Initial CutScript release - Open-source AI-powered text-based video editor
CutScript is a local-first, Descript-like video editor where you edit video by editing text. Delete a word from the transcript and it's cut from the video. Features: - Word-level transcription with WhisperX - Text-based video editing with undo/redo - AI filler word removal (Ollama/OpenAI/Claude) - AI clip creation for shorts - Waveform timeline with virtualized transcript - FFmpeg stream-copy (fast) and re-encode (4K) export - Caption burn-in and sidecar SRT generation - Studio Sound audio enhancement (DeepFilterNet) - Keyboard shortcuts (J/K/L, Space, Delete, Ctrl+Z/S/E) - Encrypted API key storage - Project save/load (.aive files) Architecture: - Electron + React + Tailwind (frontend) - FastAPI + Python (backend) - WhisperX for transcription - FFmpeg for video processing - Multi-provider AI support Performance optimizations: - RAF-throttled time updates - Zustand selectors for granular subscriptions - Dual-canvas waveform rendering - Virtualized transcript with react-virtuoso Built on top of DataAnts-AI/VideoTranscriber, completely rewritten as a desktop application. License: MIT
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Your Name
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backend/services/ai_provider.py
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211
backend/services/ai_provider.py
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"""
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Unified AI provider interface for Ollama, OpenAI, and Claude.
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"""
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import json
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import logging
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from typing import Optional, List
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import requests
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logger = logging.getLogger(__name__)
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class AIProvider:
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"""Routes completion requests to the configured provider."""
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@staticmethod
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def complete(
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prompt: str,
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provider: str = "ollama",
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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system_prompt: Optional[str] = None,
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temperature: float = 0.3,
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) -> str:
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if provider == "ollama":
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return _ollama_complete(prompt, model or "llama3", base_url or "http://localhost:11434", system_prompt, temperature)
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elif provider == "openai":
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return _openai_complete(prompt, model or "gpt-4o", api_key or "", system_prompt, temperature)
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elif provider == "claude":
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return _claude_complete(prompt, model or "claude-sonnet-4-20250514", api_key or "", system_prompt, temperature)
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else:
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raise ValueError(f"Unknown provider: {provider}")
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@staticmethod
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def list_ollama_models(base_url: str = "http://localhost:11434") -> List[str]:
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try:
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resp = requests.get(f"{base_url}/api/tags", timeout=3)
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if resp.status_code == 200:
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return [m["name"] for m in resp.json().get("models", [])]
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except Exception:
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pass
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return []
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def _ollama_complete(prompt: str, model: str, base_url: str, system_prompt: Optional[str], temperature: float) -> str:
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body = {
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"model": model,
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": temperature},
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}
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if system_prompt:
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body["system"] = system_prompt
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try:
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resp = requests.post(f"{base_url}/api/generate", json=body, timeout=120)
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resp.raise_for_status()
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return resp.json().get("response", "").strip()
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except Exception as e:
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logger.error(f"Ollama error: {e}")
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raise
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def _openai_complete(prompt: str, model: str, api_key: str, system_prompt: Optional[str], temperature: float) -> str:
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try:
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from openai import OpenAI
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client = OpenAI(api_key=api_key)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=temperature,
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)
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return response.choices[0].message.content.strip()
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except Exception as e:
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logger.error(f"OpenAI error: {e}")
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raise
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def _claude_complete(prompt: str, model: str, api_key: str, system_prompt: Optional[str], temperature: float) -> str:
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try:
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import anthropic
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client = anthropic.Anthropic(api_key=api_key)
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kwargs = {
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"model": model,
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"max_tokens": 4096,
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"temperature": temperature,
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"messages": [{"role": "user", "content": prompt}],
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}
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if system_prompt:
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kwargs["system"] = system_prompt
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response = client.messages.create(**kwargs)
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return response.content[0].text.strip()
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except Exception as e:
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logger.error(f"Claude error: {e}")
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raise
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def detect_filler_words(
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transcript: str,
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words: List[dict],
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provider: str = "ollama",
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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custom_filler_words: Optional[str] = None,
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) -> dict:
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"""
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Use an LLM to identify filler words in the transcript.
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Returns {"wordIndices": [...], "fillerWords": [{"index": N, "word": "...", "reason": "..."}]}
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"""
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word_list = "\n".join(f"{w['index']}: {w['word']}" for w in words)
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custom_line = ""
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if custom_filler_words and custom_filler_words.strip():
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custom_line = f"\n\nAdditionally, flag these user-specified filler words/phrases: {custom_filler_words.strip()}"
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prompt = f"""Analyze this transcript for filler words and verbal hesitations.
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Filler words include: um, uh, uh huh, hmm, like (when used as filler), you know, so (when starting sentences unnecessarily), basically, actually, literally, right, I mean, kind of, sort of, well (when used as filler).
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Also flag repeated words that indicate stammering (e.g., "I I I" or "the the").{custom_line}
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Here are the words with their indices:
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{word_list}
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Return ONLY a valid JSON object with this exact structure:
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{{"wordIndices": [list of integer indices to remove], "fillerWords": [{{"index": integer, "word": "the word", "reason": "brief reason"}}]}}
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Be conservative -- only flag clear filler words, not words that are part of meaningful sentences."""
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system = "You are a precise text analysis tool. Return only valid JSON, no explanation."
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result_text = AIProvider.complete(
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prompt=prompt,
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provider=provider,
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model=model,
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api_key=api_key,
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base_url=base_url,
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system_prompt=system,
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temperature=0.1,
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)
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try:
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start = result_text.find("{")
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end = result_text.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(result_text[start:end])
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except json.JSONDecodeError:
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logger.error(f"Failed to parse AI response as JSON: {result_text[:200]}")
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return {"wordIndices": [], "fillerWords": []}
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def create_clip_suggestion(
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transcript: str,
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words: List[dict],
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target_duration: int = 60,
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provider: str = "ollama",
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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) -> dict:
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"""
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Use an LLM to find the best clip segments in a transcript.
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"""
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word_list = "\n".join(
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f"{w['index']}: \"{w['word']}\" ({w.get('start', 0):.1f}s - {w.get('end', 0):.1f}s)"
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for w in words
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)
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prompt = f"""Analyze this transcript and find the most engaging {target_duration}-second segment(s) that would work well as a YouTube Short or social media clip.
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Look for: compelling stories, surprising facts, emotional moments, clear explanations, humor, or quotable statements.
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Words with indices and timestamps:
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{word_list}
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Return ONLY a valid JSON object:
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{{"clips": [{{"title": "short catchy title", "startWordIndex": integer, "endWordIndex": integer, "startTime": float, "endTime": float, "reason": "why this segment is engaging"}}]}}
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Suggest 1-3 clips, each approximately {target_duration} seconds long."""
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system = "You are a viral content expert. Return only valid JSON, no explanation."
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result_text = AIProvider.complete(
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prompt=prompt,
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provider=provider,
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model=model,
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api_key=api_key,
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base_url=base_url,
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system_prompt=system,
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temperature=0.5,
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)
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try:
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start = result_text.find("{")
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end = result_text.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(result_text[start:end])
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except json.JSONDecodeError:
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logger.error(f"Failed to parse clip suggestions: {result_text[:200]}")
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return {"clips": []}
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