Create app.py

app.py

@@ -0,0 +1,44 @@
+from transformers import pipeline
+import gradio as gr
+
+# Load the model
+model_name = "google/flan-t5-base"  # Choose your desired model
+generator = pipeline("text2text-generation", model=model_name)
+
+# Function to generate test cases
+def generate_test_cases(method, url, headers, payload):
+    prompt = f"""
+    Generate comprehensive API test cases:
+    Method: {method}
+    URL: {url}
+    Headers: {headers}
+    Payload: {payload}
+
+    Include:
+    - Happy path
+    - Negative test cases
+    - Performance scenarios
+    - Boundary conditions
+    """
+    try:
+        response = generator(prompt, max_length=500)
+        return response[0]['generated_text']
+    except Exception as e:
+        return f"Error generating test cases: {str(e)}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_test_cases,
+    inputs=[
+        gr.Textbox(label="HTTP Method (GET, POST, etc.)"),
+        gr.Textbox(label="API URL"),
+        gr.Textbox(label="Headers (JSON format)"),
+        gr.Textbox(label="Payload (JSON format)"),
+    ],
+    outputs="text",
+    title="API Test Case Generator",
+    description="Generate structured test cases for APIs using Hugging Face models."
+)
+
+if __name__ == "__main__":
+    iface.launch()