Update app.py

app.py

@@ -7,27 +7,76 @@
 # demo.launch()
 import requests
 from PIL import Image
-from transformers import AutoModelForCausalLM, AutoProcessor
+from transformers import AutoModelForCausalLM, AutoProcessor, MarianMTModel, MarianTokenizer
 import torch
 import gradio as gr
 
+# 验证 SentencePiece 是否安装
+try:
+    import sentencepiece
+    print("SentencePiece is installed successfully!")
+except ImportError:
+    print("SentencePiece is NOT installed!")
+
 # 设置设备
 device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
 
-# 加载模型和处理器
+# 加载 Florence-2 模型和处理器
+print("Loading Florence-2 model...")
 model = AutoModelForCausalLM.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v1.5", trust_remote_code=True).to(device)
 processor = AutoProcessor.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v1.5", trust_remote_code=True)
+print("Florence-2 model loaded successfully.")
+
+# 加载 Helsinki-NLP 的翻译模型（英文到中文）
+print("Loading translation model...")
+translation_model_name = "Helsinki-NLP/opus-mt-en-zh"
+translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)
+translation_model = MarianMTModel.from_pretrained(translation_model_name).to(device)
+print("Translation model loaded successfully.")
 
-def generate_caption(image_url):
+# 翻译函数
+def translate_to_chinese(text):
     try:
-        # 下载并打开图像
-        image = Image.open(requests.get(image_url, stream=True).raw)
+        # 确保输入是字符串
+        if not isinstance(text, str):
+            print(f"Input is not a string: {text} (type: {type(text)})")
+            text = str(text)  # 强制转换为字符串
+        
+        print("Input text for translation:", text)
+        tokenized_text = translation_tokenizer(text, return_tensors="pt", max_length=512, truncation=True).to(device)
+        translated_tokens = translation_model.generate(**tokenized_text)
+        translated_text = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+        print("Translated text:", translated_text)
+        return translated_text
+    except Exception as e:
+        print("Translation error:", str(e))
+        return f"Translation error: {str(e)}"
+
+# 生成描述并翻译
+def generate_caption(image):
+    try:
+        # 如果输入是 URL，下载图片
+        if isinstance(image, str):
+            print("Downloading image from URL...")
+            try:
+                response = requests.get(image, stream=True, timeout=10)
+                response.raise_for_status()  # 检查请求是否成功
+                image = Image.open(response.raw)
+                print("Image downloaded successfully.")
+            except requests.exceptions.RequestException as e:
+                return f"Failed to download image: {str(e)}"
+        # 如果输入是文件路径，直接打开图片
+        else:
+            print("Loading image from file...")
+            image = Image.open(image)
         
         # 准备输入
         prompt = "<MORE_DETAILED_CAPTION>"
         inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
         
         # 生成文本
+        print("Generating caption...")
         generated_ids = model.generate(
             input_ids=inputs["input_ids"],
             pixel_values=inputs["pixel_values"],
@@ -36,30 +85,41 @@ def generate_caption(image_url):
             num_beams=3
         )
         generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+        print("Generated text:", generated_text)
+        print("Type of generated text:", type(generated_text))
         
         # 解析生成的文本
         parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
+        print("Parsed answer:", parsed_answer)
+        print("Type of parsed answer:", type(parsed_answer))
+        
+        # 翻译成中文
+        print("Translating to Chinese...")
+        translated_answer = translate_to_chinese(parsed_answer)
+        print("Translation completed.")
         
-        return parsed_answer
+        return translated_answer
     except Exception as e:
+        print("Error:", str(e))
         return f"Error: {str(e)}"
 
 # Gradio 界面
-def gradio_interface(image_url):
-    result = generate_caption(image_url)
+def gradio_interface(image):
+    result = generate_caption(image)
     return result
 
 # 创建 Gradio 应用
 iface = gr.Interface(
     fn=gradio_interface,  # 处理函数
-    inputs=gr.Textbox(label="Image URL", placeholder="Enter the URL of the image..."),  # 输入组件
-    outputs=gr.Textbox(label="Generated Caption"),  # 输出组件
+    inputs=gr.Image(label="Upload Image or Enter Image URL", type="filepath"),  # 输入组件
+    outputs=gr.Textbox(label="Generated Caption (Translated to Chinese)"),  # 输出组件
     title="Florence-2 Prompt Generation",  # 标题
-    description="Generate detailed captions for images using Florence-2 model.",  # 描述
+    description="Generate detailed captions for images using Florence-2 model and translate them to Chinese. You can upload an image or provide an image URL.",  # 描述
     examples=[
         ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"]
     ]  # 示例
 )
 
 # 启动 Gradio 应用
+print("Launching Gradio app...")
 iface.launch()