Create app.py

app.py

@@ -0,0 +1,65 @@
+import os, io
+import gradio as gr
+from PIL import Image
+
+# Make runtime conservative (avoid native kernel issues on shared GPUs)
+os.environ.setdefault("FLASH_ATTENTION", "0")
+os.environ.setdefault("XFORMERS_DISABLED", "1")
+os.environ.setdefault("ACCELERATE_USE_DEVICE_MAP", "0")
+
+# ---- VILA imports (from the repo installed via requirements.txt)
+from llava.model.builder import load_pretrained_model
+from llava.constants import DEFAULT_IMAGE_TOKEN
+
+# --- Load VILA-1.5-3B once
+MODEL_PATH = "Efficient-Large-Model/VILA1.5-3b"
+
+# Some builds need a non-None model_name; empty string is fine
+tokenizer, model, image_processor, context_len = load_pretrained_model(
+    MODEL_PATH, model_name="", model_base=None
+)
+
+# Fallback chat template (some checkpoints don’t ship one)
+if getattr(tokenizer, "chat_template", None) is None:
+    tokenizer.chat_template = (
+        "{% for message in messages %}{{ message['role'] | upper }}: "
+        "{{ message['content'] }}\n{% endfor %}ASSISTANT:"
+    )
+
+def vila_infer(image, prompt, max_new_tokens, temperature):
+    if image is None:
+        return "Please upload an image."
+    if not prompt.strip():
+        prompt = "Please describe the image."
+
+    # VILA expects a “conversation” with mixed media.
+    # We pass both the image and the text. The model code will find the image
+    # and insert media tokens automatically.
+    # (Under the hood it looks for DEFAULT_IMAGE_TOKEN or a media dict.)
+    pil = Image.fromarray(image).convert("RGB")
+
+    # Minimal prompt: put the <image> token then your question
+    user_prompt = f"{DEFAULT_IMAGE_TOKEN}\n{prompt}"
+
+    # Let VILA handle preprocessing & generation
+    out = model.generate_content(
+        prompt=[{"from":"human","value":[{"type":"image","value":pil},
+                                        {"type":"text","value":prompt}]}],
+        generation_config=None
+    )
+    # Some versions return plain text; others return dicts. Normalize:
+    return str(out)
+
+with gr.Blocks(title="VILA 1.5 3B (HF Space)") as demo:
+    gr.Markdown("## 🖼️ VILA-1.5-3B Demo\nUpload an image and ask a question.")
+    with gr.Row():
+        img = gr.Image(type="numpy", label="Image", height=320)
+        prompt = gr.Textbox(label="Prompt", value="Please describe the image", lines=2)
+    with gr.Row():
+        max_new = gr.Slider(16, 256, value=96, step=1, label="Max new tokens")
+        temp = gr.Slider(0.0, 1.0, value=0.0, step=0.1, label="Temperature")
+    btn = gr.Button("Run")
+    out = gr.Textbox(label="Output", lines=8)
+    btn.click(vila_infer, [img, prompt, max_new, temp], out)
+
+demo.launch()