Create app.py

app.py

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+from transformers import AutoProcessor, AutoModelForVision2Seq, Qwen2VLForConditionalGeneration
+import gradio as gr
+from PIL import Image
+
+model2 = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct",
+            torch_dtype="auto",  # or torch.bfloat16
+            #  attn_implementation="flash_attention_2",
+            device_map="auto",
+        )
+
+        # default processer
+processor = AutoProcessor.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct")
+
+
+# Game rules
+GAME_RULES = """In diesem Bild sehen Sie drei Farbenraster.
+In der folgenden Beschreibung wird genau eines der Raster beschrieben.
+Bitte geben Sie an, ob sich der Sprecher auf das linke, mittlere oder rechte Raster bezieht.
+Antworten Sie auf Deutsch.
+"""
+
+# Load one image
+IMAGE_OPTIONS = {
+    "Grid 1": "example1.jpg",
+    "Grid 2": "example2.jpg",
+    "Grid 3": "example3.jpg",
+    "Grid 4": "example4.jpg",
+    "Grid 5": "example5.jpg"
+}
+
+# Function to run model
+def play_game(selected_image_label, user_prompt):
+    user_prompt = user_prompt #or "Bitte beschreiben Sie das Gitter."
+
+    selected_image_path = IMAGE_OPTIONS[selected_image_label]
+    selected_image = Image.open(selected_image_path)
+
+    # Build messages 
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": selected_image},
+                {"type": "text", "text": GAME_RULES + "\n" + user_prompt},
+            ],
+        }
+    ]
+
+    # prepare input
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[text],
+        images=[selected_image],
+        return_tensors="pt",
+    ).to(model2.device)
+
+    # Run generation normally
+    generated_ids = model2.generate(**inputs, max_new_tokens=512)
+    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
+    )[0]
+    
+    return output_text
+
+
+# Gradio App
+with gr.Blocks() as demo:
+    with gr.Column():
+        image_selector = gr.Dropdown(
+            choices=list(IMAGE_OPTIONS.keys()),
+            value="Grid 1",
+            label="Wählen Sie ein Bild"
+        )
+
+        image_display = gr.Image(
+            value=Image.open(IMAGE_OPTIONS["Grid 1"]),
+            label="Ihr Bild",
+            interactive=False,
+            type="pil"
+        )
+
+        prompt_input = gr.Textbox(
+            value="Beschreiben Sie das Farbenraster...",
+            label="Ihre Beschreibung"
+        )
+
+        output_text = gr.Textbox(label="Antwort des Modells")
+
+        play_button = gr.Button("Starte das Spiel")
+
+        def update_image(selected_label):
+            selected_path = IMAGE_OPTIONS[selected_label]
+            return Image.open(selected_path)
+
+        # When user changes selection, update image
+        image_selector.change(
+            fn=update_image,
+            inputs=[image_selector],
+            outputs=image_display
+        )
+
+        # When user clicks play, send inputs to model
+        play_button.click(
+            fn=play_game,
+            inputs=[image_selector, prompt_input],
+            outputs=output_text
+        )
+
+demo.launch(share=True, server_port=4879)