style

app.py

@@ -1,5 +1,9 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoModelForImageTextToText, TextIteratorStreamer
+from transformers import (
+    AutoProcessor,
+    AutoModelForImageTextToText,
+    TextIteratorStreamer,
+)
 from threading import Thread
 import re
 import time
@@ -7,21 +11,21 @@ import time
 from optimum.intel import OVModelForVisualCausalLM
 
 
-
-# model_id = "echarlaix/SmolVLM2-2.2B-Instruct-openvino" 
-model_id = "echarlaix/SmolVLM-256M-Instruct-openvino" 
+# model_id = "echarlaix/SmolVLM2-2.2B-Instruct-openvino"
+model_id = "echarlaix/SmolVLM-256M-Instruct-openvino"
 
 processor = AutoProcessor.from_pretrained(model_id)
 model = OVModelForVisualCausalLM.from_pretrained(model_id)
 
+
 def model_inference(input_dict, history, max_tokens):
     text = input_dict["text"]
     images = []
     user_content = []
     media_queue = []
     if history == []:
-        text = input_dict["text"].strip() 
-        
+        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})
@@ -29,17 +33,17 @@ def model_inference(input_dict, history, max_tokens):
                 media_queue.append({"type": "video", "path": file})
 
         if "<image>" in text or "<video>" in text:
-            parts = re.split(r'(<image>|<video>)', 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)) 
+                    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(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)
 
@@ -50,55 +54,54 @@ def model_inference(input_dict, history, max_tokens):
         user_content = []
         media_queue = []
         for hist in history:
-            if hist["role"] == "user" and isinstance(hist["content"], tuple): 
+            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): 
+            if hist["role"] == "user" and isinstance(hist["content"], str):
                 text = hist["content"]
-                parts = re.split(r'(<image>|<video>)', 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)) 
+                        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(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 = [] 
 
+            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 = []
 
     if text == "" and not images:
         gr.Error("Please input a query and optionally image(s).")
 
     if text == "" and images:
         gr.Error("Please input a text query along the images(s).")
-    print("resulting_messages", resulting_messages)
+    # print("resulting_messages", resulting_messages)
     inputs = processor.apply_chat_template(
-    resulting_messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-    )    
+        resulting_messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    )
 
     # Generate
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    streamer = TextIteratorStreamer(
+        processor, skip_prompt=True, skip_special_tokens=True
+    )
     generation_args = dict(inputs, streamer=streamer, max_new_tokens=max_tokens)
     # generated_text = ""
 
@@ -107,33 +110,67 @@ def model_inference(input_dict, history, max_tokens):
 
     yield "..."
     buffer = ""
-    
+
     for new_text in streamer:
-    
         buffer += new_text
         # generated_text_without_prompt = buffer#[len(ext_buffer):]
         time.sleep(0.01)
         yield buffer
 
 
-examples=[
-              [{"text": "Where do the severe droughts happen according to this diagram?", "files": ["example_images/examples_weather_events.png"]}],
-              [{"text": "What art era this artpiece <image> and this artpiece <image> belong to?", "files": ["example_images/rococo.jpg", "example_images/rococo_1.jpg"]}],
-              [{"text": "Describe this image.", "files": ["example_images/mosque.jpg"]}],
-              [{"text": "When was this purchase made and how much did it cost?", "files": ["example_images/fiche.jpg"]}],
-              [{"text": "What is the date in this document?", "files": ["example_images/document.jpg"]}],
-              [{"text": "What is happening in the video?", "files": ["example_images/short.mp4"]}],
-      ]
-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 or try one of the examples. This demo doesn't use history for the chat, so every chat you start is a new conversation.",
-                examples=examples,
-                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)
-        
+examples = [
+    [
+        {
+            "text": "Where do the severe droughts happen according to this diagram?",
+            "files": ["example_images/examples_weather_events.png"],
+        }
+    ],
+    [
+        {
+            "text": "What art era this artpiece <image> and this artpiece <image> belong to?",
+            "files": ["example_images/rococo.jpg", "example_images/rococo_1.jpg"],
+        }
+    ],
+    [   {
+            "text": "Describe this image.",
+            "files": ["example_images/mosque.jpg"]
+        }
+    ],
+    [
+        {
+            "text": "When was this purchase made and how much did it cost?",
+            "files": ["example_images/fiche.jpg"],
+        }
+    ],
+    [
+        {
+            "text": "What is the date in this document?",
+            "files": ["example_images/document.jpg"],
+        }
+    ],
+    [
+        {
+            "text": "What is happening in the video?",
+            "files": ["example_images/short.mp4"],
+        }
+    ],
+]
+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 or try one of the examples. This demo doesn't use history for the chat, so every chat you start is a new conversation.",
+    examples=examples,
+    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)