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interface.py
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interface.py
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import gradio as gr
import pandas as pd
import json
import os
import random
from core import compile_program, list_prompts, export_to_csv, generate_program_response
# Gradio interface
custom_css = """
.expand-button {
min-width: 20px !important;
width: 20px !important;
padding: 0 !important;
font-size: 10px !important;
}
.prompt-card {
height: 150px !important;
display: flex !important;
flex-direction: column !important;
justify-content: space-between !important;
padding: 10px !important;
position: relative !important;
}
.prompt-details {
flex-grow: 1 !important;
}
.view-details-btn {
position: absolute !important;
bottom: 10px !important;
right: 10px !important;
}
.red-text {
color: red !important;
}
"""
example2_signature = "JokeTopic:Funny-Gpt4oMini_ChainOfThought_Bootstrapfewshotwithrandomsearch-20241003.json - joke, topic -> funny (Score: 100)"
with gr.Blocks(css=custom_css) as demo:
# Compile Program Tab
with gr.Tabs():
with gr.TabItem("Compile Program"):
with gr.Row():
with gr.Column():
gr.Markdown("# DSPyUI: a Gradio user interface for DSPy")
gr.Markdown("Compile a DSPy program by specifying your settings and providing example data.")
with gr.Column():
gr.Markdown("### Demo Examples:")
with gr.Row():
example1 = gr.Button("Judging Jokes")
example2 = gr.Button("Telling Jokes")
example3 = gr.Button("Rewriting Jokes")
# Task Instructions
with gr.Row():
with gr.Column(scale=4):
instructions = gr.Textbox(
label="Task Instructions",
lines=3,
placeholder="Enter detailed task instructions here.",
info="Provide clear and comprehensive instructions for the task. This will guide the DSPy program in understanding the specific requirements and expected outcomes.",
interactive=True # Add this line to ensure the textbox is editable
)
input_values = gr.State([])
output_values = gr.State([])
file_data = gr.State(None)
with gr.Row():
with gr.Column():
gr.Markdown("### Inputs")
gr.Markdown("Add input fields for your task. Each input field represents a piece of information your DSPy program will receive.")
with gr.Row():
add_input_btn = gr.Button("Add Input Field")
remove_input_btn = gr.Button("Remove Last Input", interactive=False)
with gr.Column():
gr.Markdown("### Outputs")
gr.Markdown("Add output fields for your task. Each output field represents a piece of information your DSPy program will generate.")
with gr.Row():
add_output_btn = gr.Button("Add Output Field")
remove_output_btn = gr.Button("Remove Last Output", interactive=False)
def add_field(values):
new_values = values + [("", "")]
return new_values, gr.update(interactive=True)
def remove_last_field(values):
new_values = values[:-1] if values else values
return new_values, gr.update(interactive=bool(new_values))
add_input_btn.click(
add_field,
inputs=input_values,
outputs=[input_values, remove_input_btn]
)
remove_input_btn.click(
remove_last_field,
inputs=input_values,
outputs=[input_values, remove_input_btn]
)
add_output_btn.click(
add_field,
inputs=output_values,
outputs=[output_values, remove_output_btn]
)
remove_output_btn.click(
remove_last_field,
inputs=output_values,
outputs=[output_values, remove_output_btn]
)
def load_csv(filename):
try:
df = pd.read_csv(f"example_data/{filename}")
return df
except Exception as e:
print(f"Error loading CSV: {e}")
return None
row_choice_options = gr.State([])
@gr.render(inputs=[input_values, output_values, file_data])
def render_variables(input_values, output_values, file_data):
inputs = []
outputs = []
with gr.Row():
with gr.Column():
if not input_values:
gr.Markdown("Please add at least one input field.", elem_classes="red-text")
for i, input_value in enumerate(input_values):
name, desc = input_value
with gr.Group():
with gr.Row():
input_name = gr.Textbox(
placeholder=f"Input{i+1}",
value=name if name else None,
key=f"input-name-{i}",
show_label=False,
label=f"Input {i+1} Name",
info="Specify the name of this input field.",
interactive=True,
scale=9
)
expand_btn = gr.Button("▼", size="sm", scale=1, elem_classes="expand-button")
input_desc = gr.Textbox(
value=desc if desc else None,
placeholder=desc if desc else "Description (optional)",
