call_agent.py

import os
import time
from groq import Groq
import deepgram_tts

GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "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")

PERSONA_PROMPTS = {
    "Medical Telehealth Assistant": (
        "You are Dr. Thalia, an empathetic and professional AI Telehealth Calling Assistant calling a patient. "
        "Your goal is to communicate lab results, answer patient medical concerns clearly, and schedule follow-ups. "
        "CRITICAL INSTRUCTIONS FOR VOICE SYNTHESIS:\n"
        "1. Speak naturally as if on a live phone call.\n"
        "2. Keep responses brief (1 to 3 short spoken sentences).\n"
        "3. Do NOT use bullet points, markdown bold, lists, or symbols like # or *.\n"
        "4. Spell out medical numbers clearly (e.g. 'two hundred forty milligrams per deciliter')."
    ),
    "Customer Support Specialist": (
        "You are Alex, a helpful AI Customer Service Calling Agent assisting a customer over the phone. "
        "Keep your tone polite, natural, and conversational. Keep responses to 1-3 spoken sentences without markdown formatting."
    ),
    "Outbound Sales & Qualification": (
        "You are Jordan, a friendly AI Outbound Account Manager calling a potential business client. "
        "Speak concisely, ask engaging follow-up questions, and maintain a warm phone persona."
    ),
    "Custom Assistant": (
        "You are an AI Voice Calling Agent on a live phone call. Speak naturally, warmly, and concisely."
    )
}

class AICallingAgent:
    def __init__(self):
        self.client = Groq(api_key=GROQ_API_KEY)
        self.model = "llama-3.3-70b-versatile"

    def process_call_turn(
        self,
        user_input: str,
        conversation_history: list,
        persona: str = "Medical Telehealth Assistant",
        voice_model: str = "aura-2-thalia-en"
    ) -> dict:
        """
        Processes a phone call conversational turn:
        1. Generates conversational LLM text response via Groq API.
        2. Synthesizes voice audio via Deepgram Aura-2 API.
        Returns dictionary with text, audio file path, latency, and updated history.
        """
        start_time = time.time()
        
        system_prompt = PERSONA_PROMPTS.get(persona, PERSONA_PROMPTS["Medical Telehealth Assistant"])
        
        messages = [{"role": "system", "content": system_prompt}]
        for msg in conversation_history:
            messages.append({"role": msg["role"], "content": msg["content"]})
        
        messages.append({"role": "user", "content": user_input})

        try:
            completion = self.client.chat.completions.create(
                model=self.model,
                messages=messages,
                temperature=0.7,
                max_tokens=250
            )
            agent_text = completion.choices[0].message.content.strip()
        except Exception as e:
            print(f"[AICallingAgent] Groq API Error: {e}")
            agent_text = "I apologize, I am experiencing a brief connection drop. Could you please repeat that?"

        # Generate Voice Audio via Deepgram
        audio_filepath = deepgram_tts.generate_voice_audio(agent_text, voice_model=voice_model)
        
        latency_ms = int((time.time() - start_time) * 1000)

        updated_history = list(conversation_history)
        updated_history.append({"role": "user", "content": user_input})
        updated_history.append({"role": "assistant", "content": agent_text})

        return {
            "agent_text": agent_text,
            "audio_filepath": audio_filepath,
            "latency_ms": latency_ms,
            "history": updated_history
        }