Create app.py

app.py

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+import os
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+import torch
+import gradio as gr
+
+# Load the model and processor
+model_name = "arjunanand13/Florence-enphase"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
+
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+torch.cuda.empty_cache()
+
+
+def predict(image, question):
+    
+    encoding = processor(image, question, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model.generate(**encoding, max_length=256)
+    answer = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+    return answer
+
+def gradio_interface(image, question):
+    
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    answer = predict(image, question)
+    return answer
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Enter your question")
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Florence-enphase Leg lift classifier",
+    description="Upload an image and ask a question about it."
+)
+
+
+iface.launch()