init

.gitignore

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+.cache.sqlite
+__pycache__/
+.vscode/
+latlong
+
+audio/
+venv/
+

install.sh

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+apt-get -y install espeak-ng

main.py

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+import tts
+from textgen import tool_funcs
+
+t = tts.TTS()
+
+save_path = 'audio/weather.wav'
+
+t.create_audio(tool_funcs.get_high_low(), save_path)
+
+
+

requirements.txt

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+kokoro>=0.9.4
+soundfile
+misaki[en]
+
+openmeteo-requests
+requests-cache
+retry-requests
+numpy
+pandas

textgen/llm.py

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+# Import the Llama class from the llama_cpp library
+import llama_cpp
+import json
+
+tools = json.loads(open('tools.json', 'r').read())['tools']
+
+class TextGen:
+
+    llm: llama_cpp.Llama
+
+    def __init__(self, model_path: str, n_ctx: int, n_gpu_layers: int):
+        # 1. Instantiate the Llama model
+        # Provide the path to your downloaded .gguf file
+        # n_ctx is the maximum context size (number of tokens) the model can handle.
+        # n_gpu_layers specifies how many layers to offload to the GPU. -1 means offload all possible layers.
+        llm = llama_cpp.Llama(
+        model_path="./models/mistral-7b-instruct-v0.2.Q4_K_M.gguf",  # Path to your GGUF model
+        n_ctx=n_ctx,           # Context window size
+        n_gpu_layers=n_gpu_layers,        # Offload all layers to GPU. Set to 0 if no GPU.
+        verbose=False         # Suppress verbose output
+        )
+
+    def generate(self, prompt: str) -> str:
+        # 3. Generate text
+        # The llm object is callable. Pass the prompt to it.
+        # max_tokens is the maximum number of tokens to generate.
+        # stop is a list of strings that will stop the generation when encountered.
+        # echo=True will include your prompt in the output.
+        output = self.llm(
+            prompt,
+            max_tokens=200,
+            echo=True
+        )
+
+        # 4. Print the result
+        # The output is a dictionary. The generated text is in 'choices'[0]['text'].
+        text = output['choices'][0]['text']
+        print(text)
+
+        return text

textgen/tool_funcs.py

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+from . import weather
+
+wapi = weather.OpenMeteo() # Weather API
+
+def get_high_low() -> str:
+
+    weather_dataframe = wapi.get_weather()
+
+    high = round(weather_dataframe.max(axis=0)['temperature_2m'])
+    low = round(weather_dataframe.min(axis=0)['temperature_2m'])
+
+    return f'The high today is {high}, and the low is {low}.'

textgen/tools.json

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+{
+    "tools" : [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get the weather where the user lives"
+            }
+        }
+    ]
+}

textgen/weather.py

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+import openmeteo_requests
+
+import pytz
+import pandas as pd
+import requests_cache
+from retry_requests import retry
+
+# Setup the Open-Meteo API client with cache and retry on error
+cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
+retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
+URL = "https://api.open-meteo.com/v1/forecast"
+
+
+class OpenMeteo:
+
+    openmeteo: openmeteo_requests.Client
+
+    def __init__(self):
+        self.openmeteo = openmeteo_requests.Client(session = retry_session)
+
+    def get_weather(self) -> pd.DataFrame:
+        params = {
+            "latitude": 36.0626,
+            "longitude": -94.1574,
+            "hourly": ["temperature_2m", "rain"],
+            "timezone": "America/Chicago",
+            # "forecast_days": 1,
+            "start_date": "2025-09-30",
+	        "end_date": "2025-09-30",
+            "temperature_unit": 'fahrenheit',
+            # "bounding_box": "-90,-180,90,180",
+            # "models": "dwd_icon_global"
+        }
+
+        responses = self.openmeteo.weather_api(URL, params=params)
+
+        # Process first location. Add a for-loop for multiple locations or weather models
+        response = responses[0]
+        print(f"Coordinates: {response.Latitude()}°N {response.Longitude()}°E")
+        print(f"Elevation: {response.Elevation()} m asl")
+        print(f"Timezone: {response.Timezone()}{response.TimezoneAbbreviation()}")
+        print(f"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s")
+
+        # Process hourly data. The order of variables needs to be the same as requested.
+        hourly = response.Hourly()
+        hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
+        hourly_rain = hourly.Variables(1).ValuesAsNumpy()
+
+        hourly_data = {"date": pd.date_range(
+            start = pd.to_datetime(hourly.Time(), unit = "s", utc = True).astimezone(pytz.timezone('US/Central')),
+            end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True).astimezone(pytz.timezone('US/Central')),
+            freq = pd.Timedelta(seconds = hourly.Interval()),
+            inclusive = "left"
+        )}
+
+        hourly_data["temperature_2m"] = hourly_temperature_2m
+        hourly_data['rain'] = hourly_rain
+
+        hourly_dataframe = pd.DataFrame(data = hourly_data)
+        print("\nHourly data\n", hourly_dataframe)
+
+        return hourly_dataframe
+
+

tts.py

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+from kokoro import KPipeline
+from IPython.display import display, Audio
+import soundfile as sf
+import torch
+
+class TTS:
+
+    pipeline = KPipeline(lang_code='a')
+
+    def __init__(self):
+        pass
+
+    # Should be saved as .wav
+    def create_audio(self, text: str, save_path: str) -> None:
+        generator = self.pipeline(text, voice='af_heart')
+        for i, (gs, ps, audio) in enumerate(generator):
+            print(i, gs, ps)
+            display(Audio(data=audio, rate=24000, autoplay=i==0))
+            sf.write(save_path, audio, 24000)
+