File size: 6,537 Bytes
125214f 4c2fc41 125214f e00ca5f 125214f c3dcca1 125214f c3dcca1 125214f ecd0257 125214f 1a4d79e 125214f 1a4d79e 125214f 1a4d79e 125214f 1a4d79e 125214f 1a4d79e 125214f 1a4d79e 125214f e00ca5f 125214f dd1c5c5 e79add6 c537d6b 7236022 e79add6 7236022 d163e0e 7236022 125214f 7236022 125214f 407f69f 7236022 e490a7c d163e0e 45ef170 125214f 45ef170 7236022 45ef170 1a4d79e 125214f 45ef170 125214f 1a4d79e 125214f 1a4d79e 125214f |
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 |
import streamlit as st
import torch
import bitsandbytes
import accelerate
import scipy
import copy
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.gen_utilities import free_gpu_resources
from my_model.KBVQA import KBVQA, prepare_kbvqa_model
from my_model.utilities.state_manager import StateManager
state_manager = StateManager()
def answer_question(caption, detected_objects_str, question, model):
free_gpu_resources()
answer = model.generate_answer(question, caption, detected_objects_str)
free_gpu_resources()
return answer
# Sample images (assuming these are paths to your sample images)
sample_images = ["Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg",
"Files/sample4.jpg", "Files/sample5.jpg", "Files/sample6.jpg",
"Files/sample7.jpg"]
def analyze_image(image, model):
img = copy.deepcopy(image) # we dont wanna apply changes to the original image
caption = model.get_caption(img)
image_with_boxes, detected_objects_str = model.detect_objects(img)
st.text("I am ready, let's talk!")
free_gpu_resources()
return caption, detected_objects_str, image_with_boxes
def image_qa_app(kbvqa):
# Display sample images as clickable thumbnails
st.write("Choose from sample images:")
cols = st.columns(len(sample_images))
for idx, sample_image_path in enumerate(sample_images):
with cols[idx]:
image = Image.open(sample_image_path)
st.image(image, use_column_width=True)
if st.button(f'Select Sample Image {idx + 1}', key=f'sample_{idx}'):
state_manager.process_new_image(sample_image_path, image, kbvqa)
# Image uploader
uploaded_image = st.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
if uploaded_image is not None:
state_manager.process_new_image(uploaded_image.name, Image.open(uploaded_image), kbvqa)
# Display and interact with each uploaded/selected image
for image_key, image_data in state_manager.get_images_data().items():
st.image(image_data['image'], caption=f'Uploaded Image: {image_key[-11:]}', use_column_width=True)
if not image_data['analysis_done']:
st.text("Cool image, please click 'Analyze Image'..")
if st.button('Analyze Image', key=f'analyze_{image_key}'):
caption, detected_objects_str, image_with_boxes = state_manager.analyze_image(image_data['image'], kbvqa)
state_manager.update_image_data(image_key, caption, detected_objects_str, True)
# Initialize qa_history for each image
qa_history = image_data.get('qa_history', [])
if image_data['analysis_done']:
question = st.text_input(f"Ask a question about this image ({image_key[-11:]}):", key=f'question_{image_key}')
if st.button('Get Answer', key=f'answer_{image_key}'):
if question not in [q for q, _ in qa_history]:
answer = answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa)
state_manager.add_to_qa_history(image_key, question, answer)
# Display Q&A history for each image
for q, a in qa_history:
st.text(f"Q: {q}\nA: {a}\n")
def process_new_image(image_key, image, kbvqa):
"""Process a new image and update the session state."""
if image_key not in st.session_state['images_data']:
st.session_state['images_data'][image_key] = {
'image': image,
'caption': '',
'detected_objects_str': '',
'qa_history': [],
'analysis_done': False
}
def run_inference():
st.title("Run Inference")
method = st.selectbox("Choose a method:", ["Fine-Tuned Model", "In-Context Learning (n-shots)"], index=0, key='method')
detection_model = st.selectbox("Choose a model for objects detection:", ["yolov5", "detic"], index=1, key='detection_model')
default_confidence = 0.2 if st.session_state['detection_model'] == "yolov5" else 0.4
# confidence_level = st.slider("Select minimum detection confidence level", min_value=0.1, max_value=0.9, value=default_confidence, step=0.1)
confidence_level = state_manager.set_slider_value(text="Select minimum detection confidence level",
min_value=0.1,
max_value=0.9,
value=default_confidence,
step=0.1,
slider_key_name='confidence_level'
)
# state_manager.update_model_settings(detection_model=detection_model, confidence_level=confidence_level, selected_method=method)
# need_model_reload = state_manager.check_settings_changed(method, detection_model, confidence_level) and state_manager.is_model_loaded()
state_manager.display_session_state()
button_label = "Reload Model" if st.session_state['kbvqa'] is not None else "Load Model"
if st.session_state.method == "Fine-Tuned Model":
if st.button(button_label):
if button_label == "Load Model" and state_manager.is_model_loaded():
# st write(st.session_state['kbvqa'])
st.write("stop playing around :):)P:)")
st.write("Model already loaded.")
else:
state_manager.load_model()
st.write("Model is ready for inference.")
if state_manager.is_model_loaded():
state_manager.display_model_settings()
state_manager.display_session_state()
image_qa_app(state_manager.get_model())
else:
st.write('Model is not ready yet, will be updated later.')
def display_model_settings():
st.write("### Current Model Settings:")
st.table(pd.DataFrame(st.session_state['model_settings'], index=[0]))
def display_session_state():
st.write("### Current Session State:")
# Convert session state to a list of dictionaries, each representing a row
data = [{'Key': key, 'Value': str(value)} for key, value in st.session_state.items()]
# Create a DataFrame from the list
df = pd.DataFrame(data)
st.table(df)
|