refactor(app): 替换本地 CLIP 模型为远程 Qwen3-VL-Embedding API

- 移除 CLIPEmbedder 本地模型类，改用远程图片 Embedding API 获取特征向量
  - 新增 get_image_embedding() 函数，支持重试机制
  - 移除本地图片上传功能，仅保留 URL 输入和示例图片选择
  - build_index() 增加进度条显示，索引失败时展示具体错误信息
  - 移除 torch、transformers、requests 依赖，新增 httpx
  - 更新界面文案，反映新的技术方案

app.py

@@ -1,21 +1,26 @@
 """
 病虫害以图搜图
-基于 CLIP 本地模型的图片 Embedding 相似度搜索
+基于图片 Embedding API 的相似度搜索
 """
 
 from __future__ import annotations
 
 import io
-import os
+import time
 from dataclasses import dataclass
 from typing import Literal
 
+import httpx
 import numpy as np
 import plotly.graph_objects as go
 import requests
 import streamlit as st
 from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
+
+# ─── API Config ──────────────────────────────────────────────────────────────
+IMAGE_EMBEDDING_API_URL = "https://llm.dev.maimaiag.com/qwen3-vl-embedding/v1/embeddings"
+EMBEDDING_MODEL = "Qwen3-VL-Embedding"
+API_KEY = "sk--VnOesEU5D8wnHjdg0MEsA"
 
 # ─── Page Config ────────────────────────────────────────────────────────────
 st.set_page_config(
@@ -165,35 +170,37 @@ EXAMPLE_IMAGES: list[tuple[str, str]] = [
 ]
 
 
-# ─── CLIP Embedder ───────────────────────────────────────────────────────────
+# ─── Embedding API ───────────────────────────────────────────────────────────
-class CLIPEmbedder:
+def get_image_embedding(image_url: str, text: str = "这是什么病虫害？", max_retries: int = 2) -> list[float]:
-    MODEL_NAME = "openai/clip-vit-base-patch32"
+    """调用远程 API 获取图片 Embedding，支持重试。"""
-
+    payload = {
-    def __init__(self) -> None:
+        "model": EMBEDDING_MODEL,
-        self._processor: CLIPProcessor | None = None
+        "messages": [
-        self._model: CLIPModel | None = None
+            {
-
+                "role": "user",
-    def _load(self) -> tuple[CLIPProcessor, CLIPModel]:
+                "content": [
-        if self._processor is None or self._model is None:
+                    {"type": "text", "text": text},
-            with st.spinner("首次启动正在加载 CLIP 模型，请稍候..."):
+                    {"type": "image_url", "image_url": {"url": image_url}},
-                self._processor = CLIPProcessor.from_pretrained(self.MODEL_NAME)
+                ],
-                self._model = CLIPModel.from_pretrained(self.MODEL_NAME)
+            }
-        return self._processor, self._model
+        ],
-
+    }
-    def embed(self, image: Image.Image) -> np.ndarray:
+    last_error = None
-        processor, model = self._load()
+    for attempt in range(max_retries + 1):
-        inputs = processor(images=image, return_tensors="pt")
+        try:
-        image_features = model.get_image_features(**inputs)
+            resp = httpx.post(
-        vec = image_features.detach().cpu().numpy().flatten()
+                IMAGE_EMBEDDING_API_URL,
-        norm = np.linalg.norm(vec)
+                headers={"Content-Type": "application/json"},
-        if norm == 0:
+                json=payload,
-            return vec
+                timeout=120,
-        return vec / norm
+            )
-
+            resp.raise_for_status()
-
+            return resp.json()["data"][0]["embedding"]
-@st.cache_resource(show_spinner=False)
+        except httpx.HTTPStatusError as e:
-def get_embedder() -> CLIPEmbedder:
+            last_error = e
-    return CLIPEmbedder()
+            if attempt < max_retries:
+                time.sleep(2 * (attempt + 1))
+    raise last_error
 
 
 # ─── Utilities ───────────────────────────────────────────────────────────────
@@ -215,21 +222,24 @@ def load_image(source: str | io.BytesIO) -> Image.Image | None:
     return img
 
