First Commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+effnetb1.pth filter=lfs diff=lfs merge=lfs -text

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/Coffee_Bean_Classifier.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Coffee_Bean_Classifier.iml" filepath="$PROJECT_DIR$/.idea/Coffee_Bean_Classifier.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

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+import os, torch, torchvision, torchvision
+import gradio as gr
+
+from model import build_effnetb1
+from typing import Dict
+from pathlib import Path
+
+# Define Class names
+class_names = ["Dark", "Medium", "Light", "Green"]
+
+# Load path for example photo list
+exp_list = list(Path("examples/").glob("*.png"))
+
+# Build and load model params
+model, transforms = build_effnetb1()
+model_path = "effnetb1.pth"
+model.load_state_dict(torch.load(model_path,
+                                 map_location=torch.device("cpu")))
+
+
+# Predict based on image given | move everything to device("cpu") / Spaces run on CPU
+def predict(img) -> Dict:
+    # move model to cpu
+    model.to("cpu")
+    model.eval()
+    with torch.inference_mode():
+        transformed_image = transforms(img).unsqueeze(dim=0)
+        # move input to cpu
+        target_image_pred = model(transformed_image.to("cpu"))
+
+    target_image_pred_probs = torch.softmax(target_image_pred, dim=1)
+    print(target_image_pred_probs)
+    pred_labels_and_probs = {class_names[i]: float(target_image_pred_probs[0][i]) for i in range(len(class_names))}
+
+    return pred_labels_and_probs
+
+
+# Gradio App
+title = "Coffee Bean Multi-classifier based on level of roasting ☕️"
+description = """Created from multi-classifier model using transfer learning from [EfficientNetB1](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b1.html).
+                Model was trained on 10 epochs on default weights, and demonstrated a testing accuarcy of 98%.\n Further information and the source code is provided at my [Github Repo](https://github.com/sehyunlee217/coffee_bean_multi_classification).
+                \n\n There are four roasting levels: Green and lightly roasted coffee beans are Laos Typica Bolaven. Doi Chaang are the medium roasted, and Brazil Cerrado are dark roasted. All coffee beans are Arabica beans.\n"""
+article = "Dataset from: Ontoum, S., Khemanantakul, T., Sroison, P., Triyason, T., & Watanapa, B. (2022). Coffee Roast Intelligence. arXiv preprint arXiv:2206.01841."
+
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=4, label="Predictions")],
+                    examples=exp_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+demo.launch()

effnetb1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95bb93daa3af27fcbcfa78fa9ec0759eaa064bdeeeeca423cce8bd0b7b206daa
+size 26504890

model.py

@@ -0,0 +1,23 @@
+import torch, torchvision
+from torch import nn
+
+def build_effnetb1():
+    # weight & model initialization
+    effnetb1_weights = torchvision.models.EfficientNet_B1_Weights.DEFAULT
+    effnetb1 = torchvision.models.efficientnet_b1(weights=effnetb1_weights)
+    effnetb1_transforms = effnetb1_weights.transforms()
+    effnetb1.name = "effnetb1"
+
+    # Freeze params
+    for params in effnetb1.parameters():
+        params.requires_grad = False
+
+    # Edit Classifiers
+    effnetb1.classifier = torch.nn.Sequential(
+        nn.Dropout(p=0, inplace=True),
+        nn.Linear(in_features=1280,
+                  out_features=4,
+                  bias=True).to("cpu")
+    )
+
+    return effnetb1, effnetb1_transforms

requirements.txt

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+torch==2.3.1
+torchvision==0.18.1
+gradio==4.37.2
+httpx==0.24.1
+numpy==1.26.4