Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor,  AutoModelForCausalLM
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Inference with the pre-trained model
+checkpoint1 = "microsoft/git-base"
+processor = AutoProcessor.from_pretrained(checkpoint1)
+#model1 = AutoModelForCausalLM.from_pretrained(checkpoint1, use_auth_token=access_token)
+model1 = AutoModelForCausalLM.from_pretrained(checkpoint1)
+
+# Inference with the fine-tuned model
+checkpoint2 = "wangjin2000/git-base-finetune"
+#model2 = AutoModelForCausalLM.from_pretrained(checkpoint2, use_auth_token=access_token)
+model2 = AutoModelForCausalLM.from_pretrained(checkpoint2)
+
+def img2cap_com(image):
+    input1 = processor(images=image, return_tensors="pt").to(device)
+    pixel_values1 = input1.pixel_values
+    generated_id1 = model1.generate(pixel_values=pixel_values1, max_length=50)
+    generated_caption1 = processor.batch_decode(generated_id1, skip_special_tokens=True)[0]
+
+    input2 = processor(images=image, return_tensors="pt").to(device)
+    pixel_values2 = input2.pixel_values
+    generated_id2 = model2.generate(pixel_values=pixel_values2, max_length=50)
+    generated_caption2 = processor.batch_decode(generated_id2, skip_special_tokens=True)[0]
+    
+    return generated_caption1,generated_caption2
+
+inputs = [
+    gr.inputs.Image(type="pil", label="Original Image")
+]
+
+outputs = [
+    gr.outputs.Textbox(label="Caption from pre-trained model"),
+    gr.outputs.Textbox(label="Caption from fine-tuned model"),
+]
+
+title = "Image Captioning using Pre-trained and Fine-tuned Model"
+description = "GIT-base is used to generate Image Caption for the uploaded image."
+
+examples = [
+    ["Image1.png"],
+    ["Image2.png"],
+    ["Image3.png"],
+    ["Image4.png"],
+    ["Image5.png"],
+    ["Image6.png"]
+]
+
+gr.Interface(
+    img2cap_com,
+    inputs,
+    outputs,
+    title=title,
+    description=description,
+    examples=examples,
+    theme="huggingface",
+).launch(debug=True, enable_queue=True)