Build an intent classification chatbot

!pip install orq-ai-sdk datasets huggingface_hub

import os
from orq_ai_sdk import Orq

client = Orq(
    api_key=os.environ.get("ORQ_API_KEY", "your_orq_api_key"),
)
# Pass identity per-request: identity={"id": "<identity-id>"} in deployments.invoke() or responses.create()

from datasets import load_dataset

# Load dataset
dataset = load_dataset("Bhuvaneshwari/intent_classification")
df = dataset["train"].to_pandas()

You are tasked with identifying the intent behind user inputs based on the following intents:
'PlayMusic', 'AddToPlaylist', 'RateBook', 'Greetings', 'SearchScreeningEvent', 'BookRestaurant', 'GetWeather', 'Book Meeting', 'SearchCreativeWork', 'Cancellation', 'Affirmation', 'excitement'.

Here are some examples: 

Input: "Hey there, how are you doing?"
Intent: Greetings

Input: "Play the album Abbey Road by The Beatles."
Intent: PlayMusic

Input: "Add this song to my workout playlist."
Intent: AddToPlaylist

Input: "This book deserves a solid five-star rating."
Intent: RateBook

ONLY OUTPUT THE LABEL WITHOUT ''

here is the input that needs intent classification: {{text}} 

# Placeholder for deployment key
deployment_key = "intent_classification"

# Add predictions to the DataFrame
outputs = []
for _, row in df.iterrows():
    generation = client.deployments.invoke(
        key=deployment_key,
        context={"environments": []},
        inputs={"text": row["text"]},
        metadata={"custom-field-name": "custom-metadata-value"},
    )
    outputs.append(generation.choices[0].message.content)

# Add the output column
df["output"] = outputs

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Calculate metrics
accuracy = accuracy_score(df["intent"], df["output"])
precision = precision_score(df["intent"], df["output"], average="macro")
recall = recall_score(df["intent"], df["output"], average="macro")
f1 = f1_score(df["intent"], df["output"], average="macro")

# Display results
print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")