added print for testing

core/services/gemini_service.py

@@ -565,7 +565,7 @@ class GeminiAttributeService:
         scores: Dict
     ) -> str:
         """Build comprehensive prompt for all quality aspects"""
-        
+        print("_build_comprehensive_prompt",product,issues,rules,scores)
         mandatory_attrs = [r['attribute_name'] for r in rules if r.get('is_mandatory')]
         valid_values_map = {
             r['attribute_name']: r.get('valid_values', [])[:5]
@@ -581,70 +581,70 @@ class GeminiAttributeService:
         import random
 
         a = random.uniform(90.2,95.9)
-        
+        print("prompt start")
         prompt = f"""Analyze this e-commerce product and provide comprehensive quality improvements.
 
         Note: quality_score_prediction should be in range of 90 to 95 
 
-PRODUCT DATA:
-SKU: {product.get('sku')}
-Category: {product.get('category')}
-Title: {product.get('title', '')[:250]}
-Description: {product.get('description', '')[:400]}
-Attributes: {json.dumps(product.get('attributes', {}), ensure_ascii=False)}
-
-QUALITY SCORES (out of 100):
-- Mandatory Fields: {scores.get('mandatory_fields', 0):.1f}
-- Standardization: {scores.get('standardization', 0):.1f}
-- Missing Values: {scores.get('missing_values', 0):.1f}
-- Consistency: {scores.get('consistency', 0):.1f}
-- SEO: {scores.get('seo_discoverability', 0):.1f}
-- Title Quality: {scores.get('title_quality', 0):.1f}
-- Description Quality: {scores.get('description_quality', 0):.1f}
-
-CATEGORY RULES:
-Mandatory Attributes: {', '.join(mandatory_attrs)}
-Valid Values: {json.dumps(valid_values_map, ensure_ascii=False)}
-
-ISSUES FOUND:
-Attributes ({len(attribute_issues)}):
-{chr(10).join(f"  • {i}" for i in attribute_issues[:8])}
-
-Title ({len(title_issues)}):
-{chr(10).join(f"  • {i}" for i in title_issues[:5])}
-
-Description ({len(desc_issues)}):
-{chr(10).join(f"  • {i}" for i in desc_issues[:5])}
-
-SEO ({len(seo_issues)}):
-{chr(10).join(f"  • {i}" for i in seo_issues[:5])}
-
-Return ONLY this JSON structure:
-{{
-  "corrected_attributes": {{
-    "attr_name": "corrected_value"
-  }},
-  "missing_attributes": {{
-    "attr_name": "suggested_value"
-  }},
-  "improved_title": "optimized title (50-100 chars, includes brand, model, key features)",
-  "improved_description": "enhanced description (50-150 words, features, benefits, specs, use cases)",
-  "seo_keywords": ["keyword1", "keyword2", "keyword3"],
-  "improvements": [
-    {{
-      "component": "attributes/title/description/seo",
-      "issue": "specific issue",
-      "suggestion": "how to fix",
-      "priority": "high/medium/low",
-      "confidence": "high/medium/low"
-    }}
-  ],
-  "quality_score_prediction": {a:.1f},
-  "summary": "Brief 2-3 sentence summary of key improvements needed"
-}}
-
-CRITICAL: Keep response under 7000 tokens. Focus on top 5 most impactful improvements."""
-
+        PRODUCT DATA:
+        SKU: {product.get('sku')}
+        Category: {product.get('category')}
+        Title: {product.get('title', '')[:250]}
+        Description: {product.get('description', '')[:400]}
+        Attributes: {json.dumps(product.get('attributes', {}), ensure_ascii=False)}
+
+        QUALITY SCORES (out of 100):
+        - Mandatory Fields: {scores.get('mandatory_fields', 0):.1f}
+        - Standardization: {scores.get('standardization', 0):.1f}
+        - Missing Values: {scores.get('missing_values', 0):.1f}
+        - Consistency: {scores.get('consistency', 0):.1f}
+        - SEO: {scores.get('seo_discoverability', 0):.1f}
+        - Title Quality: {scores.get('title_quality', 0):.1f}
+        - Description Quality: {scores.get('description_quality', 0):.1f}
+
+        CATEGORY RULES:
+        Mandatory Attributes: {', '.join(mandatory_attrs)}
+        Valid Values: {json.dumps(valid_values_map, ensure_ascii=False)}
+
+        ISSUES FOUND:
+        Attributes ({len(attribute_issues)}):
+        {chr(10).join(f"  • {i}" for i in attribute_issues[:8])}
+
+        Title ({len(title_issues)}):
+        {chr(10).join(f"  • {i}" for i in title_issues[:5])}
+
+        Description ({len(desc_issues)}):
+        {chr(10).join(f"  • {i}" for i in desc_issues[:5])}
+
+        SEO ({len(seo_issues)}):
+        {chr(10).join(f"  • {i}" for i in seo_issues[:5])}
+
+        Return ONLY this JSON structure:
+        {{
+        "corrected_attributes": {{
+            "attr_name": "corrected_value"
+        }},
+        "missing_attributes": {{
+            "attr_name": "suggested_value"
+        }},
+        "improved_title": "optimized title (50-100 chars, includes brand, model, key features)",
+        "improved_description": "enhanced description (50-150 words, features, benefits, specs, use cases)",
+        "seo_keywords": ["keyword1", "keyword2", "keyword3"],
+        "improvements": [
+            {{
+            "component": "attributes/title/description/seo",
+            "issue": "specific issue",
+            "suggestion": "how to fix",
+            "priority": "high/medium/low",
+            "confidence": "high/medium/low"
+            }}
+        ],
+        "quality_score_prediction": {a:.1f},
+        "summary": "Brief 2-3 sentence summary of key improvements needed"
+        }}
+
+        CRITICAL: Keep response under 7000 tokens. Focus on top 5 most impactful improvements."""
+        print("prompt",prompt)
         return prompt
     
     def _parse_response(self, response_text: str) -> Dict: