Update app.py

app.py

@@ -1,31 +1,50 @@
+import pandas as pd
 from transformers import pipeline
 import gradio as gr
 
 # 載入 Hugging Face 的中文三分類情緒模型
 classifier = pipeline("text-classification", model="uer/roberta-base-finetuned-dianping-chinese", tokenizer="uer/roberta-base-finetuned-dianping-chinese")
 
-# 分析函式
-def analyze_sentiment(text):
-    result = classifier(text)[0]
-    label = result["label"]
-    score = round(result["score"], 4)
+# 分析函式（接收 CSV）
+def analyze_csv(file):
+    df = pd.read_csv(file)
 
-    if label == "LABEL_0":
-        sentiment = "負向情緒"
-    elif label == "LABEL_1":
-        sentiment = "中立情緒"
-    elif label == "LABEL_2":
-        sentiment = "正向情緒"
-    else:
-        sentiment = "無法判斷"
+    # 檢查是否有 "text" 欄位
+    if "text" not in df.columns:
+        return "錯誤：CSV 檔案中必須包含 'text' 欄位。"
 
-    return f"判斷結果：{sentiment}\n信心分數：{score}"
+    # 對每一列文字做情緒分析
+    results = []
+    for text in df["text"]:
+        result = classifier(str(text))[0]
+        label = result["label"]
+        score = round(result["score"], 4)
 
-# Gradio 介面
+        if label == "LABEL_0":
+            sentiment = "負向"
+        elif label == "LABEL_1":
+            sentiment = "中立"
+        elif label == "LABEL_2":
+            sentiment = "正向"
+        else:
+            sentiment = "未知"
+
+        results.append({"label": sentiment, "score": score})
+
+    # 加回原始 dataframe
+    df["情緒判斷"] = [r["label"] for r in results]
+    df["信心分數"] = [r["score"] for r in results]
+
+    # 輸出為新的 csv 檔案
+    output_file = "/tmp/output.csv"
+    df.to_csv(output_file, index=False)
+    return output_file
+
+# Gradio 介面（接收檔案，回傳檔案）
 gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(lines=4, placeholder="請輸入中文內容"),
-    outputs="text",
-    title="中文情緒分析系統（正向／中立／負向）",
-    description="輸入一段中文，模型會自動判斷情緒傾向"
+    fn=analyze_csv,
+    inputs=gr.File(label="上傳包含 'text' 欄位的 CSV 檔案", file_types=[".csv"]),
+    outputs=gr.File(label="下載標註後的 CSV 檔案"),
+    title="中文情緒分析系統（批次處理）",
+    description="上傳一份 CSV，系統會針對 'text' 欄做情緒分析，並下載結果。"
 ).launch()