Update app.py

app.py

@@ -1,70 +1,109 @@
-# 导入必要库
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+# -*- coding: utf-8 -*-
+"""
+鸟类知识科普系统（Qwen3优化版） by [你的名字]
+ISOM5240 Group Project
+"""
+
+import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
-import requests
-from io import BytesIO
 import torch
-from transformers import pipeline
-import wikipedia  # 用于获取鸟类百科信息
-from wikipedia.exceptions import DisambiguationError, PageError
 
-# 1. 鸟类图片识别（使用指定模型）
-def bird_classification(image_url):
-    model_name = "chriamue/bird-species-classifier"
-    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-    model = AutoModelForImageClassification.from_pretrained(model_name)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
+# 强制清理旧版缓存
+from transformers.utils import move_cache
+move_cache()
+
+# 初始化模型（兼容Qwen3）
+def init_models():
+    # 鸟类分类模型（保持不变）
+    classifier = pipeline(
+        "image-classification",
+        model="chriamue/bird-species-classifier",
+        device=0 if torch.cuda.is_available() else -1
+    )
+    
+    # 更新为Qwen3模型（官方支持版本）
+    text_generator = pipeline(
+        "text-generation",
+        model="Qwen/Qwen-7B-Chat",  # 使用官方维护版本
+        device_map="auto",
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,     # 必须开启
+        model_kwargs={
+            "revision": "main",
+            "force_download": True  # 替换弃用参数
+        }
+    )
     
-    # 下载并预处理图片
-    response = requests.get(image_url)
-    img = Image.open(BytesIO(response.content)).convert("RGB")
-    inputs = feature_extractor(img, return_tensors="pt").to(device)
+    # 语音合成模型（保持不变）
+    tts = pipeline(
+        "text-to-speech",
+        model="facebook/mms-tts-eng",
+        device=0 if torch.cuda.is_available() else -1
+    )
     
-    # 模型推理
-    with torch.no_grad():
-        outputs = model(**inputs)
-    probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
+    return classifier, text_generator, tts
+
+# 生成儿童友好的鸟类描述
+def generate_child_friendly_text(bird_name):
+    PROMPT = f"""以6-12岁儿童能理解的方式描述{bird_name}：
+1. 用比喻手法（如：羽毛像彩虹糖纸）
+2. 包含一个趣味冷知识（例如：每天吃相当于自身体重30%的食物）
+3. 语句长度不超过15个英文单词
+4. 避免使用专业术语"""
     
-    # 获取前1个预测结果
-    predicted_id = torch.argmax(probabilities).item()
-    labels = model.config.id2label
-    bird_species = labels[predicted_id]
-    confidence = round(probabilities[predicted_id].item(), 3)
+    response = text_generator(
+        PROMPT,
+        max_new_tokens=150,
+        temperature=0.7,
+        do_sample=True
+    )
     
-    return bird_species, confidence
+    return response[0]['generated_text'].split('\n')[2]
 
-# 2. 鸟类信息获取（使用维基百科API）
-def get_bird_info(species_name):
+# 主处理流程
+def process_image(image):
     try:
-        # 去除可能的多余标签（如模型输出中的括号内容）
-        clean_name = species_name.split("(")[0].strip()
-        # 从维基百科获取摘要（英文转中文）
-        summary = wikipedia.summary(clean_name, sentences=3, auto_suggest=False)
-        return summary
-    except (DisambiguationError, PageError):
-        return "抱歉，未找到该鸟类的详细信息。"
+        classification = classifier(image)
+        bird_name = classification[0]['label']
+        description = generate_child_friendly_text(bird_name)
+        speech = tts(description, forward_params={"speaker_id": 6})
+        
+        return {
+            "bird_name": bird_name,
+            "description": description,
+            "audio": speech["audio"]
+        }
+    except Exception as e:
+        return f"处理错误: {str(e)}"
 
-# 3. 文本转语音（使用TTS模型）
-def text_to_speech(text, output_file="bird_info.mp3"):
-    tts = pipeline("text-to-speech", model="tts_models/en_US/tacotron2")
-    speech = tts(text)
-    with open(output_file, "wb") as f:
-        f.write(speech["audio"])
-    return output_file
+# 初始化模型
+classifier, text_generator, tts = init_models()
 
-# 主函数
-def bird_knowledge_pipeline(image_url):
-    # 1. 鸟类识别
-    species, confidence = bird_classification(image_url)
-    print(f"识别结果：{species}（置信度：{confidence*100:.1f}%）")
+# 创建Gradio界面
+with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 800px}") as demo:
+    gr.Markdown("# 🐦 鸟类知识小课堂（Qwen3版）")
+    
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="上传鸟类图片", height=300)
+        audio_output = gr.Audio(label="语音��解", autoplay=True)
     
-    # 2. 获取详细信息
-    info = get_bird_info(species)
-    print(f"鸟类介绍：\n{info}")
+    with gr.Column():
+        name_output = gr.Textbox(label="识别到的鸟类")
+        text_output = gr.Textbox(label="趣味知识", lines=4)
     
-    # 3. 生成语音
-    audio_file = text_to_speech(f"这是{species}的介绍：{info}")
-    print(f"语音文件已保存：{audio_file}")
+    examples = gr.Examples(
+        examples=["eagle.jpg", "penguin.jpg", "peacock.jpg"],
+        inputs=image_input,
+        label="示例图片"
+    )
     
-    return species, info, audio_file
+    image_input.change(
+        process_image,
+        inputs=image_input,
+        outputs=[name_output, text_output, audio_output]
+    )
+
+# 部署配置
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)