Update app.py

app.py

@@ -1,64 +1,27 @@
+from fastai.vision.all import *
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot that answers my queries like a 10 year old.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# Load the model and define categories
+categories = ['dog', 'cat']
+learn = load_learner('model.pkl')
+
+def is_cat(img):
+    pred, idx, probs = learn.predict(img)
+    return {categories[i]: float(probs[i]) for i in range(len(categories))}
+
+# Create Gradio interface components
+image = gr.components.Image()
+label = gr.components.Label()
+examples = [['dog.jpg'], ['cat.jpg']]
+
+# Create and launch the interface
+interface = gr.Interface(
+    fn=is_cat,
+    inputs=image,
+    outputs=label,
+    examples=examples,
+    title="Cat vs Dog Classifier"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()