52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
from openai import OpenAI
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from fastapi import FastAPI, File, Response, Request
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from pydantic import BaseModel
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import whisper
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app = FastAPI()
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openAI_clinet = OpenAI()
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model = whisper.load_model("base")
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class ConversationMessege(BaseModel):
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role: str
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content: str
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class Conversation(BaseModel):
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messages: list[ConversationMessege]
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@app.post("/get-text")
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def get_text(response: Response, audio: bytes = File()):
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response.headers["Access-Control-Allow-Origin"] = "*"
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with open("audio", "wb") as f:
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f.write(audio)
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# transcript = openAI_clinet.audio.transcriptions.create(
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# model="whisper-1",
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# file=audio,
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# response_format="text",
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# RequestBody
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# )
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result = model.transcribe("audio")
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data = {"len": len(audio), "user-transcript": result["text"]}
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return data
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@app.post("/conversation")
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async def get_next_response(request: Request, response: Response):
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response.headers["Access-Control-Allow-Origin"] = "*"
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#role = "test"
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#res_msg = "temp test response"
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messages = await request.json()
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res = openAI_clinet.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=messages,
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)
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res_msg = res.choices[0].message.content
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role = res.choices[0].message.role
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print(messages)
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print(res_msg)
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return {"role": role, "content": res_msg}
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