Create app.py with inference

BrainMRI Radiology Expert: A Qwen2-VL-2B-Instruct model fine tuned on brain mri images from spr-serena/mri_scans_labelled.
It can receive a 2D brain scan slice or image and provide a brief description of the image.

app.py

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+# #Inference using gradio
+from peft import PeftModel
+from transformers import Qwen2VLForConditionalGeneration
+from transformers import AutoProcessor
+
+#load the base model and finetuned adapter
+base_model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+model = PeftModel.from_pretrained(base_model, "vignesha7/qwen2-2b-instruct-Brain-MRI-Description")
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+
+#inference function
+def generate_description(sample):
+    system_message = "You are an expert MRI radiographer. you can describe what you see in the mri image"
+
+    prompt = "Describe accurately what you see in this radiology image."
+    messages = [
+        {  "role": "system",
+           "content": [{"type": "text", "text": system_message}]
+         },
+        { "role": "user",
+          "content" : [
+            {"type" : "text",  "text"  : prompt},
+            {"type" : "image", "image" : sample}]
+        },
+    ]
+    # Preparation for inference
+    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(model.device)
+    # Inference: Generation of the output
+    generated_ids = model.generate(**inputs, max_new_tokens=256, top_p=1.0, do_sample=True, temperature=0.8)
+    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]
+
+### Gradio app ###
+title = "BrainMRI Radiology Expert"
+description = "An Qwen2-VL-2B-Instruct model fine tuned on brain mri images.Describes the brain image"
+
+demo = gr.Interface(
+    fn=generate_description,
+    inputs=gr.Image(type='pil'),
+    outputs='text',
+    title=title,
+    description=description,    
+)
+
+demo.launch()
+