Update app.py

app.py

@@ -1,9 +1,21 @@
 import os
 from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
 import streamlit as st
+import torch
+import requests
+
+def prettier(results):
+    for item in results:
+        score = round(item['score'], 3)
+        label = item['label']  # Use square brackets to access the 'label' key
+        location = [round(value, 2) for value in item['box'].values()]
+        print(f'Detected {label} with confidence {score} at location {location}')
+
+
 def input_image_setup(uploaded_file):
     if uploaded_file is not None:
-        #read te file into byte
+        #read the file into byte
         bytes_data = uploaded_file.getvalue()        
         image_parts=[
             {
@@ -18,14 +30,34 @@ def input_image_setup(uploaded_file):
 #Streamlit App
 st.set_page_config(page_title="Image Detection")
 st.header("Object Detection Application")
+#Select your model
+models = ["facebook/detr-resnet-50", "ciasimbaya/ObjectDetection", "hustvl/yolos-tiny"]  # List of supported models
+model_name = st.selectbox("Select model", models)
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForObjectDetection.from_pretrained(model_name)
+#Upload an image
 uploaded_file = st.file_uploader("choose an image...", type=["jpg","jpeg","png"])
 image=""
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True) 
+    st.image(image, caption="Uploaded Image.", use_column_width=True)
 submit = st.button("Detect Objects ")
-
 if submit:
     image_data=input_image_setup(uploaded_file)
     st.subheader("The response is..")
-    st.write(response)
+    #process with model
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # model predicts bounding boxes and corresponding COCO classes
+    logits = outputs.logits
+    bboxes = outputs.pred_boxes
+    # print results
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 2) for i in box.tolist()]
+    print(
+        f"Detected {model.config.id2label[label.item()]} with confidence "
+        f"{round(score.item(), 3)} at location {box}"
+    )