Update app.py

app.py

@@ -1,60 +1,43 @@
-import os
-import io
-from PIL import Image,ImageDraw
-from transformers import AutoImageProcessor, AutoModelForObjectDetection
-import streamlit as st
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image, ImageDraw
 import torch
+import gradio as gr
 import requests
+from io import BytesIO
 
-def input_image_setup(uploaded_file):
-    if uploaded_file is not None:
-        bytes_data = uploaded_file.getvalue()
-        image = Image.open(io.BytesIO(bytes_data))  # Convert bytes data to PIL image
-        return image
-    else:
-        raise FileNotFoundError("No file uploaded")
-
-#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","microsoft/table-transformer-detection","valentinafeve/yolos-fashionpedia"]  # 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)
-submit = st.button("Detect Objects ")
-if submit:
-    image_data = input_image_setup(uploaded_file)
-    st.subheader("The response is..")
-    inputs = processor(images=image, return_tensors="pt")
+# Load pre-trained DETR model
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+# COCO class index for "person" = 1 (used as proxy for face detection)
+FACE_CLASS_INDEX = 1
+
+def detect_faces(img: Image.Image):
+    # Prepare input for the model
+    inputs = processor(images=img, return_tensors="pt")
     outputs = model(**inputs)
 
-    logits = outputs.logits
-    bboxes = outputs.pred_boxes
-    
-    target_sizes = torch.tensor([image.size[::-1]])
-    results = processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
+    # Get outputs
+    target_sizes = torch.tensor([img.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
 
-    # Draw bounding boxes on the image
-    drawn_image = image.copy()
-    draw = ImageDraw.Draw(drawn_image)
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        box = [int(i) for i in box.tolist()]
-        draw.rectangle(box, outline="red", width=2)
-        label_text = f"{model.config.id2label[label.item()]} ({round(score.item(), 2)})"
-        draw.text((box[0], box[1]), label_text, fill="red")
-    
-    st.image(drawn_image, caption="Detected Objects", use_column_width=True)
-    st.subheader("List of Objects:")
+    # Draw bounding boxes
+    draw = ImageDraw.Draw(img)
     for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        box = [round(i, 2) for i in box.tolist()]
-        st.write(
-            f"Detected :orange[{model.config.id2label[label.item()]}] with confidence "
-            f":green[{round(score.item(), 3)}] at location :violet[{box}]"
-        )
+        if label.item() == FACE_CLASS_INDEX:  # 'person'
+            box = [round(i, 2) for i in box.tolist()]
+            draw.rectangle(box, outline="green", width=3)
+            draw.text((box[0], box[1]), f"{score:.2f}", fill="green")
+
+    return img
+
+# Gradio interface
+iface = gr.Interface(
+    fn=detect_faces,
+    inputs=gr.Image(type="pil"),
+    outputs="image",
+    title="Face Detection App (Hugging Face + Gradio)",
+    description="Upload an image and detect faces using facebook/detr-resnet-50 model."
+)
+
+iface.launch()