session-local directory
with st.spinner("Processing image... 0%"):
image = Image.open(uploaded_image).convert("RGB")
# Create a directory for session images if not exists
if not os.path.exists("session_images"):
os.makedirs("session_images")
# Save image to local session directory
image_path = os.path.join("session_images", uploaded_image.name)
image.save(image_path)
# Generate and save caption
caption = generate_caption(image)
st.session_state.chat_history.append({'role': 'user', 'content': f''})
st.session_state.chat_history.append({'role': 'assistant', 'content': caption})
st.spinner("Processing image... 100%")
st.session_state.user_text = st.chat_input(placeholder="Enter your text here.")
if st.session_state.user_text:
with st.chat_message("user", avatar=st.session_state.avatars['user']):
st.markdown(st.session_state.user_text)
with st.chat_message("assistant", avatar=st.session_state.avatars['assistant']):
response, st.session_state.chat_history = get_response(
system_message=st.session_state.system_message,
chat_history=st.session_state.chat_history,
user_text=st.session_state.user_text,
max_new_tokens=st.session_state.max_response_length
)
st.markdown(response)
st.spinner("Thinking... 100%")
|