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import streamlit as st
from transformers import DetrImageProcessor, DetrForObjectDetection, pipeline
import torch
from PIL import Image
# Load the DETR model for object detection
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
detr_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
# Load an NLP model for summarization (T5-small used as an example)
summarizer = pipeline("summarization", model="t5-small")
st.title("Hassan's Project")
st.title("DETR Object Detection with NLP Summary")
st.write("Upload an image to detect objects and get a summary of what is detected.")
# File uploader in Streamlit
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
# Load and display the image
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Process the image and perform object detection
inputs = processor(images=image, return_tensors="pt")
outputs = detr_model(**inputs)
# Post-process the results to get bounding boxes and labels with a lower confidence threshold
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[0]
# Generate descriptions for detected objects
descriptions = []
st.write("Detected objects:")
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
label_text = detr_model.config.id2label[label.item()]
description = f"Detected {label_text} with confidence {round(score.item(), 2)} at location {box}."
descriptions.append(description)
st.write(description) # Display each detected object
# Combine descriptions into a single text input for the summarizer
description_text = " ".join(descriptions)
# Generate a summary using the NLP model
summary = summarizer(description_text, max_length=50, min_length=10, do_sample=False)[0]['summary_text']
# Display the summary
st.subheader("Summary")
st.write(summary) |