Convert natural language to SQL queries

!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

ds = load_dataset("Clinton/Text-to-sql-v1")

# Convert to a pandas DataFrame (selecting the "train" split as an example)
df = ds["train"].to_pandas()

# Select the top 300 rows
df = df.head(50)

# Display the DataFrame or save it
print(df)

Below are sql tables schemas paired with instruction that describes a task. Using valid SQLite, write a response that appropriately completes the request for the provided tables:

Here is the instruction: {{instruction}}

Here is the table: {{table}}

Here are some output examples, adhere to this output form:

SELECT home_team FROM table_name_77 WHERE away_team = "carlton"

SELECT "Yellow jersey" FROM table_3791 WHERE "Distance (km)" = '125'

SELECT COUNT(DISTINCT demographic.subject_id) FROM demographic INNER JOIN diagnoses ON demographic.hadm_id = diagnoses.hadm_id INNER JOIN lab ON demographic.hadm_id = lab.hadm_id WHERE diagnoses.icd9_code = "45620" AND lab.fluid = "Blood"

NO NEED TO INCLUDE   ```sql```  AROUND THE OUTPUT:

 ```sql SQL
SELECT Sex, COUNT(*) AS FacultyCount
FROM Faculty
GROUP BY Sex
ORDER BY FacultyCount DESC;

# Initialize the outputs list
outputs = []

# Iterate through each row in the DataFrame
for _, row in df.iterrows():
    # Extract the 'instruction' and 'input' columns for each row
    instruction = row["instruction"]
    table = row["input"]

    # Invoke the deployment for each row
    generation = client.deployments.invoke(
        key="text_to_SQL",  # Replace with your actual deployment key
        context={
            "environments": []
        },
        inputs={
            "table": table,
            "instruction": instruction
        },
        metadata={
            "custom-field-name": "custom-metadata-value"
        }
    )

    # Append the model's output to the outputs list
    outputs.append(generation.choices[0].message.content)

# Add the outputs as a new column in the DataFrame
df["output"] = outputs

# Save the results to a CSV file
df.to_csv("generated_sql_queries.csv", index=False)
print("Generated SQL queries saved to 'generated_sql_queries.csv'.")