app.py

import gradio as gr
import json
from run_llm import template_all, prompt2_pos, prompt2_chunk, prompt2_parse, demon_pos, demon_chunk, demon_parse, model_mapping

# Your existing code

# Function to process text based on model and task
def process_text(model_name, task, text):
    # Define prompts for each strategy based on the task
    strategy_prompts = {
        'Strategy 1': template_all.format(text),
        'Strategy 2': {
            'POS': prompt2_pos.format(text),
            'Chunking': prompt2_chunk.format(text),
            'Parsing': prompt2_parse.format(text),
        }.get(task, "Invalid Task Selection for Strategy 2"),
        'Strategy 3': {
            'POS': demon_pos,
            'Chunking': demon_chunk,
            'Parsing': demon_parse,
        }.get(task, "Invalid Task Selection for Strategy 3"),
    }

    # Get the selected prompt based on the strategy
    prompt = strategy_prompts.get(model_name, "Invalid Model Selection")

    # Add your logic to feed the prompt to the selected model and get the result
    result = "Processed Result"  # Replace this with your actual result
    return result

# Dropdown options for model and task
model_options = list(model_mapping.keys())
task_options = ['POS', 'Chunking', 'Parsing']

# Gradio interface
iface = gr.Interface(
    fn=process_text,
    inputs=[
        gr.Dropdown(model_options, label="Select Model"),
        gr.Dropdown(task_options, label="Select Task"),
        gr.Textbox(label="Input Text", placeholder="Enter the text to process..."),
    ],
    outputs=[
        gr.Textbox(label="Strategy 1 QA Result", output_transform=lambda x: json.dumps(x, indent=2)),
        gr.Textbox(label="Strategy 2 Instruction Result", output_transform=lambda x: json.dumps(x, indent=2)),
        gr.Textbox(label="Strategy 3 Structured Prompting Result", output_transform=lambda x: json.dumps(x, indent=2)),
    ],
    live=False,
)

iface.launch()