Upload app.py

app.py

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+# Importing the requirements
+import warnings
+warnings.filterwarnings("ignore")
+
+import gradio as gr
+from src.minicpm.response import describe_image
+
+
+# Image, text query, and input parameters
+image = gr.Image(type="pil", label="Image")
+question = gr.Textbox(label="Question", placeholder="Enter your question here")
+temperature = gr.Slider(
+    minimum=0.01, maximum=1.99, step=0.01, value=0.7, label="Temperature"
+)
+top_p = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.8, label="Top P")
+top_k = gr.Slider(minimum=0, maximum=1000, step=1, value=100, label="Top K")
+max_new_tokens = gr.Slider(minimum=1, maximum=4096, step=1, value=512, label="Max Tokens")
+
+# Output for the interface
+answer = gr.Textbox(label="Predicted answer", show_label=True, show_copy_button=True)
+
+# Examples for the interface
+examples = [
+    [
+        "images/cat.jpg",
+        "How many cats are there?",
+        0.7,
+        0.8,
+        100,
+        512,
+    ],
+    [
+        "images/dog.jpg",
+        "¿De qué color es el perro?",
+        0.7,
+        0.8,
+        100,
+        512,
+    ],
+    [
+        "images/bird.jpg",
+        "Que fait l'oiseau ?",
+        0.7,
+        0.8,
+        100,
+        512,
+    ],
+]
+
+# Title, description, and article for the interface
+title = "Visual Question Answering"
+description = "Gradio Demo for the MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming. This model can answer questions about images in natural language. To use it, upload your image, type a question, select associated parameters, use the default values, click 'Submit', or click one of the examples to load them. You can read more at the links below."
+article = "<p style='text-align: center'><a href='https://github.com/OpenBMB/MiniCPM-o' target='_blank'>Model GitHub Repo</a> | <a href='https://huggingface.co/openbmb/MiniCPM-o-2_6' target='_blank'>Model Page</a></p>"
+
+
+# Launch the interface
+interface = gr.Interface(
+    fn=describe_image,
+    inputs=[image, question, temperature, top_p, top_k, max_new_tokens],
+    outputs=answer,
+    examples=examples,
+    cache_examples=True,
+    cache_mode="lazy",
+    title=title,
+    description=description,
+    article=article,
+    theme="Glass",
+    flagging_mode="never",
+)
+interface.launch(debug=False)