key=f"input-desc-{i}",
show_label=False,
label=f"Input {i+1} Description",
info="Optionally provide a description for this input field.",
interactive=True,
visible=False
)
desc_visible = gr.State(False)
expand_btn.click(
lambda v: (not v, gr.update(visible=not v)),
inputs=[desc_visible],
outputs=[desc_visible, input_desc]
)
inputs.extend([input_name, input_desc, desc_visible])
with gr.Column():
if not output_values:
gr.Markdown("Please add at least one output field.", elem_classes="red-text")
for i, output_value in enumerate(output_values):
name, desc = output_value
with gr.Group():
with gr.Row():
output_name = gr.Textbox(
placeholder=f"Output{i+1}",
value=name if name else None,
key=f"output-name-{i}",
show_label=False,
label=f"Output {i+1} Name",
info="Specify the name of this output field.",
scale=9,
interactive=True,
)
expand_btn = gr.Button("▼", size="sm", scale=1, elem_classes="expand-button")
output_desc = gr.Textbox(
value=desc if desc else None,
placeholder=desc if desc else "Description (optional)",
key=f"output-desc-{i}",
show_label=False,
label=f"Output {i+1} Description",
info="Optionally provide a description for this output field.",
visible=False,
interactive=True,
)
desc_visible = gr.State(False)
expand_btn.click(
lambda v: (not v, gr.update(visible=not v)),
inputs=[desc_visible],
outputs=[desc_visible, output_desc]
)
outputs.extend([output_name, output_desc, desc_visible])
def update_judge_prompt_visibility(metric, *args):
# Correctly assign input and output fields based on the actual arguments
input_fields = []
output_fields = []
filtered_args = [args[i] for i in range(0, len(args), 3)] # Filter out descriptions and visibility
for arg in filtered_args:
if arg and isinstance(arg, str) and arg.strip():
if len(input_fields) < len(input_values):
input_fields.append(arg)
elif len(output_fields) < len(output_values):
output_fields.append(arg)
if metric == "LLM-as-a-Judge":
prompts = list_prompts(output_filter=input_fields + output_fields)
choices = [f"{p['ID']} - {p['Signature']} (Score: {p['Eval Score']})" for p in prompts]
choices.append(example2_signature)
return gr.update(visible=True, choices=choices, value=example2_signature)
else:
return gr.update(visible=False, choices=[])
metric_type.change(
update_judge_prompt_visibility,
inputs=[metric_type] + inputs + outputs,
outputs=[judge_prompt]
)
random_row_button.click(
select_random_row,
inputs=[row_choice_options],
outputs=[row_selector]
)
def compile(data):
input_fields = []
input_descs = []
output_fields = []
output_descs = []
for i in range(0, len(inputs), 3):
if data[inputs[i]].strip():
input_fields.append(data[inputs[i]])
if data[inputs[i+1]].strip():
input_descs.append(data[inputs[i+1]])
for i in range(0, len(outputs), 3):
if data[outputs[i]].strip():
output_fields.append(data[outputs[i]])
if data[outputs[i+1]].strip():
output_descs.append(data[outputs[i+1]])
# Get the judge prompt ID if LLM-as-a-Judge is selected
judge_prompt_id = None
if data[metric_type] == "LLM-as-a-Judge":
judge_prompt_id = data[judge_prompt].split(' - ')[0]
hint = data[hint_textbox] if data[dspy_module] == "ChainOfThoughtWithHint" else None
usage_instructions, optimized_prompt = compile_program(
input_fields,
output_fields,
data[dspy_module],
data[llm_model],
data[teacher_model],
data[example_data],
data[optimizer],
data[instructions],
data[metric_type],
judge_prompt_id,
input_descs,
output_descs,
hint # Add the hint parameter
)
signature = f"{', '.join(input_fields)} -> {', '.join(output_fields)}"
# Extract evaluation score from usage_instructions
score_line = [line for line in usage_instructions.split('\n') if line.startswith("Evaluation score:")][0]
evaluation_score = float(score_line.split(":")[1].strip())
# Remove the evaluation score line from usage_instructions
usage_instructions = '\n'.join([line for line in usage_instructions.split('\n') if not line.startswith("Evaluation score:")])
# Extract baseline score from usage_instructions
baseline_score_line = [line for line in usage_instructions.split('\n') if line.startswith("Baseline score:")][0]
baseline_score = float(baseline_score_line.split(":")[1].strip())
# Remove the baseline score line from usage_instructions
usage_instructions = '\n'.join([line for line in usage_instructions.split('\n') if not line.startswith("Baseline score:")])
