Update app.py

app.py

@@ -7,123 +7,93 @@ import torch
 import spaces
 import subprocess
 
+# Install flash-attn with no CUDA build
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
-from io import BytesIO
-
 processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
-model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", 
-                                               _attn_implementation="flash_attention_2",
-                                               torch_dtype=torch.bfloat16).to("cuda:0")
+model = AutoModelForImageTextToText.from_pretrained(
+    "HuggingFaceTB/SmolVLM2-2.2B-Instruct", 
+    _attn_implementation="flash_attention_2",
+    torch_dtype=torch.bfloat16
+).to("cuda:0")
 
 @spaces.GPU
-def model_inference(
-    input_dict, history, max_tokens
-): 
-    text = input_dict["text"]
-    images = []
-    user_content = []
+def model_inference(input_dict, history, max_tokens): 
+    text = input_dict.get("text", "").strip()
     media_queue = []
-    if history == []:
-        text = input_dict["text"].strip() 
-        
-        for file in input_dict.get("files", []):
-            if file.endswith((".png", ".jpg", ".jpeg", ".gif", ".bmp")):
-                media_queue.append({"type": "image", "path": file})
-            elif file.endswith((".mp4", ".mov", ".avi", ".mkv", ".flv")):
-                media_queue.append({"type": "video", "path": file})
+    user_content = []
 
-        if "<image>" in text or "<video>" in text:
-            parts = re.split(r'(<image>|<video>)', text)  
-            for part in parts:
-                if part == "<image>" and media_queue:
-                    user_content.append(media_queue.pop(0)) 
-                elif part == "<video>" and media_queue:
-                    user_content.append(media_queue.pop(0))  
-                elif part.strip():  
-                    user_content.append({"type": "text", "text": part.strip()})
-        else:
-            user_content.append({"type": "text", "text": text})
-            
-            for media in media_queue:
-                user_content.append(media)
+    # Process uploaded media files
+    for file in input_dict.get("files", []):
+        if file.endswith((".png", ".jpg", ".jpeg", ".gif", ".bmp")):
+            media_queue.append({"type": "image", "path": file})
+        elif file.endswith((".mp4", ".mov", ".avi", ".mkv", ".flv")):
+            media_queue.append({"type": "video", "path": file})
 
-        resulting_messages = [{"role": "user", "content": user_content}]
+    # Construct user content with placeholders
+    if "<image>" in text or "<video>" in text:
+        parts = re.split(r'(<image>|<video>)', text)  
+        for part in parts:
+            if part == "<image>" and media_queue:
+                user_content.append(media_queue.pop(0)) 
+            elif part == "<video>" and media_queue:
+                user_content.append(media_queue.pop(0))  
+            elif part.strip():
+                user_content.append({"type": "text", "text": part.strip()})
+    else:
+        user_content.append({"type": "text", "text": text})
+        user_content.extend(media_queue)  
 
-    elif len(history) > 0:
-        resulting_messages = []
-        user_content = []
-        media_queue = []
-        for hist in history:
-            if hist["role"] == "user" and isinstance(hist["content"], tuple): 
-                file_name = hist["content"][0]
-            if file_name.endswith((".png", ".jpg", ".jpeg")):
-                media_queue.append({"type": "image", "path": file_name})
-            elif file_name.endswith(".mp4"):
-                media_queue.append({"type": "video", "path": file_name})
+    resulting_messages = [{"role": "user", "content": user_content}]
 
+    # Process history
+    if history:
         for hist in history:
-            if hist["role"] == "user" and isinstance(hist["content"], str): 
-                text = hist["content"]
-                parts = re.split(r'(<image>|<video>)', text)  
-                
-                for part in parts:
-                    if part == "<image>" and media_queue:
-                        user_content.append(media_queue.pop(0)) 
-                    elif part == "<video>" and media_queue:
-                        user_content.append(media_queue.pop(0))  
-                    elif part.strip(): 
-                        user_content.append({"type": "text", "text": part.strip()})
-            
+            if hist["role"] == "user":
+                if isinstance(hist["content"], tuple) and len(hist["content"]) > 0:
+                    file_name = hist["content"][0]
+                    if file_name.endswith((".png", ".jpg", ".jpeg")):
+                        media_queue.append({"type": "image", "path": file_name})
+                    elif file_name.endswith(".mp4"):
+                        media_queue.append({"type": "video", "path": file_name})
+
             elif hist["role"] == "assistant":
-                resulting_messages.append({
-                    "role": "user",
-                    "content": user_content
-                })
-                resulting_messages.append({
-                    "role": "assistant",
-                    "content": [{"type": "text", "text": hist["content"]}]
-                })
-                user_content = [] 
+                resulting_messages.append({"role": "assistant", "content": [{"type": "text", "text": hist["content"]}]})
 
-    if text == "" and not images:
-        gr.Error("Please input a query and optionally image(s).")
+    if not text and not media_queue:
+        gr.Warning("Please provide text or an image/video.")
 
-    if text == "" and images:
-        gr.Error("Please input a text query along the images(s).")
-    print("resulting_messages", resulting_messages)
+    # Process inputs
     inputs = processor.apply_chat_template(
         resulting_messages,
         add_generation_prompt=True,
         tokenize=True,
         return_dict=True,
-        return_tensors="pt",
-    )
-
-    inputs = inputs.to(model.device)
-    
-    # Generate
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_args = dict(inputs, streamer=streamer, max_new_tokens=max_tokens)
-    generated_text = ""
+        return_tensors="pt"
+    ).to(model.device, dtype=torch.bfloat16)  # Ensure dtype consistency
 
-    thread = Thread(target=model.generate, kwargs=generation_args)
+    # Generate output
+    streamer = TextIteratorStreamer(processor.tokenizer, skip_prompt=True, skip_special_tokens=True)
+    thread = Thread(target=model.generate, kwargs={"input_ids": inputs["input_ids"], "streamer": streamer, "max_new_tokens": max_tokens})
     thread.start()
 
     yield "..."
     buffer = ""
-    
     for new_text in streamer:
         buffer += new_text
         time.sleep(0.01)
         yield buffer
 
-demo = gr.ChatInterface(fn=model_inference, title="SmolVLM2: The Smollest Video Model Ever 📺", 
-                description="Play with [SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) in this demo. To get started, upload an image and text. This demo doesn't use history for the chat, so every chat you start is a new conversation.",
-                textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", ".mp4"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
-                cache_examples=False,
-                additional_inputs=[gr.Slider(minimum=100, maximum=500, step=50, value=200, label="Max Tokens")],
-                type="messages"
-                )
+demo = gr.ChatInterface(
+    fn=model_inference,
+    title="SmolVLM2: The Smollest Video Model Ever 📺", 
+    description="Play with [SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) in this demo. To get started, upload an image and text.",
+    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", ".mp4"], file_count="multiple"), 
+    stop_btn="Stop Generation", 
+    multimodal=True,
+    cache_examples=False,
+    additional_inputs=[gr.Slider(minimum=100, maximum=500, step=50, value=200, label="Max Tokens")],
+    type="messages"
+)
 
 demo.launch(debug=True, share=True)