Update app.py

app.py

@@ -7,6 +7,7 @@ import torch
 import spaces
 import subprocess
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+subprocess.run("pip install av", shell=True)  # Install pyav for video processing
 
 from io import BytesIO
 
@@ -16,114 +17,68 @@ model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B
                                                torch_dtype=torch.bfloat16).to("cuda:0")
 
 @spaces.GPU
-def model_inference(
-    input_dict, history, max_tokens
-): 
+def model_inference(input_dict, history, max_tokens): 
     text = input_dict["text"]
-    images = []
-    user_content = []
     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})
-
-        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)
-
-        resulting_messages = [{"role": "user", "content": user_content}]
-
-    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})
-
-        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()})
-            
-            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 = [] 
+    user_content = []
+    
+    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})
 
-    if text == "" and not images:
-        gr.Error("Please input a query and optionally image(s).")
+    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)
 
-    if text == "" and images:
-        gr.Error("Please input a text query along the images(s).")
-    print("resulting_messages", resulting_messages)
+    resulting_messages = [{"role": "user", "content": user_content}]
+    
+    if not text and not media_queue:
+        return "Please provide text and/or media input."
+    
     inputs = processor.apply_chat_template(
-    resulting_messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-    )
-
-    inputs = inputs.to(model.device)
+        resulting_messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    ).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 = ""
-
+    
     thread = Thread(target=model.generate, kwargs=generation_args)
     thread.start()
-
-    yield "..."
+    
+    yield "Generating response..."
     buffer = ""
     
     for new_text in streamer:
         buffer += new_text
-        generated_text_without_prompt = buffer
         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.launch(debug=True)
+demo = gr.ChatInterface(
+    fn=model_inference,
+    title="SmolVLM2: The Smallest Video Model Ever 📺", 
+    description="Play with SmolVLM2-2.2B-Instruct. Upload an image or video and ask a question.",
+    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(share=True, debug=True)