Create app.py

app.py

@@ -0,0 +1,61 @@
+import requests
+import torch
+from PIL import Image
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+# Load model and processor
+model_id_or_path = "rhymes-ai/Aria"
+model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
+
+# Function to process the input and generate text
+def generate_response(image):
+    # Convert the input image to PIL format (if necessary)
+    if isinstance(image, str):
+        image = Image.open(requests.get(image, stream=True).raw)
+    
+    # Prepare messages for the model
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"text": None, "type": "image"},
+                {"text": "what is the image?", "type": "text"},
+            ],
+        }
+    ]
+
+    text = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=text, images=image, return_tensors="pt")
+    
+    # Move pixel values to the correct dtype
+    inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    # Generate response
+    with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
+        output = model.generate(
+            **inputs,
+            max_new_tokens=500,
+            stop_strings=["<|im_end|>"],
+            tokenizer=processor.tokenizer,
+            do_sample=True,
+            temperature=0.9,
+        )
+        output_ids = output[0][inputs["input_ids"].shape[1]:]
+        result = processor.decode(output_ids, skip_special_tokens=True)
+
+    return result
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.inputs.Image(type="filepath"),
+    outputs="text",
+    title="Image-to-Text Model",
+    description="Upload an image, and the model will describe it.",
+)
+
+# Launch the app
+iface.launch()