Initial commit: 25 scripts Python sauvegardés

Date: 2026-08-28 11:43:34 EDT
2026-08-28 11:43:34 -04:00
parent 0ba9701e0f
commit 994ea86d47
204 changed files with 70104 additions and 2 deletions
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+Pipecat Bot Template — OpenAILLMService avec API Hermes
+========================================================
+Template minimal pour un bot texte via WebSocket.
+Adapté de ~/workspace/jarvis-pipecat/bot.py
+
+Prérequis: pipecat-ai[websocket]>=1.4.0, openai, uvicorn, websockets
+"""
+
+import json, os, subprocess, sys
+from fastapi import FastAPI, WebSocket
+from fastapi.responses import HTMLResponse
+import uvicorn
+
+# ⚠️ Pipecat 1.4.0 : utiliser `import module as m` (from-import cassé)
+import pipecat.frames.frames as pframes
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams, FastAPIWebsocketTransport
+
+# ─── Config ──────────────────────────────────────────────────
+PORT = int(os.environ.get("BOT_PORT", "7081"))
+HERMES_URL = os.environ.get("HERMES_API_URL", "http://localhost:8642/v1")
+HERMES_KEY = os.environ.get("HERMES_API_KEY", "")
+SYSTEM_PROMPT = "Tu es un assistant vocal concis. Parle en français, 3 phrases max."
+
+
+# ─── Processeur custom : texte → LLMMessagesFrame ────────────
+class TextToLLMProcessor(FrameProcessor):
+    """Remplace le context aggregator (buggé en 1.4.0)."""
+
+    def __init__(self, system_prompt: str):
+        super().__init__()
+        self._messages = [{"role": "system", "content": system_prompt}]
+
+    async def process_frame(self, frame, direction):
+        await self.push_frame(frame, direction)
+        if isinstance(frame, pframes.InputTransportMessageFrame):
+            text = frame.message
+            if isinstance(text, str) and text.strip():
+                self._messages.append({"role": "user", "content": text})
+                if len(self._messages) > 21:
+                    self._messages = [self._messages[0]] + self._messages[-20:]
+                await self.push_frame(pframes.LLMMessagesFrame(messages=list(self._messages)))
+        elif isinstance(frame, pframes.LLMFullResponseStartFrame):
+            self._response_text = ""
+        elif isinstance(frame, pframes.TextFrame):
+            if hasattr(self, '_response_text'):
+                self._response_text += frame.text
+        elif isinstance(frame, pframes.LLMFullResponseEndFrame):
+            if hasattr(self, '_response_text') and self._response_text.strip():
+                self._messages.append({"role": "assistant", "content": self._response_text.strip()})
+                del self._response_text
+
+
+# ─── App ─────────────────────────────────────────────────────
+app = FastAPI()
+
+
+@app.websocket("/ws")
+async def ws_endpoint(websocket: WebSocket):
+    transport = FastAPIWebsocketTransport(
+        websocket=websocket,
+        params=FastAPIWebsocketParams(add_wav_header=False, session_timeout=None),
+    )
+    llm = OpenAILLMService(
+        api_key=HERMES_KEY,
+        base_url=HERMES_URL,
+        settings=OpenAILLMService.Settings(model="Hermes Agent", temperature=0.8, max_tokens=300, system_instruction=SYSTEM_PROMPT),
+    )
+    proc = TextToLLMProcessor(SYSTEM_PROMPT)
+    pipeline = Pipeline(processors=[transport.input(), proc, llm, transport.output()])
+    task = PipelineTask(pipeline)
+    runner = PipelineRunner()
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=PORT)