Update app.py

app.py

@@ -1,6 +1,5 @@
-from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
+from transformers import AutoTokenizer, TextStreamer
 from PIL import Image
-import requests
 import torch
 from threading import Thread
 import gradio as gr
@@ -8,100 +7,137 @@ from gradio import FileData
 import time
 import spaces
 from unsloth import FastVisionModel
-ckpt ="Daemontatox/DocumentLlama"
-#model = MllamaForConditionalGeneration.from_pretrained(ckpt,
-    #torch_dtype=torch.bfloat16).to("cuda")
-#processor = AutoProcessor.from_pretrained(ckpt)
+
+# Load model and tokenizer
+ckpt = "Daemontatox/DocumentLlama"
 model, tokenizer = FastVisionModel.from_pretrained(
     ckpt,
-    load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.
-    use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
+    load_in_4bit=True,
+    use_gradient_checkpointing="unsloth",
 )
 
-
+# Enable inference mode
+FastVisionModel.for_inference(model)
 
 @spaces.GPU()
 def bot_streaming(message, history, max_new_tokens=2048):
-    
     txt = message["text"]
-    ext_buffer = f"{txt}"
-    
-    messages= [] 
+    messages = []
     images = []
     
-
-    for i, msg in enumerate(history): 
+    # Process history
+    for i, msg in enumerate(history):
         if isinstance(msg[0], tuple):
-            messages.append({"role": "user", "content": [{"type": "text", "text": history[i+1][0]}, {"type": "image"}]})
-            messages.append({"role": "assistant", "content": [{"type": "text", "text": history[i+1][1]}]})
+            messages.append({
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": history[i+1][0]},
+                    {"type": "image"}
+                ]
+            })
+            messages.append({
+                "role": "assistant",
+                "content": [{"type": "text", "text": history[i+1][1]}]
+            })
             images.append(Image.open(msg[0][0]).convert("RGB"))
         elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
-            # messages are already handled
             pass
-        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str): # text only turn
-            messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
-            messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})
+        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str):
+            messages.append({
+                "role": "user",
+                "content": [{"type": "text", "text": msg[0]}]
+            })
+            messages.append({
+                "role": "assistant",
+                "content": [{"type": "text", "text": msg[1]}]
+            })
 
-    # add current message
+    # Handle current message
     if len(message["files"]) == 1:
-        
-        if isinstance(message["files"][0], str): # examples
+        if isinstance(message["files"][0], str):  # examples
             image = Image.open(message["files"][0]).convert("RGB")
-        else: # regular input
+        else:  # regular input
             image = Image.open(message["files"][0]["path"]).convert("RGB")
         images.append(image)
-        messages.append({"role": "user", "content": [{"type": "text", "text": txt}, {"type": "image"}]})
+        messages.append({
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": txt}
+            ]
+        })
     else:
-        messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})
-
+        messages.append({
+            "role": "user",
+            "content": [{"type": "text", "text": txt}]
+        })
 
-    texts = processor.apply_chat_template(messages, add_generation_prompt=True)
-
-    if images == []:
-        inputs = processor(text=texts, return_tensors="pt").to("cuda")
+    # Prepare inputs
+    input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+    
+    if images:
+        inputs = tokenizer(
+            images[-1],  # Use the last image
+            input_text,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to("cuda")
     else:
-        inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
-    streamer = TextIteratorStreamer(processor, skip_special_tokens=True, skip_prompt=True)
+        inputs = tokenizer(
+            input_text,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to("cuda")
 
-    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
-    generated_text = ""
+    # Setup streaming
+    text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+    buffer = ""
+
+    def generate():
+        nonlocal buffer
+        output_ids = model.generate(
+            **inputs,
+            streamer=text_streamer,
+            max_new_tokens=max_new_tokens,
+            use_cache=True,
+            temperature=1.5,
+            min_p=0.1
+        )
     
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread = Thread(target=generate)
     thread.start()
-    buffer = ""
     
-    for new_text in streamer:
+    for new_text in text_streamer:
         buffer += new_text
-        generated_text_without_prompt = buffer
         time.sleep(0.01)
         yield buffer
 
-
-demo = gr.ChatInterface(fn=bot_streaming, title="Document Analyzer", examples=[
-    [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]},
-    200],
-    [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]},
-    250],
-    [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]},
-    250],
-    [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]},
-    250],
-    [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 
-    250],
+# Setup Gradio interface
+demo = gr.ChatInterface(
+    fn=bot_streaming,
+    title="Document Analyzer",
+    examples=[
+        [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]}, 200],
+        [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]}, 250],
+        [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]}, 250],
+        [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]}, 250],
+        [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 250],
     ],
-      textbox=gr.MultimodalTextbox(), 
-      additional_inputs = [gr.Slider(
-              minimum=10,
-              maximum=500,
-              value=2048,
-              step=10,
-              label="Maximum number of new tokens to generate",
-          )
-        ],
-      cache_examples=False,
-      description="MllM ",
-      stop_btn="Stop Generation", 
-      fill_height=True,
-    multimodal=True)
-    
+    textbox=gr.MultimodalTextbox(),
+    additional_inputs=[
+        gr.Slider(
+            minimum=10,
+            maximum=500,
+            value=2048,
+            step=10,
+            label="Maximum number of new tokens to generate",
+        )
+    ],
+    cache_examples=False,
+    description="MllM",
+    stop_btn="Stop Generation",
+    fill_height=True,
+    multimodal=True
+)
+
 demo.launch(debug=True)