Update

app.py

@@ -1,7 +1,48 @@
 import gradio as gr
+import torch
+from PIL import Image
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+from peft import PeftModel
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load model and processor (do this outside the inference function to avoid reloading)
+base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+lora_weights_path = "taesiri/BunsBunny-LLama-3.2-11B-Vision-Instruct-DummyTask2"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+processor = AutoProcessor.from_pretrained(base_model_path)
+model = MllamaForConditionalGeneration.from_pretrained(
+    base_model_path,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, lora_weights_path)
+
+def inference(image, question):
+    # Prepare input
+    messages = [
+        {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": question}]}
+    ]
+    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(image, input_text, add_special_tokens=False, return_tensors="pt").to(model.device)
+    
+    # Run inference
+    with torch.no_grad():
+        output = model.generate(**inputs, max_new_tokens=2048)
+    
+    # Decode output
+    result = processor.decode(output[0], skip_special_tokens=True)
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Enter your question")
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="Image Analysis AI",
+    description="Upload an image and ask a question about it. The AI will analyze and respond.",
+)
+
+if __name__ == "__main__":
+    demo.launch()