# Extract human-readable ID from usage_instructions
human_readable_id = None
for line in usage_instructions.split('\n'):
if "programs/" in line and ".json" in line:
human_readable_id = line.split('programs/')[1].split('.json')[0]
break
if human_readable_id is None:
raise ValueError("Could not extract human-readable ID from usage instructions")
# Save details to JSON
details = {
"input_fields": input_fields,
"input_descriptions": input_descs,
"output_fields": output_fields,
"output_descriptions": output_descs,
"dspy_module": data[dspy_module],
"llm_model": data[llm_model],
"teacher_model": data[teacher_model],
"optimizer": data[optimizer],
"instructions": data[instructions],
"signature": signature,
"evaluation_score": evaluation_score,
"baseline_score": baseline_score,
"optimized_prompt": optimized_prompt,
"usage_instructions": usage_instructions,
"human_readable_id": human_readable_id
}
row_choice_options = [f"Row {i+1}" for i in range(len(data[example_data]))]
# Create 'prompts' folder if it doesn't exist
if not os.path.exists('prompts'):
os.makedirs('prompts')
# Save JSON file with human-readable ID
json_filename = f"prompts/{human_readable_id}.json"
with open(json_filename, 'w') as f:
json.dump(details, f, indent=4)
return signature, evaluation_score, optimized_prompt, gr.update(choices=row_choice_options, visible=True, value="Row 1"), gr.update(visible=True), row_choice_options, gr.update(visible=True), gr.update(visible=True), human_readable_id, gr.update(visible=True), baseline_score
gr.Markdown("### Data")
gr.Markdown("Provide example data for your task. This will help the DSPy compiler understand the format of your data. You can either enter the data manually or upload a CSV file with the correct column headers.")
with gr.Column():
with gr.Row():
enter_manually_btn = gr.Button("Enter manually", interactive=len(input_values) > 0 and len(output_values) > 0 and file_data is None)
upload_csv_btn = gr.UploadButton("Upload CSV", file_types=[".csv"], interactive=len(input_values) > 0 and len(output_values) > 0 and file_data is None)
headers = [input_value[0] for input_value in input_values] + [output_value[0] for output_value in output_values]
example_data = gr.Dataframe(
headers=headers,
datatype=["str"] * (len(input_values) + len(output_values)),
interactive=True,
row_count=1,
col_count=(len(input_values) + len(output_values), "fixed"),
visible=file_data is not None, # Only visible if file_data is not None
label="Example Data",
value=file_data if file_data is not None else pd.DataFrame(columns=headers)
)
export_csv_btn = gr.Button("Export to CSV", interactive=file_data is not None and len(input_values) > 0 and len(output_values) > 0)
csv_download = gr.File(label="Download CSV", visible=False)
error_message = gr.Markdown()
def show_dataframe(*args):
# Correctly assign input and output fields based on the actual arguments
input_fields = []
output_fields = []
filtered_args = [args[i] for i in range(0, len(args), 3)] # Filter out descriptions and visibility
input_names = [name for name, _ in input_values]
for arg in filtered_args:
if arg and isinstance(arg, str) and arg.strip():
if len(input_fields) < len(input_names):
input_fields.append(arg)
elif len(output_fields) < len(output_values):
output_fields.append(arg)
headers = input_fields + output_fields
# Create a new dataframe with the correct headers
new_df = pd.DataFrame(columns=headers)
return gr.update(visible=True, value=new_df), gr.update(visible=True), gr.update(visible=True), gr.update(interactive=False), gr.update(interactive=False)
enter_manually_btn.click(
show_dataframe,
inputs=inputs + outputs,
outputs=[example_data, export_csv_btn, compile_button, enter_manually_btn, upload_csv_btn]
)
upload_csv_btn.upload(
process_csv,
inputs=[upload_csv_btn] + inputs + outputs,
outputs=[example_data, example_data, compile_button, error_message, enter_manually_btn, upload_csv_btn]
)
export_csv_btn.click(
export_to_csv,
inputs=[example_data],
outputs=[csv_download]
).then(
lambda: gr.update(visible=True),
outputs=[csv_download]
)
compile_button.click(
compile,
inputs=set(inputs + outputs + [llm_model, teacher_model, dspy_module, example_data, upload_csv_btn, optimizer, instructions, metric_type, judge_prompt, hint_textbox]),
