Update app.py

app.py

@@ -1,68 +1,66 @@
-import gradio as gr
-import spaces
+import base64
+from io import BytesIO
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 from transformers import AutoModelForCausalLM, AutoProcessor
 import torch
 from PIL import Image
 import subprocess
+
+# Install flash-attn
 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()
+app = FastAPI()
 
+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)
+    "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")
+class InputData(BaseModel):
+    image: str
+    text_input: str
+    model_id: str = "microsoft/Phi-3.5-vision-instruct"
 
-    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
+@app.post("/run_example")
+async def run_example(input_data: InputData):
+    try:
+        model = models[input_data.model_id]
+        processor = processors[input_data.model_id]
 
-css = """
-  #output {
-    height: 500px; 
-    overflow: auto; 
-    border: 1px solid #ccc; 
-  }
-"""
+        # Decode base64 image
+        image_data = base64.b64decode(input_data.image)
+        image = Image.open(BytesIO(image_data)).convert("RGB")
 
-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")
+        user_prompt = '<|user|>\n'
+        assistant_prompt = '<|assistant|>\n'
+        prompt_suffix = "<|end|>\n"
+        prompt = f"{user_prompt}<|image_1|>\n{input_data.text_input}{prompt_suffix}{assistant_prompt}"
 
-        submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text])
+        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]
 
-demo.launch(debug=True)
+        return {"response": response}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))