add everyhting

app.py

@@ -1,7 +1,44 @@
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn = load_learner("model.pkl")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+labels = learn.dls.vocab
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+
+examples = []
+for t in templates:
+    for n in range(1,4):
+         examples.append(f"campanelle/{n}.jpg")
+         examples.append(f"macaroni/{n}.jpg")
+         examples.append(f"fusilli/{n}.jpg")
+
+title = "Pasta Recognition"
+
+description = """
+Pasta is delicious, but there are too many pasta types to remember. This app will help you
+recall the name of the pasta whose image you'll upload.
+
+Under the hood it's a pasta classifier, built using Fast.ai by fine-tuning the
+`convnext_base_in22ft1k` model. It supports only the followig pasta types:
+
+* Campanelle
+* Ditalini
+* Fettuccine
+* Fusilli
+* Linguine
+* Macaroni
+* Pappardelle
+
+The model's accuracy is ~82%.
+
+The notebook used to train the model can be seen [in Google Colab](https://colab.research.google.com/drive/1NM6BvRkV7xDlnncFqVRGxxg0GFtqpVgT?usp=sharing).
+"""
+
+iface = gr.Interface(fn=classify_image, inputs=image, outputs=gr.outputs.Label(), examples=examples, title=title, description=description)
+iface.launch(inline=False)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0576c8d7c57113c5f23dc3028fb900345e2a02bca7e6453f7f12ab76f19e4ad3
+size 114641385