File size: 8,871 Bytes
125214f 7e54217 125214f 1812270 eff41fa 40d77a8 125214f 1948116 eaa7a81 1948116 eaa7a81 40d77a8 1948116 d4b85b8 fc89ea0 1948116 5667733 29f316e 1948116 80c9355 fc89ea0 80c9355 1948116 d4b85b8 80c9355 1948116 7316948 0bac0de 180f51e 8cedf13 824fe45 522b5f3 824fe45 2b70006 522b5f3 8cedf13 c791f22 40d77a8 8cedf13 bb17a59 1948116 3394a6e 1948116 8b8fe48 3394a6e 1948116 40d77a8 2fdc9bb 8b8fe48 3b61686 1948116 7c8c861 6a338ab 11781a6 1948116 5600c91 16b6309 5600c91 3a20d92 fc89ea0 5600c91 3394a6e c791f22 5600c91 3394a6e 3db717c 3394a6e 3a20d92 3394a6e fc89ea0 3394a6e c45c1b1 fc89ea0 904c909 8b8fe48 c791f22 904c909 802de9d c791f22 904c909 c791f22 6b844f6 904c909 802de9d fc89ea0 904c909 8b8fe48 c791f22 7e54217 3394a6e 6b844f6 c791f22 6b844f6 3b0e897 6b844f6 7e54217 6b844f6 fc89ea0 6b844f6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
import streamlit as st
import torch
import bitsandbytes
import accelerate
import scipy
import copy
import time
from PIL import Image
import torch.nn as nn
import pandas as pd
from my_model.object_detection import detect_and_draw_objects
from my_model.captioner.image_captioning import get_caption
from my_model.utilities.gen_utilities import free_gpu_resources
from my_model.state_manager import StateManager
from my_model.config import inference_config as config
class InferenceRunner(StateManager):
def __init__(self):
super().__init__()
self.initialize_state()
self.sample_images = config.SAMPLE_IMAGES
def answer_question(self, caption, detected_objects_str, question, model):
free_gpu_resources()
answer = model.generate_answer(question, caption, detected_objects_str)
free_gpu_resources()
return answer
def image_qa_app(self, kbvqa):
# Display sample images as clickable thumbnails
self.col1.write("Choose from sample images:")
cols = self.col1.columns(len(self.sample_images))
st.write('loading in prog?', st.session_state['loading_in_progress'])
for idx, sample_image_path in enumerate(self.sample_images):
with cols[idx]:
image = Image.open(sample_image_path)
image_for_display = self.resize_image(sample_image_path, 80, 80)
st.image(image_for_display)
if st.button(f'Select Sample Image {idx + 1}', key=f'sample_{idx}'):
self.process_new_image(sample_image_path, image, kbvqa)
st.write('loading in prog?',st.session_state['loading_in_progress'])
# Image uploader
uploaded_image = self.col1.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
st.write(st.session_state['loading_in_progress'])
if uploaded_image is not None:
self.process_new_image(uploaded_image.name, Image.open(uploaded_image), kbvqa)
# Display and interact with each uploaded/selected image
with self.col2:
for image_key, image_data in self.get_images_data().items():
with st.container():
nested_col21, nested_col22 = st.columns([0.65, 0.35])
image_for_display = self.resize_image(image_data['image'], 600)
nested_col21.image(image_for_display, caption=f'Uploaded Image: {image_key[-11:]}')
if not image_data['analysis_done']:
nested_col22.text("Please click 'Analyze Image'..")
with nested_col22:
if st.button('Analyze Image', key=f'analyze_{image_key}', on_click=self.disable_widgets, disabled=self.is_widget_disabled):
caption, detected_objects_str, image_with_boxes = self.analyze_image(image_data['image'], kbvqa)
self.update_image_data(image_key, caption, detected_objects_str, True)
st.session_state['loading_in_progress'] = False
# Initialize qa_history for each image
qa_history = image_data.get('qa_history', [])
if image_data['analysis_done']:
st.session_state['loading_in_progress'] = False
sample_questions = config.SAMPLE_QUESTIONS.get(image_key, [])
selected_question = nested_col22.selectbox(
"Select a sample question or type your own:",
["Custom question..."] + sample_questions,
key=f'sample_question_{image_key}')
# Text input for custom question
custom_question = nested_col22.text_input(
"Or ask your own question:",
key=f'custom_question_{image_key}')
# Use the selected sample question or the custom question
question = custom_question if selected_question == "Custom question..." else selected_question
if not question:
nested_col22.warning("Please select or enter a question.")
else:
if question in [q for q, _ in qa_history]:
nested_col22.warning("This question has already been answered.")
else:
if nested_col22.button('Get Answer', key=f'answer_{image_key}', disabled=self.is_widget_disabled):
answer = self.answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa)
st.session_state['loading_in_progress'] = False
self.add_to_qa_history(image_key, question, answer)
# Display Q&A history for each image
for num, (q, a) in enumerate(qa_history):
nested_col22.text(f"Q{num+1}: {q}\nA{num+1}: {a}\n")
def display_message(self, message, warning=False, write=False, text=False):
pass
def run_inference(self):
self.set_up_widgets()
load_fine_tuned_model = False
fine_tuned_model_already_loaded = False
reload_detection_model = False
force_reload_full_model = False
st.session_state['settings_changed'] = self.has_state_changed()
if st.session_state['settings_changed']:
self.col1.warning("Model settings have changed, please reload the model, this will take a second .. ")
st.session_state.button_label = "Reload Model" if self.is_model_loaded() and self.settings_changed else "Load Model"
with self.col1:
if st.session_state.method == "Fine-Tuned Model":
with st.container():
nested_col11, nested_col12 = st.columns([0.5, 0.5])
if nested_col11.button(st.session_state.button_label, on_click=self.disable_widgets, disabled=self.is_widget_disabled):
if st.session_state.button_label == "Load Model":
if self.is_model_loaded():
free_gpu_resources()
fine_tuned_model_already_loaded = True
else:
load_fine_tuned_model = True
else:
reload_detection_model = True
if nested_col12.button("Force Reload", on_click=self.disable_widgets, disabled=self.is_widget_disabled):
force_reload_full_model = True
if load_fine_tuned_model:
t1=time.time()
free_gpu_resources()
self.load_model()
st.session_state['time_taken_to_load_model'] = int(time.time()-t1)
st.session_state['loading_in_progress'] = False
elif fine_tuned_model_already_loaded:
free_gpu_resources()
self.col1.text("Model already loaded and no settings were changed:)")
st.session_state['loading_in_progress'] = False
elif reload_detection_model:
free_gpu_resources()
self.reload_detection_model()
st.session_state['loading_in_progress'] = False
elif force_reload_full_model:
free_gpu_resources()
t1=time.time()
self.force_reload_model()
st.session_state['time_taken_to_load_model'] = int(time.time()-t1)
st.session_state['loading_in_progress'] = False
st.session_state['model_loaded'] = True
elif st.session_state.method == "In-Context Learning (n-shots)":
self.col1.warning(f'Model using {st.session_state.method} is not deployed yet, will be ready later.')
st.session_state['loading_in_progress'] = False
if self.is_model_loaded():
free_gpu_resources()
st.session_state['loading_in_progress'] = False
self.image_qa_app(self.get_model())
st.write('load success', 'loading in prog?', st.session_state['loading_in_progress'])
|