add price and examples

.gitignore

@@ -0,0 +1,80 @@
+# Python build
+.eggs/
+gradio.egg-info
+dist/
+*.pyc
+__pycache__/
+*.py[cod]
+*$py.class
+build/
+__tmp/*
+*.pyi
+py.typed
+
+# JS build
+gradio/templates/*
+gradio/node/*
+gradio/_frontend_code/*
+js/gradio-preview/test/*
+
+# Secrets
+.env
+
+# Gradio run artifacts
+*.db
+*.sqlite3
+gradio/launches.json
+flagged/
+gradio_cached_examples/
+tmp.zip
+
+# Tests
+.coverage
+coverage.xml
+test.txt
+**/snapshots/**/*.png
+playwright-report/
+
+# Demos
+demo/tmp.zip
+demo/files/*.avi
+demo/files/*.mp4
+demo/all_demos/demos/*
+demo/all_demos/requirements.txt
+demo/*/config.json
+demo/annotatedimage_component/*.png
+
+# Etc
+.idea/*
+.DS_Store
+*.bak
+workspace.code-workspace
+*.h5
+
+# dev containers
+.pnpm-store/
+
+# log files
+.pnpm-debug.log
+
+# Local virtualenv for devs
+.venv*
+
+# FRP
+gradio/frpc_*
+.vercel
+
+# js
+node_modules
+public/build/
+test-results
+client/js/test.js
+.config/test.py
+
+# storybook
+storybook-static
+build-storybook.log
+js/storybook/theme.css
+
+# playwright
+.config/playwright/.cache

app.py

@@ -1,9 +1,11 @@
 from  hubconf import custom
 model = custom(path_or_model='best.pt')  # custom example
+model.eval()
 # model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True)  # pretrained example
 
 # Verify inference
 import numpy as np
+import torch
 from PIL import Image
 import gradio as gr
 
@@ -13,19 +15,47 @@ import gradio as gr
 # results = model(imgs)  # batched inference
 # results.print()
 # results.save()
+def total_price(predicted):
+    price = 0
+    for name in predicted:
+        if name == "side dish":
+            price += 10
+        elif name == "purple rice" or name == "white rice" or name == "brown rice":
+            price += 20
+        elif name == "40dollars meal":
+            price += 40
+        elif name == "30dollars meal":
+            price += 30
+        elif name == "25dollars meal":
+            price += 25
+
+    return price
+
 def predict(input_image):
     """
     Predict model output
     """
-    results = model(input_image)
+    # Disable gradient computation
+    with torch.no_grad():
+        results = model(input_image)
+
+    predicted = results.pandas().xyxy[0]["name"]
+
     output_image = results.render()[0]
-    price = "0"
+    price = total_price(predicted)
 
     # Return the output image and price
     return [output_image, price]
-    # return [input_image, price]
-
 
+with gr.Blocks() as demo:
+    # Title
+    gr.HTML(
+    """
+    <h1 align="center">Group 29 - AI Cafeteria Price Evaluator</h1>
+    """)
+    examples = ["./examples/img_1.jpg", "./examples/img_2.jpg", "./examples/img_3.jpg", "./examples/img_4.jpg"]
+    # gr.Interface(inputs=["image"],outputs=["image"],fn=lambda img:model(img).render()[0]).launch()
+    gr.Interface(inputs=["image"], outputs=["image", "text"], fn=predict, examples=examples)
 
-# gr.Interface(inputs=["image"],outputs=["image"],fn=lambda img:model(img).render()[0]).launch()
-gr.Interface(inputs=["image"], outputs=["image", "text"], fn=predict).launch()
+if __name__ == "__main__":
+    demo.launch()

best.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e649c278fc179eb0b75e136f2ea86a58a2dd84a7f77dba4fe2b93987d56e63df
-size 75247994
+oid sha256:2fdd53a30b200533858ef900c88f230ff0707ebde17309ba3c09fa9fd7cd5086
+size 74876410

best_old.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e649c278fc179eb0b75e136f2ea86a58a2dd84a7f77dba4fe2b93987d56e63df
+size 75247994

requirements.txt

@@ -1,6 +1,7 @@
 # Usage: pip install -r requirements.txt
 
 # Base ----------------------------------------
+gradio
 matplotlib>=3.2.2
 numpy>=1.18.5,<1.24.0
 opencv-python>=4.1.1