| import json |
| from functools import partial |
| import gradio as gr |
| from transformers import pipeline |
|
|
| with open("modules.json", "r", encoding="utf-8") as f: |
| MODULES = json.load(f)["modules"] |
|
|
| GENERATORS = [m for m in MODULES if m["type"] == "generator"] |
| CHECKERS = {m["id"]: m for m in MODULES if m["type"] == "checker"} |
| GEN_BY_ID = {m["id"]: m for m in GENERATORS} |
|
|
| llm = pipeline("text-generation", model="gpt2", max_new_tokens=512) |
|
|
| def call_llm(prompt): |
| out = llm(prompt, max_new_tokens=512, do_sample=False)[0]["generated_text"] |
| return out[len(prompt):].strip() if out.startswith(prompt) else out |
|
|
| def generator_prompt(module_id, *inputs): |
| m = GEN_BY_ID[module_id] |
| keys = list(m["input_placeholders"].keys()) |
| inputs = {k: inputs[i] if i < len(inputs) else "" for i, k in enumerate(keys)} |
| sections = m["output_sections"] |
|
|
| p = [] |
| p.append("You are a structured reasoning module.") |
| p.append(f"MODULE: {m['label']}") |
| p.append("INPUTS:") |
| for k, v in inputs.items(): |
| p.append(f"{k.upper()}: {v}") |
| p.append("") |
| p.append("Produce the following sections:") |
| for s in sections: |
| p.append(f"- {s}") |
| p.append("") |
| for s in sections: |
| p.append(f"{s}:") |
| p.append("[content]") |
| p.append("") |
| return "\n".join(p) |
|
|
| def checker_prompt(checker_id, *vals): |
| c = CHECKERS[checker_id] |
| secs = c["output_sections"] |
| if len(vals) == 0: |
| orig, draft = "", "" |
| elif len(vals) == 1: |
| orig, draft = "", vals[0] |
| else: |
| *orig_parts, draft = vals |
| orig = "\n\n".join(orig_parts) |
|
|
| p = [] |
| p.append("You are a strict evaluator.") |
| p.append(f"CHECKER: {c['label']}") |
| p.append("ORIGINAL TASK:") |
| p.append(orig) |
| p.append("") |
| p.append("DRAFT OUTPUT:") |
| p.append(draft) |
| p.append("") |
| p.append("Respond with sections:") |
| for s in secs: |
| p.append(f"- {s}") |
| p.append("") |
| for s in secs: |
| p.append(f"{s}:") |
| p.append("[content]") |
| p.append("") |
| return "\n".join(p) |
|
|
| def run_generator(mid, *inputs): |
| return call_llm(generator_prompt(mid, *inputs)) |
|
|
| def run_checker(cid, *inputs): |
| return call_llm(checker_prompt(cid, *inputs)) |
|
|
| def build_ui(): |
| with gr.Blocks(title="Modular Intelligence") as demo: |
| gr.Markdown("# Modular Intelligence\nSelect a module and generate output.") |
|
|
| for m in GENERATORS: |
| with gr.Tab(m["label"]): |
| gr.Markdown(m["description"]) |
| input_boxes = [] |
| for key, ph in m["input_placeholders"].items(): |
| tb = gr.Textbox(label=key, placeholder=ph, lines=4) |
| input_boxes.append(tb) |
|
|
| output_box = gr.Textbox(label="Module output", lines=18) |
| gr.Button("Run module").click( |
| fn=partial(run_generator, m["id"]), |
| inputs=input_boxes, |
| outputs=output_box |
| ) |
|
|
| if m.get("has_checker"): |
| cid = m.get("checker_id") |
| if cid in CHECKERS: |
| gr.Markdown("### Checker") |
| checker_out = gr.Textbox(label="Checker output", lines=14) |
| gr.Button("Run checker").click( |
| fn=partial(run_checker, cid), |
| inputs=input_boxes + [output_box], |
| outputs=checker_out |
| ) |
| return demo |
|
|
| if __name__ == "__main__": |
| app = build_ui() |
| app.launch() |
