Audio Transcription And Evaluation With Gemini Flash

# Note: This example uses Python SDK v7
print('#### Installing arize SDK')

! pip install "arize[Tracing]>=7.1.0"

print('#### arize SDK installed!')

import os

os.environ['ARIZE_API_KEY'] = ARIZE_API_KEY

from datetime import datetime, timezone, timedelta

from arize.exporter import ArizeExportClient
from arize.utils.types import Environments

client = ArizeExportClient()

print('#### Exporting your primary dataset into a dataframe.')

primary_df = client.export_model_to_df(
    space_id=getpass.getpass(prompt="Enter your ARIZE SPACE ID Key: "),
    model_id=PROJECT_NAME,
    where="name = 'process_audio'", #Just pull the spans with name = "process_audio"
    environment=Environments.TRACING,
    start_time = datetime.now(timezone.utc) - timedelta(days=1),
    end_time = datetime.now(timezone.utc) #pull traces for the last 24 hours
)

#set the column in the dataframe to match the variable name used in our eval template
primary_df["output"] = primary_df["attributes.output.value"]

SENTIMENT_EVAL_TEMPLATE = """

You are a helpful AI bot that checks for the sentiment in the output text. Your task is to evaluate the sentiment of the given output and categorize it as positive, neutral, or negative.

Here is the data:
[BEGIN DATA]
============
[Output]: {attributes.output.value}
============
[END DATA]

Determine the sentiment of the output based on the content and context provided. Your response should be ONLY a single word, either "positive", "neutral", or "negative", and should not contain any text or characters aside from that word.

Then write out in a step by step manner an EXPLANATION to show how you determined the sentiment of the output.  Do not include any text or characters aside from the EXPLANATION.

Your response should follow the format of the example response below. Provide a single LABEL and a single EXPLANATION. Do not include any special characters in the response. Do not include special characters such as "#" in your response.

Example response:

EXPLANATION: An explanation of your reasoning for why the label is "positive", "neutral", or "negative"
LABEL: "positive" or "neutral" or "negative"

"""

#Gemini as LLM as a Judge - LLM Classify

#google auth to access the Gemini model
!gcloud auth application-default login # authenticate with google
!gcloud config set project audioevals # you must have a valid project id in your google cloud account first

import pandas as pd
from phoenix.evals import (GeminiModel, llm_classify)

#We will use Gemini 1.5 pro to evaluate the text transcription
project_id = "audioevals" # Set this to your google project id
gemini_model = GeminiModel(model="gemini-1.5-pro", project=project_id)

rails = ["positive", "neutral", "negative"]

evals_df = llm_classify(
    data=primary_df,
    template=SENTIMENT_EVAL_TEMPLATE,
    model=gemini_model,
    rails=rails,
    provide_explanation=True
)

#set eval labels
evals_df["eval.sentiment.label"] = evals_df["label"]
evals_df["eval.sentiment.explanation"] = evals_df["explanation"]
evals_df["context.span_id"] = primary_df["context.span_id"]

evals_df.head()