import gradio as gr import pandas as pd import io def analyze_excel(file, target_low, low_min, low_max, target_high, high_min, high_max, dwell_time, sample_interval_min): if file is None or None in [target_low, low_min, low_max, target_high, high_min, high_max]: return pd.DataFrame(), "請先上傳 Excel 檔案並確認欄位已填寫！" try: if hasattr(file, "name"): file_path_or_bytes = file.name else: file_path_or_bytes = io.BytesIO(file) try: df_raw = pd.read_excel(file_path_or_bytes, header=None) except Exception: df_raw = pd.read_csv(file_path_or_bytes, header=None) header_idx = None for idx, row in df_raw.iterrows(): row_str_list = [str(val) for val in row.values] if any('Date' in s for s in row_str_list) and any('Time' in s for s in row_str_list): header_idx = idx break if header_idx is None: return pd.DataFrame(), "❌ 找不到 'Date' 或 'Time' 欄位，請確認檔案格式！" headers = df_raw.iloc[header_idx].values clean_headers = [] for i, h in enumerate(headers): h_str = str(h).strip() if pd.isna(h) or h_str in ['nan', 'None', '']: clean_headers.append(f"Col_{i}") else: clean_headers.append(h_str) data = df_raw.iloc[header_idx + 1:].copy().reset_index(drop=True) data.columns = clean_headers temp_cols = [col for col in clean_headers if col not in ['Date', 'Time'] and not col.startswith('Col_')] if not temp_cols: return pd.DataFrame(), "❌ 未找到有效的溫度 Channel 欄位！" for col in temp_cols: data[col] = pd.to_numeric(data[col], errors='coerce') matched_indices = [] low_min_f, low_max_f = float(low_min), float(low_max) high_min_f, high_max_f = float(high_min), float(high_max) for idx, row in data[temp_cols].iterrows(): vals = [v for v in row.values if pd.notna(v) and isinstance(v, (int, float))] in_low = any(low_min_f <= v <= low_max_f for v in vals) in_high = any(high_min_f <= v <= high_max_f for v in vals) if in_low or in_high: matched_indices.append(idx) filtered_df = data.loc[matched_indices].copy() filtered_df = filtered_df.fillna("") status_msg = f"✅ 分析成功！共撈出 {len(filtered_df)} 筆符合高低溫區間的數據。" return filtered_df, status_msg except Exception as e: return pd.DataFrame(), f"❌ 處理時發生錯誤：{str(e)}" with gr.Blocks(title="Thermal Cycle 熱循環數據分析工具") as demo: gr.Markdown("## 🌡️ Thermal Cycle 熱循環數據分析工具") with gr.Row(): file_input = gr.File(label="上傳 Excel 或 CSV 檔案", file_types=[".xlsx", ".xls", ".csv"]) gr.Markdown("### ⚙️ 溫度條件設定") with gr.Row(): target_low_input = gr.Number(label="低溫設定目標", value=-40) low_min_input = gr.Number(label="低溫下限值", value=-47) low_max_input = gr.Number(label="低溫上限值", value=-37) with gr.Row(): target_high_input = gr.Number(label="高溫設定目標", value=100) high_min_input = gr.Number(label="高溫下限值", value=97) high_max_input = gr.Number(label="高溫上限值", value=103) with gr.Row(): dwell_time_input = gr.Number(label="持溫時間 (min)", value=30) sample_interval_input = gr.Number(label="採樣間隔 (min)", value=1) status_output = gr.Textbox(label="執行狀態", interactive=False) gr.Markdown("### 🔍 極限溫度過濾結果資料表") table_output = gr.Dataframe(label="符合條件的數據明細", interactive=False) inputs = [ file_input, target_low_input, low_min_input, low_max_input, target_high_input, high_min_input, high_max_input, dwell_time_input, sample_interval_input ] for inp in inputs: inp.change(fn=analyze_excel, inputs=inputs, outputs=[table_output, status_output]) demo.launch()