Update app.py

app.py

@@ -1,97 +1,73 @@
 import gradio as gr
 import spaces
-from transformers import AutoModelForCausalLM, AutoProcessor, GPT2LMHeadModel, GPT2Tokenizer
+from transformers import AutoModelForCausalLM, AutoProcessor
 import torch
 from PIL import Image
 import subprocess
-
-# Install flash-attn with no CUDA build isolation
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
-# Define models and processors with pinning to a stable revision
 models = {
-    "microsoft/Phi-3.5-vision-instruct": AutoModelForCausalLM.from_pretrained(
-        "microsoft/Phi-3.5-vision-instruct",
-        revision="specific-revision-hash",  # Pinning to a specific revision for stability
-        trust_remote_code=True,
-        torch_dtype="auto", 
-        _attn_implementation="flash_attention_2"
-    ).cuda().eval()
+    "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",
-        revision="specific-revision-hash",  # Pinning to a specific revision for stability
-        trust_remote_code=True
-    )
+    "microsoft/Phi-3.5-vision-instruct": AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True)
 }
 
-# Fallback to GPT-2 for testing
-def load_fallback_model():
-    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-    model = GPT2LMHeadModel.from_pretrained("gpt2").cuda().eval()
-    return model, tokenizer
-
-# Default description and prompt
 DESCRIPTION = "[Phi-3.5-vision Demo](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)"
-default_question = "You are an image to prompt converter. Your work is to observe each and every detail of the image and craft a detailed prompt under 100 words."
 
-# Gradio function for generating output from image input with error handling
+kwargs = {}
+kwargs['torch_dtype'] = torch.bfloat16
+
+user_prompt = '<|user|>\n'
+assistant_prompt = '<|assistant|>\n'
+prompt_suffix = "<|end|>\n"
+
+default_question = "You are an image to prompt converter. Your work is to observe each and every detail of the image and craft a detailed prompt under 100 words in this format: [image content/subject, description of action, state, and mood], [art form, style], [artist/photographer reference if needed], [additional settings such as camera and lens settings, lighting, colors, effects, texture, background, rendering]."
+
 @spaces.GPU
 def run_example(image, text_input=default_question, model_id="microsoft/Phi-3.5-vision-instruct"):
-    try:
-        model = models[model_id]
-        processor = processors[model_id]
-    except KeyError as e:
-        print(f"Error loading model: {e}. Falling back to GPT-2.")
-        model, processor = load_fallback_model()
-    
-    user_prompt = '<|user|>\n'
-    assistant_prompt = '<|assistant|>\n'
-    prompt_suffix = "<|end|>\n"
-    
+    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 = 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]
+    response = processor.batch_decode(generate_ids, 
+                                    skip_special_tokens=True, 
+                                    clean_up_tokenization_spaces=False)[0]
     return response
 
-# Custom CSS for styling
 css = """
-  #output_text {
-    height: 500px;
-    overflow: auto;
-    border: 1px solid #333;
-  }
-  #model_selector, #text_input {
-    display: none !important;
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
   }
-  #main_container {
-    border: 2px solid black;
-    padding: 20px;
-    border-radius: 10px;
+  #model_selector, #text_input { 
+    display: none !important; 
   }
 """
 
-# Gradio interface with styling and layout improvements
 with gr.Blocks(css=css) as demo:
     gr.Markdown(DESCRIPTION)
-    with gr.Row(id="main_container"):
-        with gr.Column():
-            input_img = gr.Image(label="Input Image", interactive=True)
-            model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="microsoft/Phi-3.5-vision-instruct", visible=False)
-            text_input = gr.Textbox(label="Question", value=default_question, visible=False)
-            submit_btn = gr.Button(value="Generate Prompt")
-        
-        output_text = gr.Textbox(label="Output", id="output_text", interactive=False)
+    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", visible=False)
+                text_input = gr.Textbox(label="Question", value=default_question, visible=False)
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
 
-    # Link button action to function
-    submit_btn.click(run_example, [input_img, text_input, model_selector], output_text)
+        submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text])
 
-# Launch Gradio interface
 demo.queue(api_open=False)
-demo.launch(debug=True, show_api=False)
+demo.launch(debug=True, show_api=False)