Update app.py

app.py

@@ -1,6 +1,9 @@
 import gradio as gr
 import spaces
 import argparse
+import torch
+from transformers import AutoModel, AutoProcessor
+from transformers import StoppingCriteria, TextIteratorStreamer, StoppingCriteriaList
 
 
 parser = argparse.ArgumentParser()
@@ -23,6 +26,72 @@ div#col-container {
 }
 """
 
+model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True).to(device)
+processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True)
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [151645]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+@torch.no_grad()
+def response(message, history, image):
+    stop = StopOnTokens()
+
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+
+    for user_msg, assistant_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": assistant_msg})
+
+    if len(messages) == 1:
+        message = f" <image>{message}"
+
+    messages.append({"role": "user", "content": message})
+
+    model_inputs = processor.tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    )
+
+    image = (
+            processor.feature_extractor(image)
+            .unsqueeze(0)
+    )
+
+    attention_mask = torch.ones(
+        1, model_inputs.shape[1] + processor.num_image_latents - 1
+    )
+    
+    model_inputs = {
+        "input_ids": model_inputs,
+        "images": image,
+        "attention_mask": attention_mask
+    }
+
+    model_inputs = {k: v.to(device) for k, v in model_inputs.items()}
+
+    streamer = TextIteratorStreamer(processor.tokenizer, timeout=30., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+        stopping_criteria=StoppingCriteriaList([stop])
+        )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    history.append([message, ""])
+    partial_response = ""
+    for new_token in streamer:
+        partial_response += new_token
+        history[-1][1] = partial_response
+        yield history, gr.Button(visible=False), gr.Button(visible=True, interactive=True)
+
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.HTML(title)