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app.py

@@ -1,7 +1,8 @@
 import json
 import os
 from pathlib import Path
-
+import uuid
+from datetime import datetime
 import gradio as gr
 import pandas as pd
 from huggingface_hub import snapshot_download
@@ -186,6 +187,76 @@ def get_contract():
         "message": "backend_contract.json not loaded. Check HF_TOKEN secret or BUNDLE_REPO variable.",
     }
 
+def generate_job_demo(num_molecules, return_top_k, docking_mode):
+    """
+    CPU demo generation endpoint.
+
+    This does not run live REINVENT generation.
+    It returns a backend-style generation job response using the packaged example rows,
+    so frontend/filter/backend teams can test JSON + CSV + job history behavior.
+    """
+    job_id = f"gen_{uuid.uuid4().hex[:8]}"
+    created_at = datetime.utcnow().isoformat() + "Z"
+
+    if EXAMPLE_JSON and "results" in EXAMPLE_JSON:
+        rows = EXAMPLE_JSON["results"]
+    else:
+        rows = [
+            compute_row("CCO"),
+            compute_row("c1ccccc1"),
+        ]
+
+    num_molecules = int(num_molecules)
+    return_top_k = int(return_top_k)
+
+    selected = rows[: max(1, min(return_top_k, len(rows)))]
+
+    clean_rows = []
+    for row in selected:
+        clean = dict(row)
+
+        clean["docking_score"] = None
+        clean["docking_status"] = "not_run"
+        clean["docking_pose_file"] = None
+
+        if "pred_pAff_mean" not in clean:
+            clean["pred_pAff_mean"] = None
+
+        clean_rows.append(clean)
+
+    df = pd.DataFrame(clean_rows)
+
+    output_dir = Path("outputs")
+    output_dir.mkdir(exist_ok=True)
+
+    csv_path = output_dir / f"{job_id}_results.csv"
+    df.to_csv(csv_path, index=False)
+
+    response = {
+        "job_id": job_id,
+        "status": "completed",
+        "mode": "generate_job_cpu_demo",
+        "created_at": created_at,
+        "preset": "egfr_generator",
+        "docking_mode": docking_mode,
+        "summary": {
+            "num_requested": num_molecules,
+            "num_generated": len(rows),
+            "num_returned": len(clean_rows),
+        },
+        "columns": list(df.columns),
+        "results": clean_rows,
+        "download_csv_file": str(csv_path),
+        "warnings": [
+            "This is a CPU demo generation response.",
+            "Live REINVENT generation is not executed in this Space version.",
+            "The response shape matches the planned backend contract.",
+            "Real docking is not run in this CPU Space; docking_status is not_run.",
+        ],
+    }
+
+    return df, response, str(csv_path)
+
 
 loaded_ok, loaded_msg = download_bundle_light()
 
@@ -234,7 +305,54 @@ Important:
             inputs=smiles_input,
             outputs=[score_table, score_json],
         )
+    with gr.Tab("Generate Job Demo"):
+        gr.Markdown(
+            """
+This tab demonstrates the expected backend behavior for generation jobs.
 
+It returns:
+
+- job_id
+- status
+- summary
+- results JSON
+- downloadable CSV file
+
+Important:
+This CPU Space does not run live REINVENT generation. It uses packaged example rows to let frontend, filters, and backend test the full response format.
+"""
+        )
+
+        gen_num_molecules = gr.Number(
+            label="num_molecules",
+            value=100,
+            precision=0,
+        )
+
+        gen_return_top_k = gr.Number(
+            label="return_top_k",
+            value=20,
+            precision=0,
+        )
+
+        gen_docking_mode = gr.Dropdown(
+            label="docking_mode",
+            choices=["off", "top_k", "all"],
+            value="off",
+        )
+
+        gen_btn = gr.Button("Generate Job Demo")
+
+        gen_table = gr.Dataframe(label="Generated results table")
+        gen_json = gr.JSON(label="Backend-style generation JSON")
+        gen_file = gr.File(label="Download CSV")
+
+        gen_btn.click(
+            generate_job_demo,
+            inputs=[gen_num_molecules, gen_return_top_k, gen_docking_mode],
+            outputs=[gen_table, gen_json, gen_file],
+        )
+    
     with gr.Tab("Example Generate Response"):
         gr.Markdown(
             """