Spaces:
Runtime error
Runtime error
File size: 4,016 Bytes
12611d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
from fastapi import FastAPI, Depends, HTTPException, Header, File, UploadFile
from pydantic import BaseModel
from typing import List, Dict, Optional
from email_ai import initialize_conversation, intro_message, get_chat_model_completions
import google.generativeai as genai
import os
from dotenv import load_dotenv
import speech_recognition as sr
from io import BytesIO
import wave
# Load environment variables
load_dotenv()
# gemini_api_key = os.getenv("GEMINI_API_KEY")
# genai.configure(api_key=gemini_api_key)
# Define a secret API key (use environment variables in production)
API_KEY = os.getenv("API_KEY")
app = FastAPI()
# Initialize speech recognizer
recognizer = sr.Recognizer()
# Pydantic models for request/response validation
class Message(BaseModel):
role: str
content: str
class ChatRequest(BaseModel):
message: str
class ChatResponse(BaseModel):
response: str
conversation: List[Message]
class Report(BaseModel):
response: str
message: str
timestamp: str
# Dependency to check the API key
async def verify_api_key(x_api_key: str = Header(...)):
if x_api_key != API_KEY:
raise HTTPException(status_code=403, detail="Unauthorized")
@app.get("/init", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
async def get_conversation():
global llm, chroma_retriever, conversation_bot
conversation_bot = [Message(role="bot", content=intro_message)]
llm, chroma_retriever = initialize_conversation()
return ChatResponse(
response=intro_message,
conversation=conversation_bot
)
@app.post("/chat", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
async def send_message(request: ChatRequest):
global conversation_bot
conversation_bot.append(Message(role="user", content=request.message))
response_assistant = get_chat_model_completions(llm, chroma_retriever, request.message)
conversation_bot.append(Message(role="bot", content=response_assistant.content))
return ChatResponse(
response=response_assistant.content,
conversation=conversation_bot
)
# Voice processing endpoint
@app.post("/process-voice")
async def process_voice(audio_file: UploadFile = File(...), dependencies=[Depends(verify_api_key)]):
# async def process_voice(name: str):
global conversation_bot
try:
# Read the audio file
contents = await audio_file.read()
audio_data = BytesIO(contents)
# Convert audio to wav format for speech recognition
with sr.AudioFile(audio_data) as source:
audio = recognizer.record(source)
# Perform speech recognition
text = recognizer.recognize_google(audio)
print(text)
conversation_bot.append(Message(role="user", content=text))
response_assistant = get_chat_model_completions(llm, chroma_retriever, text)
conversation_bot.append(Message(role="bot", content=response_assistant.content))
# print('response_assistant.content')
return {
"transcribed_text": text,
"response": response_assistant.content
}
except Exception as e:
return {"error": f"Error processing voice input: {str(e)}"}
@app.post("/report")
async def handle_feedback(
request: Report,
dependencies=[Depends(verify_api_key)]
):
# if x_api_key != VALID_API_KEY:
# raise HTTPException(status_code=403, detail="Invalid API key")
# Here you can store the feedback in your database
# For example:
# await db.store_feedback(message, is_positive)
return {"status": "success"}
@app.post("/reset")
async def reset_conversation():
global conversation_bot, llm, chroma_retriever
conversation_bot = [{'bot': intro_message}]
llm, chroma_retriever = initialize_conversation()
return {"status": "conversation reset"}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |