Update app.py

app.py

@@ -147,10 +147,49 @@ def compute_similarity(resume_text, job_list):
         best_job = metrics["Average"].idxmax()
         reasoning = f"<b>The best job match is {best_job} based on the highest average similarity score.</b>"
 
+        # --- RESTORED FULL DESCRIPTION AND REFERENCES ---
         description = """
         <p><b>Explanation of the Table:</b></p>
-        <p>See below for model details (same as previous version)...</p>
-        <p>If you liked this application, feel free to send your feedback to <b>[email protected]</b>.</p>
+        <ul>
+            <li><b>Models:</b> Each row corresponds to a pre-trained model used for computing similarity. Below are details about each model:</li>
+            <ul>
+                <li><b>all-MiniLM-L6-v2:</b> Trained on NLI and STS datasets. Developed by Hugging Face and Microsoft. 
+                    (<a href="https://arxiv.org/abs/2012.15832" target="_blank">Research Paper</a>, 
+                    <a href="https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" target="_blank">Model Card</a>).</li>
+                <li><b>paraphrase-MiniLM-L6-v2:</b> Optimized for paraphrase detection on datasets like Quora Questions and MSRPC. 
+                    (<a href="https://arxiv.org/abs/2012.15832" target="_blank">Research Paper</a>, 
+                    <a href="https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2" target="_blank">Model Card</a>).</li>
+                <li><b>multi-qa-MiniLM-L6-cos-v1:</b> Fine-tuned for question-answering tasks using datasets like SQuAD and Natural Questions. 
+                    (<a href="https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1" target="_blank">Model Card</a>).</li>
+                <li><b>all-mpnet-base-v2:</b> Robust embeddings for high-contextualized tasks. 
+                    (<a href="https://arxiv.org/abs/2004.09297" target="_blank">Research Paper</a>, 
+                    <a href="https://huggingface.co/sentence-transformers/all-mpnet-base-v2" target="_blank">Model Card</a>).</li>
+                <li><b>paraphrase-mpnet-base-v2:</b> Reliable for paraphrase tasks, trained on diverse datasets. 
+                    (<a href="https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2" target="_blank">Model Card</a>).</li>
+                <li><b>all-distilroberta-v1:</b> A lightweight RoBERTa-based model for sentence embeddings. 
+                    (<a href="https://arxiv.org/abs/1907.11692" target="_blank">Research Paper</a>, 
+                    <a href="https://huggingface.co/sentence-transformers/all-distilroberta-v1" target="_blank">Model Card</a>).</li>
+                <li><b>paraphrase-albert-small-v2:</b> Suitable for paraphrasing in resource-constrained environments. 
+                    (<a href="https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2" target="_blank">Model Card</a>).</li>
+                <li><b>multi-qa-distilbert-cos-v1:</b> Optimized for multilingual question-answering tasks. 
+                    (<a href="https://huggingface.co/sentence-transformers/multi-qa-distilbert-cos-v1" target="_blank">Model Card</a>).</li>
+                <li><b>distiluse-base-multilingual-cased-v2:</b> Trained on multilingual datasets for cross-lingual embeddings. 
+                    (<a href="https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2" target="_blank">Model Card</a>).</li>
+                <li><b>all-MiniLM-L12-v2:</b> Deeper MiniLM variant for enhanced contextual understanding. 
+                    (<a href="https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2" target="_blank">Model Card</a>).</li>
+            </ul>
+            <li><b>Metrics:</b>
+                <ul>
+                    <li><b>Average:</b> Mean similarity score for each job description.</li>
+                    <li><b>Variance:</b> Variability in the similarity scores.</li>
+                    <li><b>Median:</b> Middle value of the similarity scores.</li>
+                    <li><b>Standard Deviation:</b> Spread of the similarity scores around the mean.</li>
+                    <li><b>Certainty Score:</b> Indicates model agreement, with 1 being the highest consensus.</li>
+                </ul>
+            </li>
+        </ul>
+        <p>If you liked this application, feel free to send your feedback, suggestions, or adulations to 
+        <b>[email protected]</b>.</p>
         """
 
         return reasoning, styled_df + description
@@ -237,7 +276,7 @@ def add_job_to_list(current_job, job_list):
     return updated_list, display_html, "" # Clear the input box
 
 def clear_jobs():
-    return [], "", ""
+    return [], "<i>No jobs added yet...</i>", ""
 
 # --- Main Processing ---
 def process_and_display(resume, job_list, request=None):