Create app.py

app.py

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+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+import torch
+
+# Load the Microsoft Phi-3.5-mini-instruct model
+model_name = "microsoft/phi-3.5-mini-instruct"
+processor = BlipProcessor.from_pretrained(model_name)
+model = BlipForConditionalGeneration.from_pretrained(model_name)
+
+# Define the image classification function
+def classify_image(image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+    pixel_values = inputs["pixel_values"]
+
+    # Generate the classification
+    with torch.no_grad():
+        generated_ids = model.generate(pixel_values=pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return generated_text
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Image Classification"),
+    title="Image Context Classification",
+    description="Upload an image to classify its context using Microsoft's Phi-3.5-mini-instruct model."
+)
+
+# Launch the interface
+iface.launch()