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import streamlit as st
import torch
import bitsandbytes
import accelerate
import scipy
from PIL import Image
import torch.nn as nn
from transformers import Blip2Processor, Blip2ForConditionalGeneration, InstructBlipProcessor, InstructBlipForConditionalGeneration
from my_model.object_detection import detect_and_draw_objects
from my_model.captioner.image_captioning import get_caption
from my_model.utilities import free_gpu_resources
# Placeholder for undefined functions
def load_caption_model():
st.write("Placeholder for load_caption_model function")
return None, None
def answer_question(image, question, model, processor):
return "Placeholder answer for the question"
def detect_and_draw_objects(image, model_name, threshold):
return image, "Detected objects"
def get_caption(image):
return "Generated caption for the image"
def free_gpu_resources():
pass
# Sample images (assuming these are paths to your sample images)
sample_images = ["path/to/sample1.jpg", "path/to/sample2.jpg", "path/to/sample3.jpg"]
# Main function
def main():
st.sidebar.title("Navigation")
selection = st.sidebar.radio("Go to", ["Home", "Dataset Analysis", "Evaluation Results", "Run Inference", "Dissertation Report", "Object Detection"])
if selection == "Home":
display_home()
elif selection == "Dissertation Report":
display_dissertation_report()
elif selection == "Evaluation Results":
display_evaluation_results()
elif selection == "Dataset Analysis":
display_dataset_analysis()
elif selection == "Run Inference":
run_inference()
elif selection == "Object Detection":
run_object_detection()
# Other display functions...
def run_inference():
st.title("Run Inference")
# Image-based Q&A and Object Detection functionality
image_qa_and_object_detection()
def image_qa_and_object_detection():
# Image-based Q&A functionality
st.subheader("Image-based Q&A")
image_qa_app()
# Object Detection functionality
st.subheader("Object Detection")
object_detection_app()
def image_qa_app():
# Initialize session state for storing images and their Q&A histories
if 'images_qa_history' not in st.session_state:
st.session_state['images_qa_history'] = []
# Button to clear all data
if st.button('Clear All'):
st.session_state['images_qa_history'] = []
st.experimental_rerun()
# Display sample images
st.write("Or choose from sample images:")
for idx, sample_image_path in enumerate(sample_images):
if st.button(f"Use Sample Image {idx+1}", key=f"sample_{idx}"):
uploaded_image = Image.open(sample_image_path)
process_uploaded_image(uploaded_image)
# Image uploader
uploaded_image = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
if uploaded_image is not None:
image = Image.open(uploaded_image)
process_uploaded_image(image)
def process_uploaded_image(image):
current_image_key = image.filename # Use image filename as a unique key
# ... rest of the image processing code ...
# Object Detection App
def object_detection_app():
# ... Implement your code for object detection ...
# Other functions...
if __name__ == "__main__":
main()
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