Create app.py

app.py

@@ -0,0 +1,50 @@
+"""expose an endpoint that accepts image_url and prompt, runs inference with Qwen2.5-VL, and returns a text response"""
+
+from fastapi import FastAPI, Query
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+app = FastAPI()
+
+checkpoint = "Qwen/Qwen2.5-VL-3B-Instruct"
+min_pixels = 256*28*28
+max_pixels = 1280*28*28
+processor = AutoProcessor.from_pretrained(
+    checkpoint,
+    min_pixels=min_pixels,
+    max_pixels=max_pixels
+)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    checkpoint,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    # attn_implementation="flash_attention_2",
+)
+
+@app.get("/")
+def read_root():
+    return {"message": "API is live. Use the /predict endpoint."}
+
+@app.get("/predict")
+def predict(image_url: str = Query(...), prompt: str = Query(...)):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant with vision abilities."},
+        {"role": "user", "content": [{"type": "image", "image": image_url}, {"type": "text", "text": prompt}]},
+    ]
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    ).to(model.device)
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+    output_texts = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    return {"response": output_texts[0]}