Upload 3 files

app.py

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+import torch
+import streamlit as st
+from PIL import Image
+import io
+
+# Load the YOLOv5 model
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='fire.pt')  # Load custom model
+
+# Streamlit interface
+st.title("YOLOv5 Image Detection")
+st.write("Upload an image to detect objects using YOLOv5")
+
+# File uploader for image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Convert the uploaded file to a PIL Image
+    image = Image.open(uploaded_file)
+
+    # Run the YOLOv5 model
+    results = model(image)
+
+    # Save the results image
+    results_image = results.render()[0]  # Render returns a list, we take the first element
+
+    # Convert the numpy array result to an image
+    results_image = Image.fromarray(results_image)
+
+    # Save to a buffer
+    buf = io.BytesIO()
+    results_image.save(buf, format='JPEG')
+    byte_im = buf.getvalue()
+
+    # Display the input and output images side by side
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.image(results_image, caption='Detected Image', use_column_width=True)
+
+    # Provide a download button for the output image
+    st.download_button(
+        label="Download Output Image",
+        data=byte_im,
+        file_name="output.jpg",
+        mime="image/jpeg"
+    )

fire.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9be73bee589144e12981844167b545c75210aa69d60aae84c1fa1610040b31c4
+size 14443880

requirements.txt

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+# YOLOv5 requirements
+# Usage: pip install -r requirements.txt
+
+# Base ------------------------------------------------------------------------
+gitpython>=3.1.30
+matplotlib>=3.3
+numpy>=1.23.5
+opencv-python>=4.1.1
+pillow>=10.3.0
+psutil  # system resources
+PyYAML>=5.3.1
+requests>=2.32.0
+scipy>=1.4.1
+thop>=0.1.1  # FLOPs computation
+torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
+torchvision>=0.9.0
+tqdm>=4.64.0
+ultralytics>=8.2.34  # https://ultralytics.com
+# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
+
+# Logging ---------------------------------------------------------------------
+# tensorboard>=2.4.1
+# clearml>=1.2.0
+# comet
+
+# Plotting --------------------------------------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export ----------------------------------------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.10.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn<=1.1.2  # CoreML quantization
+# tensorflow>=2.4.0,<=2.13.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev>=2023.0  # OpenVINO export
+
+# Deploy ----------------------------------------------------------------------
+setuptools>=70.0.0 # Snyk vulnerability fix
+# tritonclient[all]~=2.24.0
+
+# Extras ----------------------------------------------------------------------
+# ipython  # interactive notebook
+# mss  # screenshots
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP