Init commit

README.md

@@ -1,6 +1,6 @@
 ---
 title: My Vet
-emoji: 🏃
+emoji: 🐈
 colorFrom: purple
 colorTo: pink
 sdk: gradio

app.py

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+import os
+
+import google.generativeai as genai
+import gradio as gr
+from fastai.learner import load_learner
+from PIL import Image
+
+API_KEY = os.getenv("GENAI_API_KEY")
+
+genai.configure(api_key=API_KEY)
+
+model = genai.GenerativeModel(model_name="gemini-pro-vision")
+
+learn = load_learner("pets.pkl")
+categories = learn.vocab
+
+prompt = (
+    "You are and animal expert and a veterinarian.\n"
+    "Give your expert opinion on the following questions based on"
+    "the image of the animal.\n"
+    "Only answer answer the last question.\n"
+    "Don't give anything besides the answer."
+)
+
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+
+def random_response(message, history, image=None):
+    if image is not None:
+        image = Image.fromarray(image)
+        message = "Q: " + message.strip()
+        history = history[-5:] if len(history) > 0 else ""
+        history = "\n".join([f"Q: {i[0]}\nA:{i[1]}\n" for i in history])
+        message = prompt + '\n\n' + history + '\n' + message + "\nA: "
+        print(f"The new message is : \n{message}")
+        return model.generate_content([message, image]).text
+    else:
+        return "Please provide an image."
+
+
+with gr.Blocks() as demo:
+    image = gr.Image()
+    label = gr.Label(num_top_classes=5)
+    gr.Interface(
+        classify_image,
+        inputs=image,
+        outputs=label,
+        title="Pet Classifier",
+        description="Classify an image of a pet into different categories.",
+    )
+    gr.ChatInterface(random_response, additional_inputs=[image])
+
+demo.launch()

pets.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8011b96bc737fa4b6876a4104a11ae89550a5e908e78162271b1c664165d38dd
+size 102826302

requirements.txt

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+fastai
+gradio
+timm
+google-generativeai
+torch
+torchvision