test1

app.py

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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()

requirements.txt

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+torch>=2.1.0
+transformers>=4.44.0
+accelerate
+gradio>=4.0.0
+safetensors
+pillow