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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.utilities.gen_utilities import free_gpu_resources
from my_model.state_manager import StateManager
class InferenceRunner(StateManager):
def __init__(self):
super().__init__()
self.initialize_state()
self.sample_images = [
"Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg"]
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))
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)
# Image uploader
uploaded_image = self.col1.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
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}'):
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)
# Initialize qa_history for each image
qa_history = image_data.get('qa_history', [])
if image_data['analysis_done']:
question = nested_col22.text_input(f"Ask a question about this image ({image_key[-11:]}):", key=f'question_{image_key}')
if nested_col22.button('Get Answer', key=f'answer_{image_key}'):
if question not in [q for q, _ in qa_history]:
answer = self.answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa)
self.add_to_qa_history(image_key, question, answer)
else: nested_col22.warning("This questions has already been answered.")
# Display Q&A history for each image
for q, a in qa_history:
nested_col22.text(f"Q: {q}\nA: {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):
if st.session_state.button_label == "Load Model":
st.session_state['load_button_clicked'] = True
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"):
force_reload_full_model = True
if self.is_model_loaded() and st.session_state.kbvqa.all_models_loaded:
free_gpu_resources()
self.image_qa_app(self.get_model())
if load_fine_tuned_model:
free_gpu_resources()
self.load_model()
elif fine_tuned_model_already_loaded:
free_gpu_resources()
self.col1.text("Model already loaded and no settings were changed:)")
elif reload_detection_model:
free_gpu_resources()
self.reload_detection_model()
elif force_reload_full_model:
free_gpu_resources()
self.force_reload_model()
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.') |