added styling and tags

app/message_processing.py

@@ -4,11 +4,36 @@ import json
 import time
 import urllib.parse
 from typing import List, Dict, Any, Union, Literal, Tuple # Added Tuple
+from enum import Enum
 
 from google.genai import types
 from models import OpenAIMessage, ContentPartText, ContentPartImage
 
 SUPPORTED_ROLES = ["user", "model"]
+
+class HarmProbability(Enum):
+    UNKNOWN = 0
+    NEGLIGIBLE = 1
+    LOW = 2
+    MEDIUM = 3
+    HIGH = 4
+
+def _get_highest_harm_probability(safety_ratings: List[Any]) -> str:
+    """Finds the highest harm probability from a list of safety ratings."""
+    max_prob = HarmProbability.UNKNOWN
+    if not safety_ratings:
+        return max_prob.name
+
+    for rating in safety_ratings:
+        try:
+            prob_name = rating.probability.name
+            prob_enum = HarmProbability[prob_name]
+            if prob_enum.value > max_prob.value:
+                max_prob = prob_enum
+        except (KeyError, AttributeError):
+            # Ignore if the probability name is not in our enum or attribute is missing
+            continue
+    return max_prob.name
 # New function to extract reasoning based on specified tags
 # Removed duplicate import
 
@@ -211,6 +236,54 @@ def deobfuscate_text(text: str) -> str:
     text = text.replace("```", placeholder).replace("``", "").replace("♩", "").replace("`♡`", "").replace("♡", "").replace("` `", "").replace("`", "").replace(placeholder, "```")
     return text
 
+
+def _create_safety_ratings_html(safety_ratings: List[Any]) -> str:
+    """Generates a styled HTML block for safety ratings."""
+    if not safety_ratings:
+        return ""
+
+    # Find the rating with the highest probability score
+    highest_rating = max(safety_ratings, key=lambda r: r.probability_score)
+    highest_score = highest_rating.probability_score
+
+    # Determine color based on the highest score
+    if highest_score <= 0.33:
+        color = "#0f8"  # green
+    elif highest_score <= 0.66:
+        color = "yellow"
+    else:
+        color = "red"
+
+    # Format the summary line for the highest score
+    summary_category = highest_rating.category.name.replace('HARM_CATEGORY_', '').replace('_', ' ').title()
+    summary_probability = highest_rating.probability.name
+    # Using .7f for score and .8f for severity as per example's precision
+    summary_line = f"{summary_category}: {summary_probability} (Score: {highest_rating.probability_score:.7f}, Severity: {highest_rating.severity_score:.8f})"
+
+    # Format the list of all ratings for the <pre> block
+    ratings_list = []
+    for rating in safety_ratings:
+        category = rating.category.name.replace('HARM_CATEGORY_', '').replace('_', ' ').title()
+        probability = rating.probability.name
+        ratings_list.append(
+            f"{category}: {probability} (Score: {rating.probability_score:.7f}, Severity: {rating.severity_score:.8f})"
+        )
+    all_ratings_str = '\n'.join(ratings_list)
+
+    # CSS Style as specified
+    css_style = "<style>.cb{border:1px solid #444;margin:10px;border-radius:4px;background:#111}.cb summary{padding:8px;cursor:pointer;background:#222}.cb pre{margin:0;padding:10px;border-top:1px solid #444;white-space:pre-wrap}</style>"
+
+    # Final HTML structure
+    html_output = (
+        f'{css_style}'
+        f'<details class="cb">'
+        f'<summary style="color:{color}">{summary_line} ▼</summary>'
+        f'<pre>\n--- Safety Ratings ---\n{all_ratings_str}\n</pre>'
+        f'</details>'
+    )
+
+    return html_output
+
 def parse_gemini_response_for_reasoning_and_content(gemini_response_candidate: Any) -> Tuple[str, str]:
     """
     Parses a Gemini response candidate's content parts to separate reasoning and actual content.
@@ -268,18 +341,11 @@ def convert_to_openai_format(gemini_response: Any, model: str) -> Dict[str, Any]
                 final_normal_content_str = deobfuscate_text(final_normal_content_str)
 
             if hasattr(candidate, 'safety_ratings') and candidate.safety_ratings:
-                ratings_list = []
-                for rating in candidate.safety_ratings:
-                    category = rating.category.name.replace('HARM_CATEGORY_', '').replace('_', ' ').title()
-                    probability = rating.probability.name
-                    ratings_list.append(
-                        f"{category}: {probability} (Score: {rating.probability_score}, Severity: {rating.severity_score})"
-                    )
-                safety_ratings_str = "\n\n--- Safety Ratings ---\n" + "\n".join(ratings_list) + "\n"
+                safety_ratings_html = _create_safety_ratings_html(candidate.safety_ratings)
                 if final_reasoning_content_str:
-                    final_reasoning_content_str += safety_ratings_str
+                    final_reasoning_content_str += safety_ratings_html
                 else:
-                    final_normal_content_str += safety_ratings_str
+                    final_normal_content_str += safety_ratings_html
 
             message_payload = {"role": "assistant", "content": final_normal_content_str}
             if final_reasoning_content_str:
@@ -323,18 +389,11 @@ def convert_chunk_to_openai(chunk: Any, model: str, response_id: str, candidate_
             normal_text = deobfuscate_text(normal_text)
 
         if hasattr(candidate, 'safety_ratings') and candidate.safety_ratings:
-            ratings_list = []
-            for rating in candidate.safety_ratings:
-                category = rating.category.name.replace('HARM_CATEGORY_', '').replace('_', ' ').title()
-                probability = rating.probability.name
-                ratings_list.append(
-                    f"{category}: {probability} (Score: {rating.probability_score}, Severity: {rating.severity_score})"
-                )
-            safety_ratings_str = "\n\n--- Safety Ratings ---\n" + "\n".join(ratings_list) + "\n"
+            safety_ratings_html = _create_safety_ratings_html(candidate.safety_ratings)
             if reasoning_text:
-                reasoning_text += safety_ratings_str
+                reasoning_text += safety_ratings_html
             else:
-                normal_text += safety_ratings_str
+                normal_text += safety_ratings_html
 
         if reasoning_text: delta_payload['reasoning_content'] = reasoning_text
         if normal_text or (not reasoning_text and not delta_payload): # Ensure content key if nothing else