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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 | |
| state_manager = StateManager() | |
| class InferenceRunner(StateManager): | |
| def __init__(self): | |
| super().__init__() | |
| self.sample_images = [ | |
| "Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg", | |
| "Files/sample4.jpg", "Files/sample5.jpg", "Files/sample6.jpg", | |
| "Files/sample7.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 | |
| st.write("Choose from sample images:") | |
| cols = st.columns(len(self.sample_images)) | |
| for idx, sample_image_path in enumerate(self.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}'): | |
| self.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: | |
| self.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 self.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 = 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 = 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 = self.answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa) | |
| self.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 run_inference(self): | |
| st.title("Run Inference") | |
| self.initialize_state() | |
| self.set_up_widgets() | |
| st.session_state['settings_changed'] = self.has_state_changed() | |
| if st.session_state['settings_changed']: | |
| st.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" | |
| if st.session_state.method == "Fine-Tuned Model": | |
| if st.button(st.session_state.button_label): | |
| if st.session_state.button_label == "Load Model": | |
| if self.is_model_loaded(): | |
| st.text("Model already loaded and no settings were changed:)") | |
| else: | |
| self.load_model() | |
| else: | |
| self.reload_detection_model() | |
| if self.is_model_loaded() and st.session_state.kbvqa.all_models_loaded: | |
| self.image_qa_app(self.get_model()) | |
| else: | |
| st.write(f'Model using {st.session_state.method} is not deployed yet, will be ready later.') |