Create app.py

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLProcessor
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+MODEL_ID = "Qwen/Qwen2.5-VL-3B-Instruct"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+DEFAULT_PROMPT = (
+    "Do you see any abnormality in the chest? Write briefly. "
+    "If yes, also tell where the abnormality is in which part of the chest. "
+    "The chest parts include lungs, heart and vessels, spine, diaphragm, "
+    "soft tissues, Mediastinum and bones of chest shown in image. "
+    "Respond only in English. Do NOT use any other language. "
+    "**Do not use Chinese language.**"
+)
+
+# ---------------------------------------------------------------------------
+# Load model & processor
+# ---------------------------------------------------------------------------
+print(f"Loading model: {MODEL_ID}")
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    torch_dtype=DTYPE,
+    device_map="auto" if torch.cuda.is_available() else None,
+)
+if not torch.cuda.is_available():
+    model = model.to(DEVICE)
+
+# Skip video_processor attribute to avoid torchvision dependency
+_orig_attrs = Qwen2_5_VLProcessor.attributes[:]
+Qwen2_5_VLProcessor.attributes = [a for a in _orig_attrs if a != "video_processor"]
+processor = Qwen2_5_VLProcessor.from_pretrained(MODEL_ID)
+Qwen2_5_VLProcessor.attributes = _orig_attrs
+print("Model loaded successfully.")
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+def pad_to_square(image: Image.Image) -> Image.Image:
+    width, height = image.size
+    if width == height:
+        return image
+    max_dim = max(width, height)
+    new_image = Image.new("RGB", (max_dim, max_dim), (0, 0, 0))
+    new_image.paste(image, ((max_dim - width) // 2, (max_dim - height) // 2))
+    return new_image
+
+
+# ---------------------------------------------------------------------------
+# Inference
+# ---------------------------------------------------------------------------
+def predict(image: Image.Image, prompt: str, max_new_tokens: int, temperature: float):
+    if image is None:
+        return "Please upload a chest X-ray image."
+
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    image = pad_to_square(image)
+
+    if not prompt.strip():
+        prompt = DEFAULT_PROMPT
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = processor(
+        text=[text], images=[image], return_tensors="pt", padding=True
+    ).to(model.device)
+
+    with torch.no_grad():
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=int(max_new_tokens),
+            do_sample=temperature > 0,
+            temperature=temperature if temperature > 0 else 1.0,
+        )
+
+    generated_ids_trimmed = [
+        out_ids[len(in_ids):]
+        for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+
+    return processor.batch_decode(
+        generated_ids_trimmed,
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=False,
+    )[0]
+
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+with gr.Blocks(
+    title="Chest X-Ray Analysis — Qwen2.5-VL-3B",
+    theme=gr.themes.Soft(),
+) as demo:
+    gr.Markdown(
+        "# Chest X-Ray Analysis\n"
+        "Upload a chest X-ray and get an automated report "
+        "powered by **Qwen2.5-VL-3B-Instruct**."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="Upload Chest X-Ray")
+            prompt_input = gr.Textbox(
+                label="Prompt",
+                value=DEFAULT_PROMPT,
+                lines=4,
+            )
+            with gr.Row():
+                max_tokens_slider = gr.Slider(
+                    minimum=64, maximum=1024, value=512, step=64,
+                    label="Max New Tokens",
+                )
+                temperature_slider = gr.Slider(
+                    minimum=0.0, maximum=1.5, value=0.3, step=0.05,
+                    label="Temperature (0 = greedy)",
+                )
+            submit_btn = gr.Button("Analyze", variant="primary")
+
+        with gr.Column(scale=1):
+            output_text = gr.Textbox(label="Model Report", lines=20)
+
+    submit_btn.click(
+        predict,
+        inputs=[image_input, prompt_input, max_tokens_slider, temperature_slider],
+        outputs=output_text,
+    )
+
+    gr.Markdown(
+        "---\n"
+        "*Research purposes only — not a substitute for professional medical diagnosis.*"
+    )
+
+if __name__ == "__main__":
+    demo.launch()