File size: 5,883 Bytes
0c39cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8539a09
 
0c39cc3
 
 
8539a09
0c39cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8539a09
 
 
 
0c39cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8539a09
0c39cc3
 
8539a09
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c39cc3
 
 
8539a09
0c39cc3
 
8539a09
0c39cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8539a09
0c39cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8539a09
0c39cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
155f656
 
 
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
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
import asyncio
import base64
import json
import os
import pathlib
from typing import AsyncGenerator, Literal

import gradio as gr
import numpy as np
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastrtc import (
    AsyncStreamHandler,
    Stream,
    get_twilio_turn_credentials,
    wait_for_item,
)
from google import genai
from google.genai.types import (
    LiveConnectConfig,
    PrebuiltVoiceConfig,
    SpeechConfig,
    VoiceConfig,
    Content,
    Part
)
from gradio.utils import get_space
from pydantic import BaseModel

current_dir = pathlib.Path(__file__).parent
load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
    raise ValueError("GEMINI_API_KEY environment variable is not set")


def encode_audio(data: np.ndarray) -> str:
    """Encode Audio data to send to the server"""
    return base64.b64encode(data.tobytes()).decode("UTF-8")


class GeminiHandler(AsyncStreamHandler):
    """Handler for the Gemini API"""

    def __init__(
            self,
            expected_layout: Literal["mono"] = "mono",
            output_sample_rate: int = 24000,
            output_frame_size: int = 480,
    ) -> None:
        super().__init__(
            expected_layout,
            output_sample_rate,
            output_frame_size,
            input_sample_rate=16000,
        )
        self.input_queue: asyncio.Queue = asyncio.Queue()
        self.output_queue: asyncio.Queue = asyncio.Queue()
        self.quit: asyncio.Event = asyncio.Event()

    def copy(self) -> "GeminiHandler":
        return GeminiHandler(
            expected_layout="mono",
            output_sample_rate=self.output_sample_rate,
            output_frame_size=self.output_frame_size,
        )

    async def start_up(self):
        if not self.phone_mode:
            await self.wait_for_args()
            # Fix: Extract voice_name properly - it should be a string, not a list
            voice_name = self.latest_args[1] if len(self.latest_args) > 1 else "Puck"
        else:
            voice_name = "Puck"  # Default to Puck for phone mode

        client = genai.Client(
            api_key=api_key,
            http_options={"api_version": "v1alpha"},
        )

        config = LiveConnectConfig(
            response_modalities=["AUDIO"],  # type: ignore
            speech_config=SpeechConfig(
                voice_config=VoiceConfig(
                    prebuilt_voice_config=PrebuiltVoiceConfig(
                        voice_name=voice_name,
                    )
                )
            ),

            system_instruction=Content(
    parts=[Part(
        text="""You are an AI calling assistant for Ishwor Subedi, an AI/ML freelancer. When speaking with clients:

2. For professional inquiries, highlight these key skills concisely:
   - 2+ years in machine learning and AI
   - Computer Vision expertise
   - NLP capabilities
   - Software and mobile app development
   - Upwork freelancer with proven track record
3. For generic questions:
   - Provide brief, direct answers (1-2 sentences)
   - Avoid lengthy explanations
   - Always connect responses back to Ishwor's services when possible
4. Keep website reference simple: "Visit ishwor-subedi.com.np for portfolio details"
5. Speak in Hindi throughout
6. For unrelated topics: "Please contact Ishwor directly for assistance with this"

Maintain professional tone while keeping all responses concise and focused.
""")],
                role="user"

            )
        )
        async with client.aio.live.connect(
                model="gemini-2.0-flash-exp", config=config
        ) as session:
            async for audio in session.start_stream(
                    stream=self.stream(), mime_type="audio/pcm"
            ):
                if audio.data:
                    array = np.frombuffer(audio.data, dtype=np.int16)
                    self.output_queue.put_nowait((self.output_sample_rate, array))

    async def stream(self) -> AsyncGenerator[bytes, None]:
        while not self.quit.is_set():
            try:
                audio = await asyncio.wait_for(self.input_queue.get(), 0.1)
                yield audio
            except (asyncio.TimeoutError, TimeoutError):
                pass

    async def receive(self, frame: tuple[int, np.ndarray]) -> None:
        _, array = frame
        array = array.squeeze()
        audio_message = encode_audio(array)
        self.input_queue.put_nowait(audio_message)

    async def emit(self) -> tuple[int, np.ndarray] | None:
        return await wait_for_item(self.output_queue)

    def shutdown(self) -> None:
        self.quit.set()


stream = Stream(
    modality="audio",
    mode="send-receive",
    handler=GeminiHandler(),
    rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
    concurrency_limit=2,
    time_limit=90 if get_space() else None,
    additional_inputs=[
        gr.Dropdown(
            label="Voice",
            choices=[
                "Puck",
                "Charon",
                "Kore",
                "Fenrir",
                "Aoede",
            ],
            value="Puck",
        ),
    ],
)


class InputData(BaseModel):
    webrtc_id: str
    voice_name: str


app = FastAPI()

stream.mount(app)


@app.post("/input_hook")
async def _(body: InputData):
    stream.set_input(body.webrtc_id, body.voice_name)
    return {"status": "ok"}


@app.get("/")
async def index():
    rtc_config = get_twilio_turn_credentials() if get_space() else None
    html_content = (current_dir / "index.html").read_text()
    html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
    return HTMLResponse(content=html_content)


if __name__ == "__main__":
    import os
    import uvicorn

    port = int(os.environ.get("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)