Delete handler.py

handler.py

@@ -1,138 +0,0 @@
-import json
-import os
-import torch
-from transformers import TextStreamer  # if needed elsewhere
-from unsloth import FastLanguageModel  # Assumes FastLanguageModel supports loading a base model
-from peft import PeftModel  # For loading the adapter onto the base model
-
-# Set parameters
-max_seq_length = 4096 
-dtype = None 
-load_in_4bit = False 
-
-# Define the base model identifier (the full model from Hugging Face Hub)
-base_model_id = "meta-llama/Llama-3.2-3B-Instruct"
-
-# 1. Load the base model and tokenizer from the Hub.
-# (This downloads the complete base model with all weights.)
-base_model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name=base_model_id, 
-    max_seq_length=max_seq_length,
-    dtype=dtype,
-    load_in_4bit=load_in_4bit,
-)
-
-# 2. Load your LoRA adapter weights from your repository.
-#    Here, "betterdataai/large-tabular-model" should be the local directory or identifier where the adapter weights reside.
-#    Ensure that this path contains the adapter weights (e.g. adapter_model.safetensors) and configuration.
-model = PeftModel.from_pretrained(
-    base_model, 
-    "betterdataai/large-tabular-model",  # Path to your adapter weights
-    torch_dtype=torch.float16,
-).eval()
-
-# 3. Prepare the merged model for inference.
-FastLanguageModel.for_inference(model)
-
-def prompt_transformation(prompt):
-    initial_prompt = """
-    We have the following natural language query:
-    "{}"
-    
-    Transform the above natural language query into a formalized prompt format. The format should include:
-    
-    1. A sentence summarizing the objective.
-    2. A description of the columns, including their data types and examples.
-    3. Four example rows of the dataset in CSV format.
-    
-    An example of this format is as follows, please only focus on the format, not the content:
-    
-    "You are tasked with generating a synthetic dataset based on the following description. The dataset represents employee information. The dataset should include the following columns:
-    
-    - NAME (String): Employee's full name, consisting of a first and last name (e.g., "John Doe", "Maria Lee", "Wei Zhang").
-    - GENDER (String): Employee's gender (e.g., "Male", "Female").
-    - EMAIL (String): Employee's email address, following the standard format.
-    - CITY (String): City where the employee resides (e.g., "New York", "London", "Beijing").
-    - COUNTRY (String): Country where the employee resides (e.g., "USA", "UK", "China").
-    - SALARY (Float): Employee's annual salary, a value between 30000 and 150000 (e.g., 55000.0, 75000.0).
-    
-    Here are some examples:
-    NAME,GENDER,EMAIL,CITY,COUNTRY,SALARY
-    John Doe,Male,john.doe@example.com,New York,USA,56000.0
-    Maria Lee,Female,maria.lee@nus.edu.sg,London,UK,72000.0
-    Wei Zhang,Male,wei.zhang@meta.com,Beijing,China,65000.0
-    Sara Smith,Female,sara.smith@orange.fr,Paris,France,85000.0"
-    
-    Here is the transformed query from the given natural language query:
-    """
-    messages = [
-        {"role": "system", "content": initial_prompt.format(prompt)},
-        {"role": "user", "content": "transform the given natural language text to the designated format"}
-    ]
-    
-    inputs = tokenizer.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,  # Required for generation
-        return_tensors="pt",
-    ).to("cuda")
-    
-    output_ids = model.generate(
-        input_ids=inputs, 
-        max_new_tokens=4096, 
-        use_cache=True, 
-        temperature=1.5, 
-        min_p=0.1
-    )
-    
-    generated_ids = output_ids[0][inputs.shape[1]:]
-    return tokenizer.decode(generated_ids, skip_special_tokens=True)
-
-def table_generation(prompt):
-    messages = [
-        {"role": "system", "content": prompt},
-        {"role": "user", "content": "create 20 data rows"}
-    ]
-    
-    inputs = tokenizer.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,  # Required for generation
-        return_tensors="pt",
-    ).to("cuda")
-    
-    output_ids = model.generate(
-        input_ids=inputs, 
-        max_new_tokens=4096, 
-        use_cache=True, 
-        temperature=1.5, 
-        min_p=0.1
-    )
-    
-    generated_ids = output_ids[0][inputs.shape[1]:]
-    return tokenizer.decode(generated_ids, skip_special_tokens=True)
-
-def predict(input_data):
-    """
-    Inference endpoint entry point.
-    
-    Expects input_data as a JSON string or dict with a key "query" that contains the natural language query.
-    Returns a JSON string with the generated table.
-    """
-    try:
-        if isinstance(input_data, str):
-            data = json.loads(input_data)
-        else:
-            data = input_data
-        user_query = data.get("query", "")
-    except Exception:
-        return json.dumps({
-            "error": "Invalid input format. Please provide a JSON payload with a 'query' field."
-        })
-    
-    # Transform the user query into the designated prompt format.
-    transformed_prompt = prompt_transformation(user_query)
-    # Generate the table using the transformed prompt.
-    generated_table = table_generation(transformed_prompt)
-    
-    return json.dumps({"result": generated_table})