Add finetuned model and app

convnext_base_finetuned.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c8dadf0c017fd3749a0dc291a1d9249bdba618c6351964d1fe65a85c07a578b
+size 350500802

model.py

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+import torch
+import gradio as gr
+from transformers import ConvNextForImageClassification, AutoImageProcessor
+from PIL import Image
+
+# Load model configuration and weights manually
+model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224")  # Load the base model
+model.load_state_dict(torch.load("convnext_base_finetuned.pth", map_location="cpu"))  # Load your finetuned weights
+model.eval()
+
+# Load the processor
+processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224")
+
+# Define a function to predict the class from an image
+def predict(image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    
+    return predicted_class
+
+# Create Gradio interface for user input
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Textbox())
+
+iface.launch()

requirements.txt

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+torch==1.13.1
+transformers==4.27.0
+gradio==3.0.1