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}', 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 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}', on_click=self.disable_widgets, disabled=self.is_widget_disabled): if question not in [q for q, _ in qa_history]: 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) else: nested_col22.warning("This questions has already been answered.") st.session_state['loading_in_progress'] = False # 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, 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: free_gpu_resources() self.load_model() 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() self.force_reload_model() st.session_state['loading_in_progress'] = False 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(): st.session_state['loading_in_progress'] = False free_gpu_resources() self.image_qa_app(self.get_model())