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import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
import gradio as gr
# ----------------------------
# CONFIG
# ----------------------------
MODEL_NAME = "reverseforward/qwenmeasurement" # change this to your repo name
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16 # use float16 on A10G
# ----------------------------
# LOAD MODEL
# ----------------------------
print("Loading model...")
model = AutoModelForVision2Seq.from_pretrained(
MODEL_NAME,
torch_dtype=DTYPE,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(MODEL_NAME)
print("Model loaded successfully.")
# ----------------------------
# INFERENCE FUNCTION
# ----------------------------
def chat_with_image(image, text):
if image is None or text.strip() == "":
return "Please provide both an image and text input."
# Prepare inputs for Qwen3-VL
inputs = processor(text=[text], images=[image], return_tensors="pt").to(DEVICE, DTYPE)
# Generate output
with torch.inference_mode():
generated_ids = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.7,
)
output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return output.strip()
# ----------------------------
# GRADIO UI
# ----------------------------
title = "🧠 Qwen3-VL-8B Fine-tuned (Image + Text)"
description = """
Upload an image and enter a text prompt.
The model will reason visually and respond.
"""
demo = gr.Interface(
fn=chat_with_image,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Textbox(label="Enter Instruction or Question"),
],
outputs=gr.Textbox(label="Model Output"),
title=title,
description=description,
examples=[
["examples/cat.jpg", "Describe this image."],
["examples/room.jpg", "How many chairs are visible?"],
],
)
if __name__ == "__main__":
demo.launch()
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