First model upload and commit

README.md

@@ -1,13 +1,12 @@
 ---
-title: Mexican Flowers
-emoji: 🐢
-colorFrom: blue
-colorTo: red
+title: Potato Or Tomato
+emoji: 💻
+colorFrom: yellow
+colorTo: purple
 sdk: gradio
-sdk_version: 3.17.0
+sdk_version: 3.9
 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 +1,42 @@
+#|export
+from fastai.vision.all import *
+import os
+import gradio as gr
+def is_flower(x): return x[0].isupper()
+
+#export
+learn = load_learner('mexicanPlants (1).pkl')
+
+
+#|export
+categories = ('Jacaranda', 'Bougainvillea', 'Clavalina tree', 'Birds of paradise plant oaxaca', 'Flame vine', 'Plumeria rubra', 'Ceiba speciosa', 'Poinsettia tree', 'Calistemo', 'Flamboyan', 'Mexican creeper','primavera rosa')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories,map(float,probs)))
+
+
+# Get the current directory
+current_dir = os.getcwd()
+
+# Get a list of all files in the directory
+all_files = os.listdir(current_dir)
+
+# Create a list of categories
+
+# Create an empty list to store the photos
+photos = []
+
+# Loop through all the files
+for file in all_files:
+    # Check if the file is a photo
+    if file.endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
+        # If it is, add it to the list of photos
+        photos.append(file)
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = photos
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

mexicanPlants (1).pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04cc7bab7e656394a4994072e0ca9bc5f60f244641c3b5629faa2fa0642fa50f
+size 87501893

mexicanPlants.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f923a1f90c78142b4bded3f33c2a8ad6e81a379f8496720eabbe251f276482c
+size 87501893

requirements.txt

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+fastai
+torch
+gradio
+numpy
+pandas