Update: GTM Architect v2.6.2 (Edit Specs, Report Fix) & Company Explorer v0.4 (Export, Timestamps)

gtm_architect_orchestrator.py

@@ -211,6 +211,7 @@ def phase1(payload):
             "product_id": "string (slug)",
             "brand": "string",
             "model_name": "string",
+            "description": "string (short marketing description of the product)",
             "category": "cleaning | service | security | industrial",
             "manufacturer_url": "string"
           },
@@ -289,6 +290,16 @@ def phase1(payload):
                     specs_data['metadata'] = {}
                 specs_data['metadata']['manufacturer_url'] = product_input.strip()
             
+            # AUTO-RENAME PROJECT based on extracted metadata
+            if 'metadata' in specs_data:
+                brand = specs_data['metadata'].get('brand', '')
+                model = specs_data['metadata'].get('model_name', '')
+                if brand or model:
+                    new_name = f"{brand} {model}".strip()
+                    if new_name:
+                        logging.info(f"Renaming project {project_id} to: {new_name}")
+                        db_manager.update_project_name(project_id, new_name)
+
             data['specs'] = specs_data
         except json.JSONDecodeError:
             logging.error(f"Failed to decode JSON from Gemini response in phase1 (specs): {specs_response}")
@@ -404,9 +415,11 @@ def phase5(payload):
     lang_instr = get_output_lang_instruction(lang)
 
     # Reduziere Input-Daten auf das Wesentliche, um den Output-Fokus zu verbessern
+    # FIX: Include 'specs' (Hard Facts) for the report
     lean_phase1 = {
         "features": phase1_data.get('features', []),
-        "constraints": phase1_data.get('constraints', [])
+        "constraints": phase1_data.get('constraints', []),
+        "specs": phase1_data.get('specs', {})
     }
 
     prompt = f"""
@@ -415,6 +428,7 @@ def phase5(payload):
     INPUT DATA:
     - Product: {json.dumps(lean_phase1)}
     - ICPs: {json.dumps(phase2_data.get('icps', []))}
+    - Data Proxies: {json.dumps(phase2_data.get('dataProxies', []))}
     - Targets: {json.dumps(phase3_data.get('whales', []))}
     - Strategy Matrix: {json.dumps(phase4_data.get('strategyMatrix', []))}
     
@@ -424,9 +438,14 @@ def phase5(payload):
     REQUIRED STRUCTURE:
     1. **Executive Summary**: A brief strategic overview.
     2. **Product Analysis**: Key features & constraints.
-    3. **Target Audience**: The selected ICPs and why.
-    4. **Target Accounts**: Top companies (Whales).
-    5. **Strategy Matrix**:
+    3. **Technical Specifications (Hard Facts)**:
+       - Create a dedicated section displaying the technical data (Dimensions, Weight, Runtime, Layers, etc.) found in 'Product.specs'.
+       - Use a clear Markdown table or bullet points.
+    4. **Phase 2: ICP Discovery**:
+       - Present the selected ICPs (Industries) and the rationale behind them.
+       - List the identified Data Proxies used to find these customers.
+    5. **Target Accounts**: Top companies (Whales).
+    6. **Strategy Matrix**:
        - Create a STRICT Markdown table.
        - Columns: Segment | Pain Point | Angle | Differentiation
        - Template:
@@ -435,8 +454,8 @@ def phase5(payload):
          | [Content] | [Content] | [Content] | [Content] |
        - Use the data from the 'Strategy Matrix' input.
        - Do NOT use newlines inside table cells (use <br> instead) to keep the table structure intact.
-    6. **Next Steps**: Actionable recommendations.
-    7. **Hybrid Service Logic**: Explain the machine/human symbiosis.
+    7. **Next Steps**: Actionable recommendations.
+    8. **Hybrid Service Logic**: Explain the machine/human symbiosis.
     
     {lang_instr}
     
@@ -561,6 +580,47 @@ def phase9(payload):
     data = json.loads(response)
     db_manager.save_gtm_result(project_id, 'phase9_result', json.dumps(data))
     return data
+
+def update_specs(payload):
+    """
+    Updates the technical specifications (Hard Facts) for a project.
+    This allows manual correction of AI-extracted data.
+    """
+    project_id = payload.get('projectId')
+    new_specs = payload.get('specs')
+    
+    if not project_id:
+        raise ValueError("No projectId provided for update_specs.")
+    if not new_specs:
+        raise ValueError("No specs provided for update_specs.")
+        
+    # Load current project data
+    project_data = db_manager.get_project_data(project_id)
+    if not project_data:
+        raise ValueError(f"Project {project_id} not found.")
+        
+    phases = project_data.get('phases', {})
+    phase1_result = phases.get('phase1_result')
+    
+    if not phase1_result:
+        raise ValueError("Phase 1 result not found. Cannot update specs.")
+    
+    # FIX: Parse JSON string if necessary
+    if isinstance(phase1_result, str):
+        try:
+            phase1_result = json.loads(phase1_result)
+        except json.JSONDecodeError:
+            raise ValueError("Phase 1 result is corrupted (invalid JSON string).")
+
+    # Update specs
+    phase1_result['specs'] = new_specs
+    
+    # Save back to DB
+    # We use save_gtm_result which expects a stringified JSON for the phase result
+    db_manager.save_gtm_result(project_id, 'phase1_result', json.dumps(phase1_result))
+    
+    logging.info(f"Updated specs for project {project_id}")
+    return {"status": "success", "specs": new_specs}
     
 def translate(payload):
     # ... (to be implemented)
@@ -633,6 +693,7 @@ def main():
         "phase7": phase7,
         "phase8": phase8,
         "phase9": phase9,
+        "update_specs": update_specs,
         "translate": translate,
         "image": image,
         "list_history": list_history,