outputs=[signature, evaluation_score, optimized_prompt, row_selector, random_row_button, row_choice_options, generate_button, generate_output, human_readable_id, human_readable_id, baseline_score]
)
def generate_response(human_readable_id, row_selector, df):
selected_row = df.iloc[int(row_selector.split()[1]) - 1].to_dict()
print("selected_row:", selected_row)
return generate_program_response(human_readable_id, selected_row)
generate_button.click(
generate_response,
inputs=[human_readable_id, row_selector, example_data],
outputs=[generate_output]
)
gr.Markdown("### Settings")
with gr.Row():
model_options = [
"gpt-3.5-turbo", "gpt-4", "gpt-4o", "gpt-4o-mini",
"claude-3-5-sonnet-20240620", "claude-3-opus-20240229",
"claude-3-sonnet-20240229", "claude-3-haiku-20240307",
"mixtral-8x7b-32768", "gemma-7b-it", "llama3-70b-8192",
"llama3-8b-8192", "gemma2-9b-it", "gemini-1.5-flash-8b", "gemini-1.5-flash", "gemini-1.5-pro"
]
llm_model = gr.Dropdown(
model_options,
label="Model",
value="gpt-4o-mini",
info="Select the main language model for your DSPy program. This model will be used for inference. Typically you want to choose a fast and cheap model here, and train it on your task to improve quality.",
interactive=True # Add this line
)
teacher_model = gr.Dropdown(
model_options,
label="Teacher",
value="gpt-4o",
info="Select a more capable (but slower and more expensive) model to act as a teacher during the compilation process. This model helps generate high-quality examples and refine prompts.",
interactive=True # Add this line
)
with gr.Column():
dspy_module = gr.Dropdown(
["Predict", "ChainOfThought", "ChainOfThoughtWithHint"],
label="Module",
value="Predict",
info="Choose the DSPy module that best fits your task. Predict is for simple tasks, ChainOfThought for complex reasoning, and ChainOfThoughtWithHint for guided reasoning.",
interactive=True # This line was likely already present
)
hint_textbox = gr.Textbox(
label="Hint",
lines=2,
placeholder="Enter a hint for the Chain of Thought with Hint module.",
visible=False,
interactive=True # Add this line
)
with gr.Row():
optimizer = gr.Dropdown(
["BootstrapFewShot", "BootstrapFewShotWithRandomSearch", "MIPRO", "MIPROv2", "COPRO"],
label="Optimizer",
value="BootstrapFewShot",
info="Choose optimization strategy: None (no optimization), BootstrapFewShot (small datasets, ~10 examples) uses few-shot learning; BootstrapFewShotWithRandomSearch (medium, ~50) adds randomized search; MIPRO, MIPROv2, and COPRO (large, 300+) also optimize the prompt instructions.",
interactive=True # Add this line
)
with gr.Column():
metric_type = gr.Radio(
["Exact Match", "Cosine Similarity", "LLM-as-a-Judge"],
label="Metric",
value="Exact Match",
info="Choose how to evaluate your program's performance. Exact Match is suitable for tasks with clear correct answers, while LLM-as-a-Judge is better for open-ended or subjective tasks. Cosine Similarity can be used for fuzzier matches tasks where the output needs to be similar to the correct answer.",
interactive=True # Add this line
)
judge_prompt = gr.Dropdown(
choices=[],
label="Judge Prompt",
visible=False,
info="Select the prompt to use as the judge for evaluation.",
interactive=True # Add this line
)
compile_button = gr.Button("Compile Program", visible=False, variant="primary")
with gr.Column() as compilation_results:
gr.Markdown("### Results")
with gr.Row():
signature = gr.Textbox(label="Signature", interactive=False, info="The compiled signature of your DSPy program, showing inputs and outputs.")
evaluation_score = gr.Number(label="Evaluation Score", info="The evaluation score of your compiled DSPy program.", interactive=False)
baseline_score = gr.Number(label="Baseline Score", info="The baseline score of your unoptimized DSPy module.", interactive=False)
optimized_prompt = gr.Textbox(label="Optimized Prompt", info="The optimized prompt generated by the DSPy compiler for your program.", interactive=False)
with gr.Row():
# Add a dropdown to select a row from the dataset
with gr.Column(scale=1):
human_readable_id = gr.Textbox(interactive=False, visible=False)
row_selector = gr.Dropdown(
choices=[],
label="Select a row from the dataset",
interactive=True,
visible=False,
info="Choose a specific row from the loaded dataset to use as input for your compiled program."
