Update app.py

app.py

@@ -7,94 +7,110 @@ import gradio as gr
 from gradio import FileData
 import time
 import spaces
+import os
+
+# Load Hugging Face token from environment variables
+hf_token = os.getenv("HF_AUTH_TOKEN")
+if not hf_token:
+    raise ValueError("Hugging Face token not found. Set HF_AUTH_TOKEN in your Space settings.")
+
+# Model checkpoint
 ckpt = "meta-llama/Llama-3.2-11B-Vision-Instruct"
-model = MllamaForConditionalGeneration.from_pretrained(ckpt,
-    torch_dtype=torch.bfloat16).to("cuda")
-processor = AutoProcessor.from_pretrained(ckpt)
+
+# Load model and processor with authentication
+model = MllamaForConditionalGeneration.from_pretrained(
+    ckpt,
+    torch_dtype=torch.bfloat16,
+    token=hf_token
+).to("cuda")
+
+processor = AutoProcessor.from_pretrained(ckpt, token=hf_token)
 
 
 @spaces.GPU
 def bot_streaming(message, history, max_new_tokens=250):
-    
     txt = message["text"]
     ext_buffer = f"{txt}"
     
-    messages= [] 
+    messages = [] 
     images = []
     
-
+    # Process conversation 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]}]})
             images.append(Image.open(msg[0][0]).convert("RGB"))
         elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
-            # messages are already handled
+            # Messages are already handled
             pass
-        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str): # text only turn
+        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]}]})
 
-    # add current message
+    # Add 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"}]})
     else:
         messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})
 
-
+    # Prepare inputs
     texts = processor.apply_chat_template(messages, add_generation_prompt=True)
-
     if images == []:
         inputs = processor(text=texts, 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)
 
+    streamer = TextIteratorStreamer(processor, skip_special_tokens=True, skip_prompt=True)
     generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
     generated_text = ""
     
+    # Stream generation in a separate thread
     thread = Thread(target=model.generate, kwargs=generation_kwargs)
     thread.start()
     buffer = ""
     
     for new_text in streamer:
         buffer += new_text
-        generated_text_without_prompt = buffer
         time.sleep(0.01)
         yield buffer
 
 
-demo = gr.ChatInterface(fn=bot_streaming, title="Multimodal Llama", 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],
+# Gradio Interface
+demo = gr.ChatInterface(
+    fn=bot_streaming,
+    title="Multimodal Llama",
+    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=250,
-              step=10,
-              label="Maximum number of new tokens to generate",
-          )
-        ],
-      cache_examples=False,
-      description="Try Multimodal Llama by Meta with transformers in this demo. Upload an image, and start chatting about it, or simply try one of the examples below. To learn more about Llama Vision, visit [our blog post](https://huggingface.co/blog/llama32). ",
-      stop_btn="Stop Generation", 
-      fill_height=True,
-    multimodal=True)
-    
-demo.launch(debug=True)
+    textbox=gr.MultimodalTextbox(),
+    additional_inputs=[
+        gr.Slider(
+            minimum=10,
+            maximum=500,
+            value=250,
+            step=10,
+            label="Maximum number of new tokens to generate",
+        )
+    ],
+    cache_examples=False,
+    description=(
+        "Try Multimodal Llama by Meta with transformers in this demo. "
+        "Upload an image, and start chatting about it, or simply try one of the examples below. "
+        "To learn more about Llama Vision, visit [our blog post](https://huggingface.co/blog/llama32)."
+    ),
+    stop_btn="Stop Generation",
+    fill_height=True,
+    multimodal=True
+)
+
+demo.launch(debug=True)