# filename: srt_refiner_v4.2.py import tkinter as tk from tkinter import filedialog, ttk, scrolledtext, messagebox import sv_ttk import threading import queue import os import requests # <--- 核心改动：使用 requests import json # <--- 核心改动：用于处理JSON数据 import srt import re from datetime import timedelta # --- 核心配置 --- OLLAMA_HOST = "http://127.0.0.1:11434" CONTEXT_WINDOW = 2 # --- GUI 应用 --- class App: def __init__(self, root): self.root = root self.root.title("字幕内容精炼师 V4.2 - Requests直连版") self.root.geometry("800x600") # --- 核心改动：移除 ollama.Client --- self.is_ollama_available = False self.srt_path = "" self.original_subs = [] self.refined_subs = [] self.processing_thread = None self.gui_queue = queue.Queue() self.vars = { "srt_path": tk.StringVar(), "ollama_model": tk.StringVar(), "status": tk.StringVar(value="准备就绪") } self.build_ui() sv_ttk.set_theme("dark") self.root.after(100, self.load_ollama_models) self.root.after(100, self.process_queue) def build_ui(self): # ... (UI部分完全不变) main_frame = ttk.Frame(self.root, padding=10) main_frame.pack(fill=tk.BOTH, expand=True) main_frame.rowconfigure(3, weight=1) main_frame.columnconfigure(0, weight=1) f1 = ttk.Frame(main_frame) f1.grid(row=0, column=0, sticky="ew", pady=(0, 10)) f1.columnconfigure(1, weight=1) ttk.Label(f1, text="SRT文件:").grid(row=0, column=0, padx=(0, 5)) ttk.Entry(f1, textvariable=self.vars['srt_path'], state="readonly").grid(row=0, column=1, sticky="ew", padx=(0, 5)) ttk.Button(f1, text="选择...", command=self.select_srt_file).grid(row=0, column=2) f2 = ttk.Frame(main_frame) f2.grid(row=1, column=0, sticky="ew", pady=(0, 10)) f2.columnconfigure(1, weight=1) ttk.Label(f2, text="Ollama模型:").grid(row=0, column=0, padx=(0, 5)) self.model_combo = ttk.Combobox(f2, textvariable=self.vars['ollama_model'], state="readonly") self.model_combo.grid(row=0, column=1, sticky="ew", padx=(0, 5)) f3 = ttk.Frame(main_frame) f3.grid(row=2, column=0, sticky="ew", pady=(0, 15)) self.start_button = ttk.Button(f3, text="开始精炼", style="Accent.TButton", command=self.start_processing, state="disabled") self.start_button.pack(side=tk.LEFT, padx=(0, 5)) self.save_button = ttk.Button(f3, text="另存为...", command=self.save_srt, state="disabled") self.save_button.pack(side=tk.LEFT) log_frame = ttk.Labelframe(main_frame, text="处理日志") log_frame.grid(row=3, column=0, sticky="nsew") log_frame.rowconfigure(0, weight=1) log_frame.columnconfigure(0, weight=1) self.log_text = scrolledtext.ScrolledText(log_frame, wrap=tk.WORD, state="disabled", height=10) self.log_text.grid(row=0, column=0, sticky="nsew", padx=5, pady=5) status_bar = ttk.Frame(main_frame) status_bar.grid(row=4, column=0, sticky="ew", pady=(5, 0)) self.progress_bar = ttk.Progressbar(status_bar, orient=tk.HORIZONTAL, mode='determinate') self.progress_bar.pack(side=tk.LEFT, fill=tk.X, expand=True, padx=(0, 10)) ttk.Label(status_bar, textvariable=self.vars['status']).pack(side=tk.LEFT) def select_srt_file(self): path = filedialog.askopenfilename(filetypes=[("SRT Subtitles", "*.srt")]) if not path: return try: with open(path, 'r', encoding='utf-8-sig') as f: content = f.read() self.original_subs = list(srt.parse(content)) self.srt_path = path self.vars['srt_path'].set(os.path.basename(path)) self.log_message(f"已加载文件: {os.path.basename(path)}, 共 {len(self.original_subs)} 条字幕。") if self.is_ollama_available: self.start_button.config(state="normal") self.save_button.config(state="disabled") self.refined_subs = [] except Exception as e: messagebox.showerror("加载失败", f"无法加载或解析文件: {e}") def save_srt(self): # ... (此方法不变) if not self.refined_subs: messagebox.showwarning("无内容", "没有可保存的精炼后字幕。") return original_basename = os.path.splitext(os.path.basename(self.srt_path))[0] save_path = filedialog.asksaveasfilename( defaultextension=".srt", initialfile=f"{original_basename}_refined.srt", filetypes=[("SRT Subtitles", "*.srt")] ) if not save_path: return try: content_to_save = srt.compose(self.refined_subs) with open(save_path, 'w', encoding='utf-8') as f: f.write(content_to_save) messagebox.showinfo("保存成功", f"精炼后的SRT文件已保存至:\n{save_path}") except Exception as e: messagebox.showerror("保存失败", f"无法保存文件: {e}") def start_processing(self): # ... (此方法不变) if not self.is_ollama_available: messagebox.showerror("错误", "Ollama 服务未连接，无法开始处理。") return if not self.original_subs: messagebox.showerror("错误", "请先加载一个SRT文件。") return if not self.vars['ollama_model'].get(): messagebox.showerror("错误", "请选择一个Ollama模型。") return self.start_button.config(state="disabled") self.save_button.config(state="disabled") self.log_text.config(state="normal"); self.log_text.delete(1.0, tk.END); self.log_text.config(state="disabled") self.refined_subs = [] self.progress_bar['value'] = 0 self.progress_bar['maximum'] = len(self.original_subs) self.processing_thread = threading.Thread( target=self.refine_worker, args=(list(self.original_subs), self.vars['ollama_model'].get()), daemon=True ) self.processing_thread.start() def log_message(self, msg): self.gui_queue.put({"type": "log", "data": msg}) def process_queue(self): # ... (此方法不变) try: while True: msg = self.gui_queue.get_nowait() msg_type = msg.get("type") if msg_type == "log": self.log_text.config(state="normal") self.log_text.insert(tk.END, msg["data"] + "\n") self.log_text.see(tk.END) self.log_text.config(state="disabled") elif msg_type == "error": self.log_message(f"错误: {msg['data']}") messagebox.showerror("Ollama 错误", msg['data']) elif msg_type == "models_loaded": models = msg['data'] if models: self.model_combo['values'] = models self.model_combo.set(models[0]) self.log_message(f"成功检测到模型: {', '.join(models)}") self.is_ollama_available = True if self.original_subs: self.start_button.config(state="normal") else: self.log_message("未检测到任何本地Ollama模型。") elif msg_type == "progress": self.progress_bar['value'] = msg['current'] self.vars['status'].set(f"处理中... {msg['current']}/{msg['total']}") elif msg_type == "result": self.refined_subs.append(msg['data']) elif msg_type == "finish": self.start_button.config(state="normal") self.save_button.config(state="normal") self.vars['status'].set("精炼完成！") self.log_message("\n--- 所有字幕精炼完成！---") messagebox.showinfo("完成", "所有字幕已成功精炼！") except queue.Empty: pass finally: self.root.after(100, self.process_queue) # --- 核心改动：使用 requests 获取模型列表 --- def load_ollama_models(self): self.log_message("正在连接Ollama并获取模型列表...") def _load(): try: # Ollama列出模型的API端点是 /api/tags response = requests.get(f"{OLLAMA_HOST}/api/tags", timeout=5) response.raise_for_status() # 如果状态码不是2xx，则抛出异常 models_data = response.json()['models'] model_names = [m['name'] for m in models_data] self.gui_queue.put({"type": "models_loaded", "data": model_names}) except requests.exceptions.RequestException as e: self.gui_queue.put({"type": "error", "data": f"无法连接到Ollama服务: {e}\n请确保Ollama正在运行并且地址({OLLAMA_HOST})正确。"}) except (KeyError, IndexError) as e: self.gui_queue.put({"type": "error", "data": f"解析Ollama模型列表时出错: {e}\n返回的数据格式可能不正确。"}) threading.Thread(target=_load, daemon=True).start() # --- 核心改动：使用 requests 调用生成API --- def refine_worker(self, subs_to_process, model_name): total_subs = len(subs_to_process) for i, current_sub in enumerate(subs_to_process): start_index = max(0, i - CONTEXT_WINDOW) end_index = min(total_subs, i + 1 + CONTEXT_WINDOW) context_block = subs_to_process[start_index:end_index] prompt = self.build_prompt(context_block, i - start_index) self.log_message(f"\n[正在处理 {i+1}/{total_subs}] 原文: {current_sub.content}") # 构造请求体 payload = { "model": model_name, "prompt": prompt, "stream": False, "options": {'temperature': 0.2, 'num_predict': 128} } try: # Ollama生成的API端点是 /api/generate response = requests.post(f"{OLLAMA_HOST}/api/generate", json=payload, timeout=60) response.raise_for_status() response_data = response.json() refined_text = response_data['response'].strip().replace("\n", " ") refined_text = re.sub(r'^["\'“‘]|["\'”’]$', '', refined_text) self.log_message(f"-> 精炼后: {refined_text}") new_sub = srt.Subtitle( index=current_sub.index, start=current_sub.start, end=current_sub.end, content=refined_text ) self.gui_queue.put({"type": "result", "data": new_sub}) except Exception as e: self.log_message(f"!! 警告: 处理第 {i+1} 条时出错: {e}。将使用原始内容。") self.gui_queue.put({"type": "result", "data": current_sub}) self.gui_queue.put({"type": "progress", "current": i + 1, "total": total_subs}) self.gui_queue.put({"type": "finish"}) def build_prompt(self, context_block, current_index_in_block): # ... (此方法不变) prompt_template = """你是一个专业的视频字幕精炼师。任务是优化“待处理字幕”，使其更适合专业配音。 规则： 1. 改为流畅、专业的书面语，但【重要】必须保留所有的核心操作指令和细节。 2. 【优先】去除明显的口语化词汇（如'嗯'、'啊'）、重复和不必要的填充词（如'然后'、'就是说'）。 3. 【次要】在不影响信息完整性的前提下，可以适当缩短句子。 4. 【重要】只输出精炼后的字幕文本，不要包含任何标签、解释或引号。。 --- [上下文] {context_text} --- [待处理字幕] {target_text} --- [精炼后的文本]：""" context_lines = [] target_text = "" for j, sub in enumerate(context_block): clean_content = sub.content.replace('\n', ' ').strip() if j == current_index_in_block: target_text = clean_content context_lines.append(f"-> {clean_content} <- (待处理)") else: context_lines.append(clean_content) return prompt_template.format(context_text="\n".join(context_lines), target_text=target_text) if __name__ == "__main__": root = tk.Tk() app = App(root) root.mainloop()