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app.py

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+import gradio as gr
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/Radiology-Infer-Mini", torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("prithivMLmods/Radiology-Infer-Mini")
+
+def generate_report(image, text):
+    # Prepare the message
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": text},
+            ],
+        }
+    ]
+    
+    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("cpu")
+    
+    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_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    
+    return output_text[0]
+
+interface = gr.Interface(
+    fn=generate_report,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Enter Description/Query", placeholder="Enter your query here..."),
+    ],
+    outputs=gr.Textbox(label="Generated Report"),
+    title="Pter.AI Report Generator",
+    description="Upload a medical image and provide a description/query to generate a radiology report.",
+)
+
+interface.launch()

requirements.txt

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+transformers
+torch
+requests
+Pillow
+open_clip_torch
+ftfy
+
+gradio