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<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Prediction Results</title>
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</style>
</head>
<body>
<div class="wrap">
<div class="topline">
<div>
<h2>Prediction Results: {{ prediction_id }}</h2>
<div class="meta">The model checkpoint has been loaded and predictions have been generated.</div>
</div>
<div class="pill">DONE</div>
</div>
<div class="card">
<form action="/predict" method="post" enctype="multipart/form-data" class="grid" style="margin-bottom: 16px;">
<div class="file-field">
<label>Saved Model (.pth)</label>
<div class="file-control">
<button type="button" data-file-target="predict_model_file">Choose file</button>
<span class="file-name" data-file-name="predict_model_file">No file selected</span>
<input type="file" id="predict_model_file" name="model_file" accept=".pth" required>
</div>
</div>
<div class="file-field">
<label>True Label Mapping File (optional, .json/.txt)</label>
<div class="file-control">
<button type="button" data-file-target="predict_label_mapping_file">Choose file</button>
<span class="file-name" data-file-name="predict_label_mapping_file">No file selected</span>
<input type="file" id="predict_label_mapping_file" name="label_mapping_file" accept=".json,.txt">
</div>
</div>
<div class="file-field">
<label>Spectral (.npy/.txt/.csv)</label>
<div class="file-control">
<button type="button" data-file-target="predict_spectral_file">Choose file</button>
<span class="file-name" data-file-name="predict_spectral_file">No file selected</span>
<input type="file" id="predict_spectral_file" name="spectral_file" accept=".npy,.txt,.csv" required>
</div>
</div>
<div class="file-field">
<label>Wavelengths / Wavenumbers (optional, .npy/.txt)</label>
<div class="file-control">
<button type="button" data-file-target="predict_wavenumbers_file">Choose file</button>
<span class="file-name" data-file-name="predict_wavenumbers_file">No file selected</span>
<input type="file" id="predict_wavenumbers_file" name="wavenumbers_file" accept=".npy,.txt,.csv">
</div>
</div>
<div class="file-field">
<label>Manual wavelength range fallback (optional)</label>
<div class="grid" style="grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 8px;">
<div>
<label style="margin-bottom: 4px;">Low value</label>
<input type="number" step="0.0001" name="manual_low_cm" placeholder="e.g. 0">
</div>
<div>
<label style="margin-bottom: 4px;">High value</label>
<input type="number" step="0.0001" name="manual_high_cm" placeholder="e.g. 3500">
</div>
</div>
</div>
<div style="grid-column: 1 / -1;">
<button type="submit" style="width: 100%;">Run Prediction</button>
</div>
</form>
<div class="helper-note">
If your spectrum file already contains two columns (wavelength and intensity), you can leave the wavelength file empty.
If the spectrum file contains only intensity values, upload a wavelength file or fill in the manual range.
</div>
<div class="stats">
<div class="stat"><div class="k">Samples</div><div class="v">{{ summary.num_samples }}</div></div>
<div class="stat"><div class="k">Classes</div><div class="v">{{ summary.class_names|length }}</div></div>
<div class="stat"><div class="k">Download CSV</div><div class="v"><a href="{{ download_csv }}">CSV</a></div></div>
</div>
<div class="meta" style="margin-top: 12px;">If you need the same checkpoint for another dataset, keep the exported pth file and upload it here again.</div>
</div>
<div class="grid">
<div class="card">
<h3>Per-sample Predictions</h3>
<table>
<thead>
<tr>
<th>Sample</th>
<th>Predicted class</th>
<th>Confidence</th>
</tr>
</thead>
<tbody>
{% for row in rows %}
<tr>
<td>{{ row.sample_index }}</td>
<td>{{ row.pred_label }}</td>
<td>{{ "%.4f"|format(row.confidence) }}</td>
</tr>
{% endfor %}
</tbody>
</table>
</div>
<div class="card">
<h3>Top-5 Logits</h3>
<div class="top5-grid">
{% for row in top5_rows %}
<div class="top5-item">
<strong>Sample {{ row.sample_index }}</strong>
<div>
{% for item in row.top5 %}
<div>Top{{ item.rank }}: {{ item.class_name }} - {{ "%.6f"|format(item.logit) }}</div>
{% endfor %}
</div>
</div>
{% endfor %}
</div>
</div>
</div>
<div class="grid">
<div class="card">
<h3>Input Spectra Preview</h3>
<img class="preview-img" src="{{ preview_image }}" alt="Input spectra preview">
</div>
</div>
<div class="card">
<h3>Model Notes</h3>
<div class="meta">The model checkpoint was loaded with its saved architecture and preprocessing config, so the same pth file can be reused later for inference on new spectra.</div>
{% if summary.spectrum_source or summary.wavenumber_source %}
<div class="helper-note">Spectrum source: {{ summary.spectrum_source }} | Wavelength source: {{ summary.wavenumber_source }}</div>
{% endif %}
{% if summary.label_mapping_source %}
<div class="helper-note">Label mapping source: {{ summary.label_mapping_source }}</div>
{% endif %}
</div>
<p style="margin-top: 12px;"><a href="/">Back</a></p>
</div>
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