Deploy model

README.md

@@ -1,13 +1,13 @@
----
-title: PyTorch-FastAI TPU
-emoji: 👀
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: PyTorch-FastAI TPU
+emoji: 👀
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.18.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,27 @@
+from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastai.vision.all import *
+
+
+repo_id = "Hitomiblood/CnnLearner_resnet34_chestXrayTPU"
+
+learner = from_pretrained_fastai(repo_id)
+labels = learner.dls.vocab
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
+def predict(img):
+    #img = PILImage.create(img)
+    pred, pred_idx, probs = learner.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# Creamos la interfaz y la lanzamos.
+lista = [
+    'NORMAL2-IM-0357-0001.jpeg',
+    'NORMAL2-IM-0370-0001.jpeg',
+    'NORMAL2-IM-0374-0001.jpeg',
+    'person18_virus_49.jpeg',
+    'person29_virus_64.jpeg',
+    'person127_bacteria_602.jpeg',
+    'person128_bacteria_605.jpeg',
+]
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=2),examples=lista).launch(share=False)