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app.py

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+import torch
+import gradio as gr
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+# Initialize model and tokenizer
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
+
+# Default chat parameters
+enable_thinking = False
+
+def chat_with_model(image, question, history):
+    """
+    Chat with the MiniCPM model.
+    
+    Args:
+        image: PIL Image or None
+        question: str, user's text question
+        history: list of previous conversation turns
+    
+    Returns:
+        str: model's response
+        list: updated conversation history
+    """
+    # Build messages with history context
+    msgs = []
+    for h in history:
+        msgs.append({"role": "user", "content": h[0]})
+        msgs.append({"role": "assistant", "content": h[1]})
+    
+    # Add current user message
+    if image is not None:
+        msgs.append({"role": "user", "content": [image, question]})
+    else:
+        msgs.append({"role": "user", "content": question})
+    
+    # Generate model response
+    answer = model.chat(
+        msgs=msgs,
+        tokenizer=tokenizer,
+        enable_thinking=enable_thinking
+    )
+    
+    # Update history
+    history.append((question if image is None else [image, question], answer))
+    return answer, history
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# MiniCPM-V-4.5 Chat Interface")
+    gr.Markdown("Upload an image and ask questions, or chat without an image")
+    
+    # Store conversation history
+    chat_history = gr.State([])
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Image (optional)")
+            question_input = gr.Textbox(label="Your Question", placeholder="Enter your question here...")
+            submit_btn = gr.Button("Submit")
+            clear_btn = gr.Button("Clear History")
+        
+        with gr.Column():
+            response_output = gr.Textbox(label="Model Response", interactive=False)
+            chat_display = gr.Chatbot(label="Conversation History")
+    
+    # Handle submit action
+    submit_btn.click(
+        fn=chat_with_model,
+        inputs=[image_input, question_input, chat_history],
+        outputs=[response_output, chat_history]
+    ).then(
+        fn=lambda history: history,
+        inputs=[chat_history],
+        outputs=[chat_display]
+    )
+    
+    # Clear history
+    def clear_history():
+        return [], [], ""
+    
+    clear_btn.click(
+        fn=clear_history,
+        outputs=[chat_history, chat_display, response_output]
+    )
+
+# Launch demo
+if __name__ == "__main__":
+    demo.launch()