🚀 Complete AI Integration Platform

import requests
from typing import BinaryIO, IO

class SAMO_API_Client:
    def __init__(self, base_url="https://api.example.com"):
        # Replace 'https://api.example.com' with your actual deployment URL
        self.base_url = base_url
        self.session = requests.Session()

    def analyze_emotion(self, text: str) -> dict:
        try:
            response = self.session.post(
                f"{self.base_url}/predict",
                json={"text": text},
                headers={"Content-Type": "application/json"},
                timeout=10
            )
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            return {"error": str(e)}

    def transcribe_voice(self, audio_file: BinaryIO | IO[bytes]) -> dict:
        if not hasattr(audio_file, "read"):
            return {"error": "audio_file must be a binary file-like object (supports .read())"}
        files = {"audio_file": audio_file}
        try:
            response = self.session.post(
                f"{self.base_url}/transcribe/voice",
                files=files,
                timeout=30
            )
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            return {"error": str(e)}

# Example usage
client = SAMO_API_Client()
emotions = client.analyze_emotion("I'm excited!")
# Returns: [{"emotion": "excitement", "confidence": 0.92}]

async function analyzeEmotion(text) {
  const response = await fetch(
    'https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal',
    {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ text, generate_summary: false })
    }
  );
  return await response.json();
}

// Example usage
const analysis = await analyzeEmotion("This is amazing!");
console.log(analysis.emotion_analysis?.emotions);

import pandas as pd
import requests

def analyze_dataset(texts: list) -> pd.DataFrame:
    results = []
    for text in texts:
        analysis = requests.post(
            "https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal",
            json={"text": text, "generate_summary": False}
        ).json()
        results.append({
            'text': text,
            'emotions': analysis.get('emotion_analysis', {}).get('emotions', {})
        })
    return pd.DataFrame(results)

// React Native / Flutter
const analyzeUserFeedback = async (feedback) => {
  try {
    const response = await fetch(
      'https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal',
      {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ text: feedback, generate_summary: false })
      }
    );
    const analysis = await response.json();
    return analysis;
  } catch (error) {
    console.error('Error:', error);
  }
};