replace with create enrollment rule

app.py

@@ -1,154 +1,132 @@
 import gradio as gr
-import numpy as np
+import pydantic
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+from pydantic import Field, field_validator, BaseModel
+from enum import Enum
+from typing import List
+
+class Group(str, Enum):
+    onboarding = "onboarding"
+    sales_enablement = "sales enablement"
+    marketing = "marketing"
+    leadership = "leadership"
+
+GROUPS = ["onboarding", "sales enablement", "marketing", "leadership"]
+
+class Course(str, Enum):
+    company_culture_and_values = "company culture and values"
+    product_knowledge_and_features = "product knowledge and features"
+    sales_process_and_methodology = "sales process and methodology"
+    customer_relationship_management_system_training = "customer relationship management system training"
+    sales_enablement_strategy_and_planning = "sales enablement strategy and planning"
+    content_creation_and_curation = "content creation and curation"
+    sales_coaching_and_mentoring = "sales coaching and mentoring"
+    sales_metrics_and_analytics = "sales metrics and analytics"
+    digital_marketing_fundamentals = "digital marketing fundamentals"
+    content_marketing_strategy = "content marketing strategy"
+    social_media_marketing = "social media marketing"
+    search_engine_optimization = "search engine optimization"
+    leadership_development_and_coaching = "leadership development and coaching"
+    strategic_planning_and_decision_making = "strategic planning and decision making"
+    change_management_and_organizational_development = "change management and organizational development"
+    emotional_intelligence_and_communication_skills = "emotional intelligence and communication skills"
+
+COURSES = ["company culture and values",
+           "product knowledge and features",
+           "sales process and methodology",
+           "customer relationship management system training",
+           "sales enablement strategy and planning",
+           "content creation and curation",
+           "sales coaching and mentoring",
+           "sales metrics and analytics",
+           "digital marketing fundamentals",
+           "content marketing strategy",
+           "social media marketing",
+           "search engine optimization",
+           "leadership development and coaching",
+           "strategic planning and decision making",
+           "change management and organizational development",
+           "emotional intelligence and communication skills"
+           ]
+
+class EnrollmentRule(BaseModel):
+    rule_code: str = Field(description="unique identifier code for the rule", default="")
+    rule_name: str = Field(description="name of the rule", default="")
+    group: Group = Field(description="group to apply the rule to", default=Group.onboarding)
+    courses: List[Course] = Field(description="list of courses that the members of the group will follow", default=[course.value for course in Course])
+
+    @field_validator("rule_code")
+    def rule_code_max_length(cls, rule_code):
+        # Check if empty
+        if not rule_code.strip():
+            raise ValueError("The rule code cannot be empty.")
+        # Check length
+        if len(rule_code) > 50:
+            raise ValueError("The rule code must contain less than 50 characters.")
+        return rule_code
+    @field_validator("rule_name")
+    def rule_name_max_length(cls, rule_name):
+        # Check if empty
+        if not rule_name.strip():
+            raise ValueError("The rule name cannot be empty.")
+        # Check length
+        if len(rule_name) > 255:
+            raise ValueError("The rule name must contain less than 255 characters.")
+        return rule_name
+    @field_validator("group")
+    def group_is_valid(cls, group):
+        # Check if empty
+        if not group or not group.strip():
+            raise ValueError("Group cannot be empty.")
+        # Validate group value
+        if group.lower().strip() not in GROUPS:
+            raise ValueError(f"group '{group}' is not a valid value for field 'group'. 'group' must be one of the following: '{', '.join(GROUPS)}'")
+        return group
+    @field_validator("courses")
+    def course_list_is_valid(cls, courses):
+        # Ensure at least one course is selected
+        if not courses:
+            raise ValueError("At least one course must be provided.")
+
+        # Validate all listed courses
+        invalid_courses = []
+        for course in courses:
+            if course.lower().strip() not in COURSES:
+                invalid_courses.append(course)
+        if invalid_courses:
+            raise ValueError(f"course(s) '{', '.join(invalid_courses)}' is not a valid value for field 'courses'. 'course' must be one or more of the following: '{', '.join(COURSES)}'")
+        return courses
+    
+def create_enrollment_rule(enrollment_rule: EnrollmentRule) -> dict:
+    """Create an enrollment rule, based on the rule parameters provided by the user"""
+    payload = enrollment_rule.model_dump(mode="json")
+    return payload
+
+# A small Gradio-friendly wrapper that converts user inputs into an EnrollmentRule
+def gr_create_enrollment_rule(rule_code, rule_name, group, courses):
+    # Build the Pydantic model from the raw inputs
+    enrollment_rule = EnrollmentRule(
+        rule_code=rule_code,
+        rule_name=rule_name,
+        group=group,
+        courses=courses
     )
-
-if __name__ == "__main__":
-    demo.launch()
+    return create_enrollment_rule(enrollment_rule)
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=gr_create_enrollment_rule,
+    inputs=[
+        gr.Textbox(label="Rule Code", placeholder="Enter a unique identifier (max 50 characters)"),
+        gr.Textbox(label="Rule Name", placeholder="Enter a descriptive name (max 255 characters)"),
+        gr.Dropdown(label="Group", choices=GROUPS, value=GROUPS[0]),
+        gr.CheckboxGroup(label="Courses", choices=COURSES, value=[], info="Select one or more courses")
+    ],
+    outputs="json",
+    title="Enrollment Rule Creator",
+    description="Create an enrollment rule, based on the rule parameters provided by the user",
+    allow_flagging="never"
+)
+
+demo.launch()

requirements.txt

@@ -1,6 +1 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+gradio