requirements

app.py

@@ -7,26 +7,53 @@ from torch import nn
 from PIL import Image
 from model import create_effnet_v2_model
 
+import gradio as gr
+import os
+from timeit import default_timer as timer
+
 class_names = ['Honda', 'Hyundai', 'Toyota']
 
 effnet_v2, transforms = create_effnet_v2_model(num_classes=len(class_names), weights_path="efficient_net_s_carvision_3.pth")
 
-def predict(model, image_path, device):
+def predict(image):
+
+    start_time = timer()
 
-    image = Image.open(image_path)
+    # image = Image.open(image_path)
     image = transforms(image).unsqueeze(0)
-    image = image.to(device)
-    output = model(image)
+    # image = image.to(device)
+    output = effnet_v2(image)
     
-    model.eval()
+    effnet_v2.eval()
     with torch.inference_mode():
         probs = torch.softmax(output, dim=1)
 
     pred_labels_and_probs = {class_names[i]: float(probs[0, i]) for i in range(len(class_names))}
 
+    pred_time = round(timer() - start_time, 5)
+    return pred_labels_and_probs, pred_time
+
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "CarVision 🚗🚘🚙🏎️"
+description = "An EfficientNetv2 model to classify cars as Honda, Hyundai or Toyota"
+article = "Created by Akshay Ballal"
 
-    return pred_labels_and_probs
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
 
-print(predict(effnet_v2, "examples/Toyota_Tacoma_2017_36_18_270_35_6_75_70_212_19_RWD_5_4_Pickup_xQa.jpg", torch.device("cpu")))
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
 
-# print(predict(effnet_v2, "test.jpg", torch.device("cuda:0")))
+# Launch the demo!
+demo.launch()

requirements.txt

@@ -0,0 +1,70 @@
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==5.0.1
+anyio==3.7.0
+async-timeout==4.0.2
+attrs==23.1.0
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+colorama==0.4.6
+contourpy==1.1.0
+cycler==0.11.0
+exceptiongroup==1.1.2
+fastapi==0.99.1
+ffmpy==0.3.0
+filelock==3.12.2
+fonttools==4.40.0
+frozenlist==1.3.3
+fsspec==2023.6.0
+gradio==3.35.2
+gradio_client==0.2.7
+h11==0.14.0
+httpcore==0.17.2
+httpx==0.24.1
+huggingface-hub==0.15.1
+idna==3.4
+Jinja2==3.1.2
+jsonschema==4.17.3
+kiwisolver==1.4.4
+linkify-it-py==2.0.2
+markdown-it-py==2.2.0
+MarkupSafe==2.1.3
+matplotlib==3.7.1
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+networkx==3.1
+numpy==1.25.0
+orjson==3.9.1
+packaging==23.1
+pandas==2.0.3
+Pillow==10.0.0
+pydantic==1.10.10
+pydub==0.25.1
+Pygments==2.15.1
+pyparsing==3.1.0
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3
+PyYAML==6.0
+requests==2.31.0
+semantic-version==2.10.0
+six==1.16.0
+sniffio==1.3.0
+starlette==0.27.0
+sympy==1.12
+toolz==0.12.0
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.65.0
+typing_extensions==4.7.1
+tzdata==2023.3
+uc-micro-py==1.0.2
+urllib3==2.0.3
+uvicorn==0.22.0
+websockets==11.0.3
+yarl==1.9.2