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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.st_utils import UIManager, StateManager
def answer_question(caption, detected_objects_str, question, model):
answer = model.generate_answer(question, caption, detected_objects_str)
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):
if 'images_data' not in st.session_state:
st.session_state['images_data'] = {}
# 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}'):
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:
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 st.session_state['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 = analyze_image(image_data['image'], kbvqa) # we can use the image_with_boxes later if we want to show it.
image_data['caption'] = caption
image_data['detected_objects_str'] = detected_objects_str
image_data['analysis_done'] = 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)
qa_history.append((question, answer))
image_data['qa_history'] = qa_history
else:
st.info("This question has already been asked.")
# 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")
st.write("Please note that this is not a general purpose model, it is specifically trained on OK-VQA dataset and is designed to give direct and short answers to the given questions.")
method = st.selectbox(
"Choose a method:",
["Fine-Tuned Model", "In-Context Learning (n-shots)"],
index=0
)
detection_model = st.selectbox(
"Choose a model for objects detection:",
["yolov5", "detic"],
index=1 # "detic" is selected by default
)
default_confidence = 0.2 if 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
)
if 'model_settings' not in st.session_state:
st.session_state['model_settings'] = {'detection_model': detection_model, 'confidence_level': confidence_level}
settings_changed = (st.session_state['model_settings']['detection_model'] != detection_model or
st.session_state['model_settings']['confidence_level'] != confidence_level)
need_model_reload = settings_changed and 'kbvqa' in st.session_state and st.session_state['kbvqa'] is not None
if need_model_reload:
st.text("Model Settings have changed, please reload the model, this will take no time :)")
button_label = "Reload Model" if need_model_reload else "Load Model"
if method == "Fine-Tuned Model":
if 'kbvqa' not in st.session_state:
st.session_state['kbvqa'] = None
if st.button(button_label):
free_gpu_resources()
if st.session_state['kbvqa'] is not None:
if not settings_changed:
st.write("Model already loaded.")
else:
free_gpu_resources()
detection_model = st.session_state['model_settings']['detection_model']
confidence_level = st.session_state['model_settings']['confidence_level']
prepare_kbvqa_model(detection_model, only_reload_detection_model=True) # only reload detection model with new settings
st.session_state['kbvqa'].detection_confidence = confidence_level
free_gpu_resources()
else:
st.text("Loading the model will take no more than a few minutes . .")
st.session_state['kbvqa'] = prepare_kbvqa_model(detection_model)
st.session_state['kbvqa'].detection_confidence = confidence_level
st.session_state['model_settings'] = {'detection_model': detection_model, 'confidence_level': confidence_level}
st.write("Model is ready for inference.")
free_gpu_resources()
if st.session_state['kbvqa']:
display_model_settings()
display_session_state()
image_qa_app(st.session_state['kbvqa'])
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)
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