Armstrongttwalker alt patch 1

.github/workflows/update-modelscope-docs.yml

@@ -35,6 +35,12 @@ jobs:
     - name: Install Python dependencies
       run: |
         pip install -r requirements.txt
+
+    - name: Generate model list
+      run: |
+        cd "$GITHUB_WORKSPACE"
+        python docs/scripts/generate_model_list.py
+      continue-on-error: true 
 
     - name: Download ModelScope documentation
       id: download

docs/scripts/generate_model_list.py

@@ -0,0 +1,145 @@
+#!/usr/bin/env python3
+"""
+Automatically fetch the list of all models for the FlagRelease organization via the ModelScope official API.
+Version: 2.1 (Enhanced detection, compatible with both dictionary and list types for the Model field)
+"""
+
+import requests
+import json
+import time
+import os
+from collections import Counter
+
+def fetch_all_models():
+    url = "https://modelscope.cn/api/v1/dolphin/models"
+    all_models = []
+    page = 1
+    page_size = 20
+
+    payload_template = {
+        "PageSize": page_size,
+        "PageNumber": 1,
+        "SortBy": "GmtModified",
+        "Name": "",
+        "IncludePrePublish": True,
+        "Criterion": [{"category": "organizations", "predicate": "contains", "values": ["FlagRelease"]}]
+    }
+
+    headers = {
+        'Content-Type': 'application/json',
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
+        'Accept': 'application/json, text/plain, */*',
+        'Referer': 'https://modelscope.cn/organization/FlagRelease?tab=model',
+        'Origin': 'https://modelscope.cn',
+    }
+
+    print(f"Starting to fetch model list ({page_size} per page)...")
+
+    while page <= 50: # Safety upper limit
+        payload_template["PageNumber"] = page
+        try:
+            print(f"  Fetching page {page}...")
+            resp = requests.put(url, headers=headers, data=json.dumps(payload_template), timeout=30)
+            resp.raise_for_status()
+            data = resp.json()
+
+            # 1. Check basic response structure
+            if data.get('Code') not in [200, '200']:
+                print(f"    Abnormal response code: {data.get('Code')} - {data.get('Message')}")
+                break
+
+            data_field = data.get('Data', {})
+            if not isinstance(data_field, dict):
+                print(f"    The 'Data' field is not a dictionary: {type(data_field)}")
+                break
+
+            # 2. Core: Intelligently parse the 'Model' field
+            model_container = data_field.get('Model')
+            items_to_process = []
+
+            if isinstance(model_container, list):
+                print(f"    The 'Model' field is a list, length: {len(model_container)}")
+                items_to_process = model_container
+            elif isinstance(model_container, dict):
+                print(f"    The 'Model' field is a dictionary, its keys: {list(model_container.keys())}")
+                # Try to find a list within this dictionary
+                possible_list_keys = ['Items', 'Models', 'List', 'records', 'data', 'hits']
+                found = False
+                for key in possible_list_keys:
+                    if key in model_container and isinstance(model_container[key], list):
+                        items_to_process = model_container[key]
+                        print(f"      Found a list in Model['{key}'], length: {len(items_to_process)}")
+                        found = True
+                        break
+                if not found:
+                    print("      Warning: No common list field found in the Model dictionary.")
+            else:
+                print(f"    Unexpected type for 'Model' field: {type(model_container)}")
+
+            # 3. Process the found model entries
+            current_page_count = 0
+            if items_to_process:
+                for item in items_to_process:
+                    model_id = None
+                    if isinstance(item, dict):
+                        # Try multiple possible field names
+                        model_id = item.get('model_id') or item.get('ModelId') or item.get('id')
+                        if not model_id and item.get('Name'):
+                            org = item.get('Organization', {}).get('Name', 'FlagRelease')
+                            model_id = f"{org}/{item['Name']}"
+
+                    if model_id:
+                        if not model_id.startswith('FlagRelease/'):
+                            model_id = f"FlagRelease/{model_id}"
+                        if model_id not in all_models:
+                            all_models.append(model_id)
+                            current_page_count += 1
+                            if current_page_count <= 3: # Print only the first 3 per page to avoid clutter
+                                print(f"      Found: {model_id}")
+                print(f"    Page {page} extracted {current_page_count} new models.")
+            else:
+                print(f"    Page {page} has no processable model entries.")
+
+            # 4. Pagination judgment
+            if current_page_count < page_size:
+                print(f"    Reached the last page (items on this page {current_page_count} < {page_size}), stopping pagination.")
+                break
+
+            page += 1
+            time.sleep(0.3)
+
+        except Exception as e:
+            print(f"  Error processing page {page}: {type(e).__name__}: {e}")
+            break
+
+    return all_models
+
+if __name__ == '__main__':
+    print("=" * 60)
+    print("FlagRelease Organization Model List Fetcher v2.1 (Enhanced Detection)")
+    print("=" * 60)
+    models = fetch_all_models()
+
+    if not models:
+        print("\nWarning: Failed to fetch models. Please run diagnostics or check network.")
+    else:
+        unique_models = sorted(set(models))
+        print(f"\nSuccessfully fetched {len(unique_models)} unique models.")
+
+        # Save the file (adjust the path according to your project structure)
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        output_path = os.path.normpath(os.path.join(script_dir, '..', 'flagrelease_en', 'model_list.txt'))
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+        with open(output_path, 'w', encoding='utf-8') as f:
+            for model in unique_models:
+                f.write(f"{model}\n")
+        print(f"List saved to: {output_path}")
+
+        # Simple statistics
+        series_counter = Counter()
+        for model in unique_models:
+            short_name = model.replace('FlagRelease/', '')
+            series = short_name.split('-')[0] if '-' in short_name else short_name[:10]
+            series_counter[series] += 1
+        print(f"Total of {len(series_counter)} different model series.")