Update app.py

app.py

@@ -1,69 +1,67 @@
-import gradio as gr
-import spaces
-from transformers import AutoModelForCausalLM, AutoProcessor
-import torch
-from PIL import Image
-import subprocess
-subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-
-models = {
-    "microsoft/Phi-3.5-vision-instruct": AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()
-
-}
-
-processors = {
-    "microsoft/Phi-3.5-vision-instruct": AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True)
-}
-
-DESCRIPTION = "[Phi-3.5-vision Demo](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)"
-
-kwargs = {}
-kwargs['torch_dtype'] = torch.bfloat16
-
-user_prompt = '<|user|>\n'
-assistant_prompt = '<|assistant|>\n'
-prompt_suffix = "<|end|>\n"
-
-@spaces.GPU
-def run_example(image, text_input=None, model_id="microsoft/Phi-3.5-vision-instruct"):
-    model = models[model_id]
-    processor = processors[model_id]
-
-    prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
-    image = Image.fromarray(image).convert("RGB")
-
-    inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
-    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; 
-  }
-"""
-
-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")
-                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="microsoft/Phi-3.5-vision-instruct")
-                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, [input_img, text_input, model_selector], [output_text])
-
-demo.queue(api_open=False)
+import gradio as gr
+import spaces
+from transformers import AutoModelForCausalLM, AutoProcessor
+import torch
+from PIL import Image
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+models = {
+    "microsoft/Phi-3.5-vision-instruct": AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()
+
+}
+
+processors = {
+    "microsoft/Phi-3.5-vision-instruct": AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True)
+}
+
+kwargs = {}
+kwargs['torch_dtype'] = torch.bfloat16
+
+user_prompt = '<|user|>\n'
+assistant_prompt = '<|assistant|>\n'
+prompt_suffix = "<|end|>\n"
+
+@spaces.GPU
+def run_example(image, text_input=None, model_id="microsoft/Phi-3.5-vision-instruct"):
+    model = models[model_id]
+    processor = processors[model_id]
+
+    prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
+    image = Image.fromarray(image).convert("RGB")
+
+    inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
+    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; 
+  }
+"""
+
+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")
+                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="microsoft/Phi-3.5-vision-instruct")
+                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, [input_img, text_input, model_selector], [output_text])
+
+demo.queue(api_open=False)
 demo.launch(debug=True, show_api=False)