)
random_row_button = gr.Button("Select Random Row", visible=False, interactive=True)
generate_button = gr.Button("Generate", interactive=True, visible=False, variant="primary")
with gr.Column(scale=2):
generate_output = gr.Textbox(label="Generated Response", info="The input and output generated by your compiled DSPy program.", interactive=False, lines=10, visible=False)
def select_random_row(row_choice_options):
if row_choice_options:
random_choice = random.choice(row_choice_options)
return gr.update(value=random_choice, visible=True)
return gr.update(visible=True)
def process_csv(file, *args):
if file is not None:
try:
df = pd.read_csv(file.name)
# Correctly assign input and output fields based on the actual arguments
input_fields = []
output_fields = []
filtered_args = [args[i] for i in range(0, len(args), 3)] # Filter out descriptions and visibility
for arg in filtered_args:
if arg and isinstance(arg, str) and arg.strip():
if len(input_fields) < len(input_values):
input_fields.append(arg)
elif len(output_fields) < len(output_values):
output_fields.append(arg)
expected_headers = input_fields + output_fields
if list(df.columns) != expected_headers:
return None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=True, value=f"Error: CSV headers do not match expected format. Expected: {expected_headers}, Got: {list(df.columns)}")
return df, gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
except Exception as e:
return None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=True, value=f"Error: {str(e)}")
return None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(interactive=False), gr.update(interactive=False)
# Function to show/hide the hint textbox based on the selected module
def update_hint_visibility(module):
return gr.update(visible=module == "ChainOfThoughtWithHint")
# Connect the visibility update function to the module dropdown
dspy_module.change(update_hint_visibility, inputs=[dspy_module], outputs=[hint_textbox])
def disable_example_buttons():
return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
def update_example2():
return (
gr.update(value="Tell me a funny joke"),
gr.update(value="MIPROv2"),
gr.update(value="LLM-as-a-Judge"),
gr.update(value="gpt-4o-mini"),
gr.update(value="gpt-4o"),
gr.update(value="Predict"),
[("topic", "The topic of the joke")],
[("joke", "The funny joke")],
*disable_example_buttons(),
load_csv("telling_jokes.csv"),
gr.update(visible=True),
gr.update(visible=True),
gr.update(value=example2_signature, visible=True) # Update judge_prompt
)
example2.click(
update_example2,
inputs=[],
outputs=[instructions, optimizer, metric_type, llm_model, teacher_model, dspy_module, input_values, output_values, example1, example2, example3, file_data, compile_button, judge_prompt]
)
example1.click(
lambda _: (
gr.update(value="Rate whether a joke is funny"),
gr.update(value="BootstrapFewShotWithRandomSearch"),
gr.update(value="Exact Match"),
gr.update(value="gpt-4o-mini"),
gr.update(value="gpt-4o"),
gr.update(value="ChainOfThought"),
[("joke", "The joke to be rated"), ("topic", "The topic of the joke")],
[("funny", "Whether the joke is funny or not, 1 or 0.")],
*disable_example_buttons(),
load_csv("rating_jokes.csv"),
gr.update(visible=True)
),
inputs=[gr.State(None)],
outputs=[instructions, optimizer, metric_type, llm_model, teacher_model, dspy_module, input_values, output_values, example1, example2, example3, file_data, compile_button]
)
example3.click(
lambda _: (
gr.update(value="Rewrite in a comedian's style"),
gr.update(value="BootstrapFewShot"),
gr.update(value="Cosine Similarity"),
gr.update(value="claude-3-haiku-20240307"),
gr.update(value="claude-3-sonnet-20240229"),
gr.update(value="Predict"),
[("joke", "The joke to be rewritten"), ("comedian", "The comedian the joke should be rewritten in the style of")],
[("rewritten_joke", "The rewritten joke")],
*disable_example_buttons(),
load_csv("rewriting_jokes.csv"),
gr.update(visible=True)
),
inputs=[gr.State(None)],
outputs=[instructions, optimizer, metric_type, llm_model, teacher_model, dspy_module, input_values, output_values, example1, example2, example3, file_data, compile_button]
)
with gr.TabItem("View Prompts"):
