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app.py

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+# app.py  —  Hugging Face Space entrypoint (fixed imports)
+
+import os
+import io
+import csv
+import json
+import zipfile
+import datetime
+import gradio as gr
+
+# --- robust imports: absolute first, safe fallback for local runs ---
+try:
+    from email_eval.api import evaluate, DEFAULT_WEIGHT_PRESETS, metric_keys
+except Exception:
+    # allow running `python app.py` locally without installing a package
+    import sys, pathlib
+    sys.path.append(str(pathlib.Path(__file__).parent.resolve()))
+    from email_eval.api import evaluate, DEFAULT_WEIGHT_PRESETS, metric_keys
+
+ENGINES = ["openai", "claude"]
+PRESET = "research_defaults"
+
+def run_eval(subject, body, engine,
+             w_clarity, w_length, w_spam, w_perso, w_tone, w_grammar):
+    # normalize/collect weights
+    weights = {
+        "clarity": float(w_clarity),
+        "length": float(w_length),
+        "spam_score": float(w_spam),
+        "personalization": float(w_perso),
+        "tone": float(w_tone),
+        "grammatical_hygiene": float(w_grammar),
+    }
+
+    # evaluate
+    out = evaluate(subject or "", body or "", engine=engine, weights=weights)
+
+    # JSON for exports
+    out_json_str = json.dumps(out, indent=2, ensure_ascii=False)
+
+    # comments pane - use LLM comments + explanations
+    comments_lines = []
+    for k in metric_keys():
+        comm = out.get("comments", {}).get(k, 'No comment.')
+        expl = "; ".join(out.get("explanations", {}).get(k, []))
+        comments_lines.append(f"{k}: {comm}" + (f" (explanations: {expl})" if expl else ""))
+    comments_str = "\n".join(comments_lines) if comments_lines else "No comments."
+
+    # tokens used pane
+    usage = out.get("usage", {}) or {}
+    tokens_line = (
+        f"OpenAI tokens: {usage.get('openai_total', 0)} | "
+        f"Claude tokens: {usage.get('claude_total', 0)} | "
+        f"Total: {usage.get('total', 0)}"
+    )
+
+    # CSV export
+    ts = datetime.datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+    csv_path = f"/tmp/email_eval_{ts}.csv"
+    with open(csv_path, "w", encoding="utf-8", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(["metric", "score_0_10"])
+        for k in metric_keys():
+            writer.writerow([k, out["scores"][k]])
+        writer.writerow(["weighted_total", out["weighted_total"]])
+
+    # ZIP export (CSV + JSON)
+    zip_path = f"/tmp/email_eval_{ts}.zip"
+    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
+        zf.writestr(f"email_eval_{ts}.json", out_json_str)
+        with open(csv_path, "rb") as f:
+            zf.writestr(f"email_eval_{ts}.csv", f.read())
+
+    # Scores table data (metric, score_0_10) + weighted total
+    table_rows = [[k, out["scores"][k]] for k in metric_keys()]
+    table_rows.append(["weighted_total", out["weighted_total"]])
+
+    return table_rows, comments_str, tokens_line, csv_path, zip_path
+
+
+with gr.Blocks(title="Email Evaluator (Subject + Body)") as demo:
+    gr.HTML("""
+    <style>
+    button.primary, .gr-button-primary, button[aria-label="Evaluate"].primary {
+        background-color: #000 !important; color: #fff !important; border-color: #000 !important;
+    }
+    </style>
+    """)
+    gr.Markdown(
+        "# Email Evaluator — Subject + Body\n"
+        "Six metrics · Rules + math-based LLM (OpenAI / Claude) · Research-grounded defaults."
+    )
+
+    subject = gr.Textbox(
+        label="Subject",
+        placeholder="e.g., Q3 draft — please review by Thu 5 PM"
+    )
+    body = gr.Textbox(
+        label="Body (plain text)",
+        lines=16,
+        placeholder="Paste the email body here..."
+    )
+    engine = gr.Dropdown(
+        ENGINES, value="openai",
+        label="Engine (LLM assistance for clarity, tone flags, personalization cues, spam-lexicon matching)"
+    )
+
+    gr.Markdown("### Weights (0–10 each; normalized internally)")
+    w1 = gr.Slider(0, 10, value=DEFAULT_WEIGHT_PRESETS["research_defaults"]["clarity"],
+                   step=0.5, label="Clarity")
+    w2 = gr.Slider(0, 10, value=DEFAULT_WEIGHT_PRESETS["research_defaults"]["length"],
+                   step=0.5, label="Length")
+    w3 = gr.Slider(0, 10, value=DEFAULT_WEIGHT_PRESETS["research_defaults"]["spam_score"],
+                   step=0.5, label="Spam")
+    w4 = gr.Slider(0, 10, value=DEFAULT_WEIGHT_PRESETS["research_defaults"]["personalization"],
+                   step=0.5, label="Personalization")
+    w5 = gr.Slider(0, 10, value=DEFAULT_WEIGHT_PRESETS["research_defaults"]["tone"],
+                   step=0.5, label="Tone")
+    w6 = gr.Slider(0, 10, value=DEFAULT_WEIGHT_PRESETS["research_defaults"]["grammatical_hygiene"],
+                   step=0.5, label="Grammar")
+
+    btn = gr.Button("Evaluate", variant="primary")
+
+    with gr.Tab("Scores"):
+        out_table = gr.Dataframe(headers=["metric","score_0_10"], row_count=(7,"fixed"), col_count=(2,"fixed"), wrap=True, interactive=False, label="Metric-wise scores and total")
+    with gr.Tab("Comments"):
+        comments = gr.Textbox(label="Comments & triggered rules", lines=10)
+    with gr.Tab("Usage"):
+        tokens = gr.Textbox(label="Tokens used")
+
+    with gr.Row():
+        dl_csv = gr.File(label="Download CSV")
+        dl_zip = gr.File(label="Download ZIP (CSV + JSON)")
+
+    btn.click(
+        run_eval,
+        inputs=[subject, body, engine, w1, w2, w3, w4, w5, w6],
+        outputs=[out_table, comments, tokens, dl_csv, dl_zip]
+    )
+
+if __name__ == "__main__":
+    # Hugging Face Spaces will run this automatically, but local runs benefit from this.
+    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")), show_error=True)

requirements.txt

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+gradio>=4.44.0
+regex>=2023.12.25
+beautifulsoup4>=4.12.3
+pyspellchecker>=0.8.1
+openai>=1.40.0         # for OpenAI mode
+anthropic>=0.34.0      # for Claude mode
+orjson>=3.10.7
+python-dotenv>=1.0.1   # optional .env loading for local keys