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import streamlit as st |
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import torch |
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import bitsandbytes |
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import accelerate |
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import scipy |
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import copy |
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from PIL import Image |
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import torch.nn as nn |
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import pandas as pd |
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from my_model.object_detection import detect_and_draw_objects |
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from my_model.captioner.image_captioning import get_caption |
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from my_model.gen_utilities import free_gpu_resources |
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from my_model.KBVQA import KBVQA, prepare_kbvqa_model |
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from my_model.utilities.state_manager import StateManager |
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state_manager = StateManager() |
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def answer_question(caption, detected_objects_str, question, model): |
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free_gpu_resources() |
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answer = model.generate_answer(question, caption, detected_objects_str) |
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free_gpu_resources() |
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return answer |
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sample_images = ["Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg", |
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"Files/sample4.jpg", "Files/sample5.jpg", "Files/sample6.jpg", |
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"Files/sample7.jpg"] |
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def image_qa_app(kbvqa): |
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st.write("Choose from sample images:") |
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cols = st.columns(len(sample_images)) |
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for idx, sample_image_path in enumerate(sample_images): |
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with cols[idx]: |
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image = Image.open(sample_image_path) |
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st.image(image, use_column_width=True) |
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if st.button(f'Select Sample Image {idx + 1}', key=f'sample_{idx}'): |
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state_manager.process_new_image(sample_image_path, image, kbvqa) |
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uploaded_image = st.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"]) |
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if uploaded_image is not None: |
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state_manager.process_new_image(uploaded_image.name, Image.open(uploaded_image), kbvqa) |
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for image_key, image_data in state_manager.get_images_data().items(): |
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st.image(image_data['image'], caption=f'Uploaded Image: {image_key[-11:]}', use_column_width=True) |
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if not image_data['analysis_done']: |
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st.text("Cool image, please click 'Analyze Image'..") |
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if st.button('Analyze Image', key=f'analyze_{image_key}'): |
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caption, detected_objects_str, image_with_boxes = state_manager.analyze_image(image_data['image'], kbvqa) |
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state_manager.update_image_data(image_key, caption, detected_objects_str, True) |
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qa_history = image_data.get('qa_history', []) |
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if image_data['analysis_done']: |
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question = st.text_input(f"Ask a question about this image ({image_key[-11:]}):", key=f'question_{image_key}') |
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if st.button('Get Answer', key=f'answer_{image_key}'): |
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if question not in [q for q, _ in qa_history]: |
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answer = answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa) |
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state_manager.add_to_qa_history(image_key, question, answer) |
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for q, a in qa_history: |
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st.text(f"Q: {q}\nA: {a}\n") |
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def run_inference(): |
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st.title("Run Inference") |
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state_manager.initialize_state() |
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state_manager.set_up_widgets() |
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st.session_state['settings_changed'] = state_manager.has_state_changed() |
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if st.session_state['settings_changed']: |
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st.warning("Model settings have changed, please reload the model, this will take a second .. ") |
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st.session_state.button_label = "Reload Model" if state_manager.is_model_loaded() and state_manager.settings_changed else "Load Model" |
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state_manager.display_model_settings() |
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state_manager.display_session_state() |
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if st.session_state.method == "Fine-Tuned Model": |
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if st.button(st.session_state.button_label): |
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if st.session_state.button_label == "Load Model": |
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if state_manager.is_model_loaded(): |
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st.text("Model already loaded and no settings were changed:)") |
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else: state_manager.load_model() |
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else: |
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state_manager.reload_detection_model() |
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if state_manager.is_model_loaded() and st.session_state.kbvqa.all_models_loaded: |
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image_qa_app(state_manager.get_model()) |
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else: |
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st.write(f'Model using {st.session_state.method} is not deplyed yet, will be ready later.') |
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