Update app.py

app.py

@@ -1,44 +1,30 @@
 import gradio as gr
-import spaces
 from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
 from qwen_vl_utils import process_vision_info
 import torch
 from PIL import Image
-import subprocess
 from datetime import datetime
 import numpy as np
 import os
 
-
-# subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-
-# models = {
-#     "Qwen/Qwen2-VL-7B-Instruct": AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()
-
-# }
 def array_to_image_path(image_array):
     if image_array is None:
         raise ValueError("No image provided. Please upload an image before submitting.")
-    # Convert numpy array to PIL Image
     img = Image.fromarray(np.uint8(image_array))
-    
-    # Generate a unique filename using timestamp
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
     filename = f"image_{timestamp}.png"
-    
-    # Save the image
     img.save(filename)
-    
-    # Get the full path of the saved image
-    full_path = os.path.abspath(filename)
-    
-    return full_path
+    return os.path.abspath(filename)
 
-device = "mps" if torch.backends.mps.is_available() else "cpu"
+# Force CPU
+device = "cpu"
 
 models = {
-    "Qwen/Qwen2-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto").to(device).eval()
-
+    "Qwen/Qwen2-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained(
+        "Qwen/Qwen2-VL-7B-Instruct",
+        trust_remote_code=True,
+        torch_dtype=torch.float32  
+    ).to(device).eval()
 }
 
 processors = {
@@ -47,41 +33,31 @@ processors = {
 
 DESCRIPTION = "[Qwen2-VL-7B Demo](https://huggingface.co/Qwen/Qwen2-VL-7B-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="Qwen/Qwen2-VL-7B-Instruct"):
     image_path = array_to_image_path(image)
     
-    print(image_path)
     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")
     messages = [
-    {
+        {
             "role": "user",
             "content": [
-                {
-                    "type": "image",
-                    "image": image_path,
-                },
+                {"type": "image", "image": image_path},
                 {"type": "text", "text": text_input},
             ],
         }
     ]
     
-    # Preparation for inference
-    text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
     image_inputs, video_inputs = process_vision_info(messages)
+    
     inputs = processor(
         text=[text],
         images=image_inputs,
@@ -89,16 +65,13 @@ def run_example(image, text_input=None, model_id="Qwen/Qwen2-VL-7B-Instruct"):
         padding=True,
         return_tensors="pt",
     )
-    inputs = inputs.to("cuda")
+    inputs = inputs.to("cpu") 
     
-    # Inference: Generation of the output
-    generated_ids = model.generate(**inputs, max_new_tokens=1024)
+    generated_ids = model.generate(**inputs, max_new_tokens=512)  # reduced tokens for CPU
     generated_ids_trimmed = [
-        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
     ]
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
+    output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
     
     return output_text[0]