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| import gradio as gr | |
| from src.api_clients import openai_client, claude_client, deepseek_client | |
| from src.summary_generator import get_openrouter_models, generate_summary_from_openrouter, is_prompt_valid_for_summary | |
| from src.evaluation import evaluate | |
| from src.comparison import get_last_eval_data, run_comparison, import_model_metrics | |
| from src.utils import preset_vals | |
| from config.css import CSS | |
| with gr.Blocks(css=CSS) as demo: | |
| with gr.Tabs(): | |
| # ▸ Tab 1: Unified Summary + Evaluation | |
| with gr.Tab("🧠 Generate + Evaluate"): | |
| gr.Markdown("## Unified Summary Generator and Evaluator") | |
| article = gr.Textbox(label="Paste article", lines=10) | |
| auto_gen_toggle = gr.Checkbox(label="Enable Auto-Generation if Summary is Empty", value=True) | |
| summary = gr.Textbox(label="Paste summary", lines=6, visible=False, interactive=True) | |
| prompt = gr.Textbox(label="Prompt for summary (only if generating)", lines=2, visible=True, interactive=True) | |
| model = gr.Dropdown(choices=get_openrouter_models(), label="Model (for generation)", visible=True, interactive=True) | |
| variant = gr.Radio(["Twin-Lock","Judge-Lock","ParallelX-TJ"], value="Twin-Lock", label="Variant", elem_id="variant-group") | |
| back = gr.CheckboxGroup(["OpenAI","DeepSeek","Claude"], value=["OpenAI","DeepSeek","Claude"], label="Back-ends", elem_id="backend-group") | |
| p0 = preset_vals("Twin-Lock") | |
| w_cov = gr.Slider(0,1,p0[0],step=0.01,label="Coverage", elem_classes=["metric-slider"]) | |
| w_align= gr.Slider(0,1,p0[1],step=0.01,label="Alignment", elem_classes=["metric-slider"]) | |
| w_hall = gr.Slider(0,1,p0[2],step=0.01,label="Hallucination", elem_classes=["metric-slider"]) | |
| w_rel = gr.Slider(0,1,p0[3],step=0.01,label="Relevance", elem_classes=["metric-slider"]) | |
| w_bias = gr.Slider(0,1,p0[4],step=0.01,label="Bias/Toxicity", elem_classes=["metric-slider"]) | |
| temp = gr.Slider(0,1,0,step=0.01,label="temperature") | |
| show_ev = gr.Checkbox(True,label="Show evidence spans") | |
| run = gr.Button("🔁 Generate & Evaluate") | |
| gen_sum = gr.Textbox(label="Generated Summary", lines=6, visible=True) | |
| table = gr.DataFrame(label="Metrics") | |
| comm = gr.JSON(label="Comments JSON") | |
| score = gr.JSON(label="Average score") | |
| tokbox = gr.JSON(label="Token usage") | |
| csv_dl = gr.File(label="CSV download") | |
| zip_dl = gr.File(label="Raw JSON zip") | |
| def toggle_ui(auto): | |
| return ( | |
| gr.update(visible=not auto, interactive=not auto), | |
| gr.update(visible=auto, interactive=auto), | |
| gr.update(visible=auto, interactive=auto), | |
| gr.update(value=("🔁 Generate & Evaluate" if auto else "✅ Only Evaluate")), | |
| gr.update(visible=auto) # toggle gen_sum | |
| ) | |
| auto_gen_toggle.change( | |
| toggle_ui, | |
| auto_gen_toggle, | |
| [summary, prompt, model, run, gen_sum] | |
| ) | |
| def unified_run(article, prompt, model, summary, auto_flag, variant, active_back, temp, | |
| w_cov, w_align, w_hall, w_rel, w_bias, show_ev): | |
| if auto_flag and not summary: | |
| if not is_prompt_valid_for_summary(prompt): | |
| return "⛔ Prompt rejected: not summarization-related.", None, None, None, None, None, None | |
| summary = generate_summary_from_openrouter(article, prompt, model) | |
| elif not auto_flag and not summary: | |
| return "⚠️ Please provide a summary or enable auto-generation.", None, None, None, None, None, None | |
| return summary, *evaluate(article, summary, variant, active_back, temp, w_cov, w_align, w_hall, w_rel, w_bias, show_ev) | |
| run.click( | |
| unified_run, | |
| [article, prompt, model, summary, auto_gen_toggle, variant, back, temp, | |
| w_cov,w_align,w_hall,w_rel,w_bias,show_ev], | |
| [gen_sum, table, comm, score, tokbox, csv_dl, zip_dl] | |
| ) | |
| # ▸ Tab 3: Comparison | |
| # ▸ Tab 3: Comparison (manual inputs for both human and model, no import button) | |
| with gr.Tab("📊 Comparison"): | |
| with gr.Column(): | |
| gr.Markdown("## Compare Human vs Model Evaluations") | |
| gr.Markdown("### 🧍 Human Evaluation") | |
| with gr.Row(): | |
| hc = gr.Slider(1, 10, 1, step=1, label="Coverage") | |
| ha = gr.Slider(1, 10, 1, step=1, label="Alignment") | |
| hh = gr.Slider(1, 10, 1, step=1, label="Hallucination") | |
| hr = gr.Slider(1, 10, 1, step=1, label="Relevance") | |
| hb = gr.Slider(1, 10, 1, step=1, label="Bias/Toxicity") | |
| human_comments = gr.Textbox(label="Human Comments") | |
| gr.Markdown("### 🤖 Model Evaluation") | |
| with gr.Row(): | |
| mc = gr.Slider(1, 10, 1, step=1, label="Coverage") | |
| ma = gr.Slider(1, 10, 1, step=1, label="Alignment") | |
| mh = gr.Slider(1, 10, 1, step=1, label="Hallucination") | |
| mr = gr.Slider(1, 10, 1, step=1, label="Relevance") | |
| mb = gr.Slider(1, 10, 1, step=1, label="Bias/Toxicity") | |
| model_comments = gr.Textbox(label="Model Comments") | |
| def compare_structured(hc, ha, hh, hr, hb, human_comments, mc, ma, mh, mr, mb, model_comments): | |
| human_scores = { | |
| "coverage": hc, | |
| "alignment": ha, | |
| "hallucination": hh, | |
| "relevance": hr, | |
| "bias_toxicity": hb | |
| } | |
| model_scores = { | |
| "coverage": mc, | |
| "alignment": ma, | |
| "hallucination": mh, | |
| "relevance": mr, | |
| "bias_toxicity": mb | |
| } | |
| return run_comparison(human_scores, human_comments, model_scores, model_comments) | |
| compare_btn = gr.Button("🔍 Compare") | |
| output_analysis = gr.Textbox(label="Analysis", lines=10) | |
| compare_btn.click( | |
| compare_structured, | |
| [hc, ha, hh, hr, hb, human_comments, mc, ma, mh, mr, mb, model_comments], | |
| output_analysis | |
| ) | |
| demo.launch(share=True, show_error=True) |