Create App.py

App.py

@@ -0,0 +1,111 @@
+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()