changed scrip and added model

app.py

@@ -1,8 +1,66 @@
-#victor
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!"
+# Define the list of 1st generation Pokémon class names
+class_names = [
+    "Bulbasaur", "Ivysaur", "Venusaur",
+    "Charmander", "Charmeleon", "Charizard",
+    "Squirtle", "Wartortle", "Blastoise",
+    "Caterpie", "Metapod", "Butterfree",
+    "Weedle", "Kakuna", "Beedrill",
+    "Pidgey", "Pidgeotto", "Pidgeot",
+    "Rattata", "Raticate", "Spearow",
+    "Fearow", "Ekans", "Arbok",
+    "Psyduck", "Golduck", "Machop",
+    "Machoke", "Machamp", "Bellsprout",
+    "Weepinbell", "Victreebel", "Tentacool",
+    "Tentacruel", "Geodude", "Graveler",
+    "Golem", "Ponyta", "Rapidash",
+    "Magnemite", "Magneton", "Krabby",
+    "Kingler", "Exeggcute", "Exeggutor",
+    "Cubone", "Marowak", "Hitmonlee",
+    "Hitmonchan", "Lickitung", "Koffing",
+    "Weezing", "Rhyhorn", "Rhydon",
+    "Chansey", "Tangela", "Kangaskhan",
+    "Horsea", "Seadra", "Goldeen",
+    "Seaking", "Staryu", "Starmie",
+    "Mr. Mime", "Scyther", "Jynx",
+    "Electabuzz", "Magmar", "Pinsir",
+    "Tauros", "Magikarp", "Gyarados",
+    "Lapras", "Ditto", "Eevee",
+    "Vaporeon", "Jolteon", "Flareon",
+    "Porygon", "Omanyte", "Omastar",
+    "Kabuto", "Kabutops", "Aerodactyl",
+    "Mew", "Mewtwo"
+]
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def get_x(item):
+    return item['image']  # Access images directly
+
+def get_y(item):
+    return class_names[item['labels']]  # Map label index to class name
+
+# Load the model
+learn = load_learner('poke_model.pkl', cpu=False)  # Ensure correct path to poke_model.pkl
+
+# Categories are derived from the vocabulary of the dataloader used during training
+categories = learn.dls.vocab  # These are the class labels used in the trained model
+
+# Define the function for prediction
+def classify_pokemon(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# Gradio interface setup
+image = gr.inputs.Image(shape=(128, 128))  # Image size should match your training data size (e.g., 128x128)
+label = gr.outputs.Label()
+
+# Set up Gradio interface
+intf = gr.Interface(
+    fn=classify_pokemon,
+    inputs=image,
+    outputs=label,
+)
+
+# Launch the app
+intf.launch(share=True)  # share=True allows you to share the interface via a public link

poke_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df8a6e0f7870af2eb90b90b464009dd87dd703b2f99c0069346be2c234b63df1
+size 87794542