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import os
import asyncio
import gradio as gr
from dotenv import load_dotenv
import time
from prompt_enhancer import PromptEnhancer, get_available_models
# Load environment variables
load_dotenv(encoding='utf-8')
# Check if running on Hugging Face Spaces
IS_HF_SPACE = os.environ.get("SPACE_ID") is not None
# Configure API Key
if IS_HF_SPACE:
# Use the Hugging Face Spaces secret
api_key = os.environ.get("OPENROUTER_API_KEY")
else:
# Use local .env file
api_key = os.getenv("OPENROUTER_API_KEY")
if not api_key:
print("Warning: OPENROUTER_API_KEY not found!")
# Cache for available models
available_models = []
async def fetch_models():
"""Fetch available models from OpenRouter"""
global available_models
try:
models = await get_available_models()
available_models = models
return [f"{model['id']} - {model.get('name', 'No name')}" for model in models]
except Exception as e:
print(f"Error fetching models: {e}")
# Fallback models if API call fails
return [
"anthropic/claude-3-haiku - Claude 3 Haiku",
"anthropic/claude-3-sonnet - Claude 3 Sonnet",
"anthropic/claude-3-opus - Claude 3 Opus",
"openai/gpt-4o - GPT-4o",
"openai/gpt-4o-mini - GPT-4o Mini"
]
def get_model_id(model_display_name):
"""Extract model ID from display name"""
if " - " in model_display_name:
return model_display_name.split(" - ")[0]
return model_display_name
async def enhance_prompt(prompt, model_choice):
"""Enhance the prompt using the selected model"""
if not prompt.strip():
return "Please enter a prompt to enhance.", "", ""
start_time = time.time()
model_id = get_model_id(model_choice)
enhancer = PromptEnhancer(model_id)
try:
# Process prompt
expanded_prompt = await enhancer.analyze_and_expand_input(prompt)
suggested_enhancements = await enhancer.suggest_enhancements(prompt)
decomposition_and_reasoning = await enhancer.decompose_and_add_reasoning(expanded_prompt)
# Assemble components
components = {
"expanded_prompt": expanded_prompt,
"decomposition_and_reasoninng": decomposition_and_reasoning,
"suggested_enhancements": suggested_enhancements
}
advanced_prompt = await enhancer.assemble_prompt(components)
elapsed_time = time.time() - start_time
# Generate summary
stats = f"""
Model: {model_id}
Processing Time: {elapsed_time:.2f} seconds
Prompt Tokens: {enhancer.prompt_tokens}
Completion Tokens: {enhancer.completion_tokens}
"""
return advanced_prompt, expanded_prompt, stats
except Exception as e:
return f"Error: {str(e)}", "", ""
# Function to run async operations from Gradio
def run_async(fn):
def wrapper(*args, **kwargs):
return asyncio.run(fn(*args, **kwargs))
return wrapper
# Create the Gradio interface
async def create_ui():
# Get initial model list
model_choices = await fetch_models()
default_model = model_choices[0] if model_choices else "anthropic/claude-3-haiku - Claude 3 Haiku"
with gr.Blocks(title="Advanced Prompt Generator", theme=gr.themes.Soft()) as app:
gr.Markdown("""
# 🚀 Advanced Prompt Generator
Transform your basic prompts into highly optimized, structured prompts for better AI responses.
## How it works:
1. Enter your basic prompt
2. Select an AI model
3. Get an enhanced, structured prompt with decomposition and reasoning
""")
with gr.Row():
with gr.Column(scale=3):
prompt_input = gr.Textbox(
label="Enter Your Basic Prompt",
placeholder="E.g. Explain quantum computing",
lines=4
)
model_dropdown = gr.Dropdown(
choices=model_choices,
label="Select Model",
value=default_model
)
refresh_button = gr.Button("🔄 Refresh Models")
with gr.Row():
submit_button = gr.Button("🔮 Enhance Prompt", variant="primary")
clear_button = gr.Button("🧹 Clear")
with gr.Column(scale=4):
with gr.Tabs():
with gr.TabItem("Enhanced Prompt"):
enhanced_output = gr.Textbox(
label="Enhanced Prompt",
placeholder="Your enhanced prompt will appear here...",
lines=15
)
with gr.TabItem("Expanded Prompt Only"):
expanded_output = gr.Textbox(
label="Expanded Prompt",
placeholder="Your expanded prompt will appear here...",
lines=15
)
with gr.TabItem("Stats"):
stats_output = gr.Textbox(
label="Processing Stats",
lines=5
)
# Define event handlers
refresh_button.click(
fn=run_async(fetch_models),
outputs=model_dropdown
)
submit_button.click(
fn=run_async(enhance_prompt),
inputs=[prompt_input, model_dropdown],
outputs=[enhanced_output, expanded_output, stats_output]
)
clear_button.click(
fn=lambda: ("", "", ""),
outputs=[enhanced_output, expanded_output, stats_output]
)
return app
# Launch the app
if __name__ == "__main__":
app = asyncio.run(create_ui())
app.launch(debug=True) |