 
-def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+def cosine_similarity(a: list[float], b: list[float]) -> float:
-    return float(np.dot(a, b))
+    a_arr = np.array(a)
+    b_arr = np.array(b)
+    norm_a = np.linalg.norm(a_arr)
+    norm_b = np.linalg.norm(b_arr)
+    if norm_a == 0 or norm_b == 0:
+        return 0.0
+    return float(np.dot(a_arr, b_arr) / (norm_a * norm_b))
 
 
-@st.cache_data(show_spinner=False)
+@st.cache_resource
-def build_index() -> tuple[list[dict], list[str], list[str]]:
+def build_index() -> tuple[list[dict], list[str], list[tuple[str, str]]]:
-    embedder = get_embedder()
     items, succeeded, failed = [], [], []
-    for pest in PEST_KNOWLEDGE:
+    progress = st.progress(0, text="正在构建图片索引...")
-        img = _load_image_raw(pest.url)
+    total = len(PEST_KNOWLEDGE)
-        if img is None:
+    for i, pest in enumerate(PEST_KNOWLEDGE):
-            failed.append(pest.name)
-            continue
         try:
-            embedding = embedder.embed(img)
+            embedding = get_image_embedding(pest.url, text=pest.name)
             items.append({
                 "name": pest.name,
                 "url": pest.url,
@@ -240,40 +250,32 @@ def build_index() -> tuple[list[dict], list[str], list[str]]:
                 "category": pest.category,
             })
             succeeded.append(pest.name)
-        except Exception:
+        except Exception as e:
-            failed.append(pest.name)
+            failed.append((pest.name, str(e)))
+        progress.progress((i + 1) / total, text=f"正在构建图片索引 ({i + 1}/{total})...")
+    progress.empty()
     return items, succeeded, failed
 
 
 # ─── Sidebar ─────────────────────────────────────────────────────────────────
 with st.sidebar:
     st.header("🌿 病虫害以图搜图")
-    st.caption("上传图片，智能识别相似病虫害")
+    st.caption("输入图片 URL，通过图片 Embedding 搜索相似病虫害")
     st.divider()
 
     st.subheader("🖼️ 输入方式")
-    input_mode = st.radio("", ["上传本地图片", "输入图片 URL", "选择示例图片"], label_visibility="collapsed")
+    input_mode = st.radio("", ["输入图片 URL", "选择示例图片"], label_visibility="collapsed")
 
     # 初始化 session_state
     if "query_url" not in st.session_state:
         st.session_state.query_url = ""
-    if "query_image_bytes" not in st.session_state:
-        st.session_state.query_image_bytes = None
 
     query_source = None
     query_url = ""
 
-    if input_mode == "上传本地图片":
+    if input_mode == "输入图片 URL":
-        uploaded = st.file_uploader("选择图片", type=["jpg", "jpeg", "png", "webp"])
-        if uploaded is not None:
-            st.session_state.query_image_bytes = uploaded.getvalue()
-            st.session_state.query_url = ""
-        if st.session_state.query_image_bytes is not None:
-            query_source = io.BytesIO(st.session_state.query_image_bytes)
-    elif input_mode == "输入图片 URL":
         query_url = st.text_input("图片 URL", value=st.session_state.query_url, placeholder="https://example.com/image.jpg")
         st.session_state.query_url = query_url
-        st.session_state.query_image_bytes = None
         if query_url.strip():
             query_source = query_url.strip()
     else:
@@ -283,7 +285,6 @@ with st.sidebar:
             with cols[idx % 2]:
                 if st.button(name, key=f"ex_{name}"):
                     st.session_state.query_url = url
-                    st.session_state.query_image_bytes = None
                     st.rerun()
         if st.session_state.query_url:
             query_url = st.session_state.query_url
@@ -298,57 +299,52 @@ with st.sidebar:
     st.divider()
     st.info(
         "**使用说明**\n\n"
-        "1. 上传病虫害患处图片\n"
+        "1. 输入病虫害图片 URL 或选择示例\n"
-        "2. 系统自动提取图像特征\n"
+        "2. 系统调用 Embedding API 提取图像特征\n"
         "3. 与知识库比对返回相似结果\n"
         "4. 参考症状与防治建议"
     )
 
