Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor
+from PIL import Image
+
+# Define constants
+MODEL_NAME = "microsoft/Phi-3.5-vision-instruct"
+DESCRIPTION = "# [Phi-3.5-vision Demo](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)"
+DEVICE = "cuda"
+
+# Load model and processor
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").to(DEVICE).eval()
+processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
+
+def run_example(image, text_input, model_id):
+    # Prepare prompt and image for processing
+    prompt = f"{text_input}\n"
+    image = Image.fromarray(image).convert("RGB")
+    
+    # Process input
+    inputs = processor(prompt, image, return_tensors="pt").to(DEVICE)
+    generate_ids = model.generate(**inputs, max_new_tokens=1000, eos_token_id=processor.tokenizer.eos_token_id)
+    generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
+    response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    
+    return response
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+# Set up the Gradio interface
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Tab(label="Phi-3.5 Input"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                text_input = gr.Textbox(label="Question")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+        submit_btn.click(run_example, inputs=[input_img, text_input, MODEL_NAME], outputs=output_text)
+
+demo.launch(debug=True)