prompts = list_prompts()
selected_prompt = gr.State(None)
# Extract unique signatures for the dropdown
unique_signatures = sorted(set(p["Signature"] for p in prompts))
close_details_btn = gr.Button("Close Details", elem_classes="close-details-btn", size="sm", visible=False)
close_details_btn.click(lambda: (None, gr.update(visible=False)), outputs=[selected_prompt, close_details_btn])
@gr.render(inputs=[selected_prompt])
def render_prompt_details(selected_prompt):
if selected_prompt is not None:
with gr.Row():
with gr.Column():
details = json.loads(selected_prompt["Details"])
gr.Markdown(f"## {details['human_readable_id']}")
with gr.Group():
with gr.Column(elem_classes="prompt-details-full"):
gr.Number(value=float(selected_prompt['Eval Score']), label="Evaluation Score", interactive=False)
with gr.Row():
gr.Dropdown(choices=details['input_fields'], value=details['input_fields'], label="Input Fields", interactive=False, multiselect=True, info=", ".join(details.get('input_descs', [])))
gr.Dropdown(choices=details['output_fields'], value=details['output_fields'], label="Output Fields", interactive=False, multiselect=True, info=", ".join(details.get('output_descs', [])))
with gr.Row():
gr.Dropdown(choices=[details['dspy_module']], value=details['dspy_module'], label="Module", interactive=False)
gr.Dropdown(choices=[details['llm_model']], value=details['llm_model'], label="Model", interactive=False)
gr.Dropdown(choices=[details['teacher_model']], value=details['teacher_model'], label="Teacher Model", interactive=False)
gr.Dropdown(choices=[details['optimizer']], value=details['optimizer'], label="Optimizer", interactive=False)
gr.Textbox(value=details['instructions'], label="Instructions", interactive=False)
gr.Textbox(value=details['optimized_prompt'], label="Optimized Prompt", interactive=False)
for key, value in details.items():
if key not in ['signature', 'evaluation_score', 'input_fields', 'output_fields', 'dspy_module', 'llm_model', 'teacher_model', 'optimizer', 'instructions', 'optimized_prompt', 'human_readable_id']:
if isinstance(value, list):
gr.Dropdown(choices=value, value=value, label=key.replace('_', ' ').title(), interactive=False, multiselect=True)
elif isinstance(value, bool):
gr.Checkbox(value=value, label=key.replace('_', ' ').title(), interactive=False)
elif isinstance(value, (int, float)):
gr.Number(value=value, label=key.replace('_', ' ').title(), interactive=False)
else:
gr.Textbox(value=str(value), label=key.replace('_', ' ').title(), interactive=False)
gr.Markdown("# View Prompts")
# Add filter and sort functionality in one line
with gr.Row():
filter_signature = gr.Dropdown(label="Filter by Signature", choices=["All"] + unique_signatures, value="All", scale=2)
sort_by = gr.Radio(["Run Date", "Evaluation Score"], label="Sort by", value="Run Date", scale=1)
sort_order = gr.Radio(["Descending", "Ascending"], label="Sort Order", value="Descending", scale=1)
@gr.render(inputs=[filter_signature, sort_by, sort_order])
def render_prompts(filter_signature, sort_by, sort_order):
if filter_signature and filter_signature != "All":
filtered_prompts = list_prompts(signature_filter=filter_signature)
else:
filtered_prompts = prompts
if sort_by == "Evaluation Score":
key_func = lambda x: float(x["Eval Score"])
else: # Run Date
key_func = lambda x: x["ID"] # Use the entire ID for sorting
sorted_prompts = sorted(filtered_prompts, key=key_func, reverse=(sort_order == "Descending"))
prompt_components = []
for i in range(0, len(sorted_prompts), 3):
with gr.Row():
for j in range(3):
if i + j < len(sorted_prompts):
prompt = sorted_prompts[i + j]
with gr.Column():
with gr.Group(elem_classes="prompt-card"):
with gr.Column(elem_classes="prompt-details"):
gr.Markdown(f"**ID:** {prompt['ID']}")
gr.Markdown(f"**Signature:** {prompt['Signature']}")
gr.Markdown(f"**Eval Score:** {prompt['Eval Score']}")
view_details_btn = gr.Button("View Details", elem_classes="view-details-btn", size="sm")
prompt_components.append((prompt, view_details_btn))
for prompt, btn in prompt_components:
btn.click(
lambda p=prompt: (p, gr.update(visible=True)),
outputs=[selected_prompt, close_details_btn]
)
if __name__ == "__main__":
demo.launch()