app.py

import logging
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr

# Set up logging
logging.basicConfig(
    filename="app.log",
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)

# Select the model you want to use (LLaMA, GPT, or CodeT5)
MODEL_NAME = "Qwen/Qwen2.5-Coder-3B-Instruct"

# Load the model and tokenizer
logging.info(f"Loading the model: {MODEL_NAME}...")
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
    logging.info("Model loaded successfully.")
except Exception as e:
    logging.error(f"Error loading the model: {e}")
    raise

# Define a function to generate test cases
def generate_test_cases(api_info):
    logging.info(f"Generating test cases for API info: {api_info}")
    try:
        prompt = (
            f"Generate API test cases for the following API:\n\n{api_info}\n\n"
            f"Test cases should include:\n- Happy path\n- Negative tests\n- Edge cases"
        )
        # Tokenize the input prompt
        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
        
        # Generate output from the model
        outputs = model.generate(
            inputs["input_ids"],
            attention_mask=inputs["attention_mask"],  # Provide attention mask explicitly
            max_length=512,
            num_return_sequences=1,
            do_sample=True
        )
        # Decode the generated text
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        logging.info("Test cases generated successfully.")
        return generated_text
    except Exception as e:
        logging.error(f"Error generating test cases: {e}")
        return "An error occurred while generating test cases."


# Process input and generate output
def process_input(url, method, headers, payload):
    try:
        logging.info("Received user input.")
        api_info = f"URL: {url}\nMethod: {method}\nHeaders: {headers}\nPayload: {payload}"
        logging.debug(f"Formatted API info: {api_info}")
        test_cases = generate_test_cases(api_info)
        return test_cases
    except Exception as e:
        logging.error(f"Error processing input: {e}")
        return "An error occurred. Please check the input format and try again."

# Define Gradio interface
interface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(label="API URL"),
        gr.Textbox(label="HTTP Method"),
        gr.Textbox(label="Headers (JSON format)"),
        gr.Textbox(label="Payload (JSON format)"),
    ],
    outputs="text",
    title=f"API Test Case Generator ({MODEL_NAME})"
)

# Launch Gradio app
if __name__ == "__main__":
    try:
        logging.info("Starting the Gradio app...")
        interface.launch()
        logging.info("Gradio app launched successfully.")
    except Exception as e:
        logging.error(f"Error launching the Gradio app: {e}")