 # ─── Build Index ─────────────────────────────────────────────────────────────
+with st.spinner("首次加载需要构建图片索引，请稍候..."):
     index_items, succeeded, failed = build_index()
 
 # ─── Main Layout ─────────────────────────────────────────────────────────────
 st.title("🌿 病虫害以图搜图")
-st.caption("基于 CLIP 视觉模型的病虫害相似度检索与防治建议")
+st.caption("基于图片 Embedding API 的病虫害相似度检索与防治建议")
 
 # Status badges
-if succeeded:
+st.success(f"📚 图片索引构建完成，成功 {len(succeeded)} 张")
-    st.badge(f"📚 知识库 {len(succeeded)} 种", color="blue")
 if failed:
-    st.badge(f"⚠️ 索引失败 {len(failed)} 种", color="red")
+    st.warning(f"以下 {len(failed)} 张图片索引失败：")
+    for name, err in failed:
+        st.error(f"- **{name}**：{err}")
 
 st.markdown("<br>", unsafe_allow_html=True)
 
 # ─── Search Logic ────────────────────────────────────────────────────────────
-if search_clicked:
+if search_clicked and query_url.strip():
-    if query_source is None:
+    if not index_items:
-        st.warning("请先上传图片、输入图片 URL 或选择示例图片后再点击搜索")
-    elif not index_items:
         st.warning("知识库索引为空，请检查网络连接后刷新页面重试。")
     else:
-        query_img = load_image(query_source)
+        with st.spinner("正在分析图片并搜索..."):
-        if query_img is not None:
+            try:
+                query_embedding = get_image_embedding(query_url.strip())
+
                 col_query, col_preview = st.columns([1, 3])
                 with col_query:
                     st.subheader("🔍 查询图片")
+                    query_img = load_image(query_url.strip())
+                    if query_img is not None:
                         st.image(query_img, use_container_width=True)
 
                 with col_preview:
-                st.subheader("⏳ 正在分析...")
-                progress = st.progress(0, text="提取图像特征...")
-
-                embedder = get_embedder()
-                query_embedding = embedder.embed(query_img)
-                progress.progress(50, text="比对知识库...")
-
                     scores = []
                     for item in index_items:
                         sim = cosine_similarity(query_embedding, item["embedding"])
                         scores.append((sim, item))
                     scores.sort(key=lambda x: x[0], reverse=True)
                     results = scores[:top_k]
-                progress.progress(100, text="搜索完成")
-                progress.empty()
 
                     st.subheader(f"🏆 搜索结果（Top-{len(results)}）")
 
@@ -408,6 +404,9 @@ if search_clicked:
                         f"建议结合田间实际情况进一步确认，参考防治方案：**{best['treatment']}**"
                     )
 
+            except Exception as e:
+                st.error(f"搜索失败: {e}")
+
 # ─── Footer ───────────────────────────────────────────────────────────────────
 st.divider()
-st.caption("病虫害以图搜图 · 基于 CLIP 视觉模型 · 结果仅供参考，请结合田间实际情况判断")
+st.caption("病虫害以图搜图 · 基于 Qwen3-VL-Embedding · 结果仅供参考，请结合田间实际情况判断")

pyproject.toml

@@ -5,14 +5,12 @@ description = "病虫害以图搜图 — 基于图片 Embedding 的相似度搜
 readme = "README.md"
 requires-python = ">=3.14"
 dependencies = [
+    "httpx>=0.28.1",
     "numpy>=2.3.5",
     "pillow>=11.2.1",
     "plotly>=6.5.0",
-    "requests>=2.32.3",
     "ruff>=0.14.8",
     "streamlit==1.52.1",
-    "torch>=2.7.0",
-    "transformers>=4.51.3",
 ]
 
 [[tool.uv.index]]