Create app.py

app.py

@@ -0,0 +1,75 @@
+images_dir = "images"
+import io
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from PIL import Image
+import torch
+torch.cuda.empty_cache()
+
+from fastapi import FastAPI, File, Form,UploadFile,HTTPException
+
+app=FastAPI()
+
+app.cor
+
+def run_model(image,text_input):
+    torch.cuda.empty_cache()
+    model_id= "Qwen/Qwen2-VL-7B-Instruct-AWQ"
+    model = Qwen2VLForConditionalGeneration.from_pretrained(
+        model_id , torch_dtype=torch.float16, device_map="cuda:0"
+    )
+    min_pixels = 256*28*28
+    max_pixels = 1280*28*28
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct-AWQ", min_pixels=min_pixels, max_pixels=max_pixels)
+
+    torch.cuda.empty_cache()
+    image_path = Image.open(image)
+    print(image_path)
+    messages = [
+    {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path,
+                },
+                {"type": "text", "text": text_input},
+            ],
+        }
+    ]
+   
+    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",
+    )
+    inputs = inputs.to("cuda")
+   
+    # Inference: Generation of the output
+    torch.cuda.empty_cache()
+
+    generated_ids = model.generate(**inputs, max_new_tokens=1024)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    return output_text[0]
+
+
+@app.post("/call_qwen_model")
+async def call_model(file: UploadFile = File(...),json_str: str = Form(...)):
+    try:
+        request_object_content = await file.read()
+        img = io.BytesIO(request_object_content)
+        output = run_model(img, json_str)
+        return  {"output": output}
+    except Exception as e :
+        raise HTTPException (f"Error: {e}")