server/app.py

@@ -124,31 +124,33 @@ def get_dataset_sources():
 @app.route("/datasets/scrape", methods=["POST"])
 @jwt_required()
 def scrape_data():
-    if "sources" not in request.form:
+    data = request.get_json()
-        return jsonify({"error": "Data source names are required."}), 400
+
+    if not data or "sources" not in data:
+            return jsonify({"error": "Sources must be provided"}), 400
     
     user_id = int(get_jwt_identity())
-    sources = request.form.getlist("sources")
+    dataset_name = data["name"].strip()
-    limit = int(request.form.get("limit", max_fetch_limit))
+    source_configs = data["sources"]
 
-    dataset_name = request.form.get("name", "").strip()
+    if not isinstance(source_configs, list) or len(source_configs) == 0:
-    search = request.form.get("search")
+        return jsonify({"error": "Sources must be a non-empty list"}), 400
-    category = request.form.get("category")
 
-    if limit > max_fetch_limit:
+    # Light Validation
-        return jsonify({"error": f"Due to API limitations, we cannot receive more than ${max_fetch_limit} posts"}), 400
+    for source in source_configs:
+        if "name" not in source:
+            return jsonify({"error": "Each source must contain a name"}), 400
+        if "limit" in source:
+            source["limit"] = int(source["limit"])
   
-    for source in sources:
-        if source not in connectors.keys():
-            return jsonify({"error": "Source must exist"}), 400
-        
-    limits = split_limit(limit, len(sources))
-    per_source = dict(zip(sources, limits))
     dataset_id = dataset_manager.save_dataset_info(user_id, dataset_name, default_topic_list)
-    dataset_manager.set_dataset_status(dataset_id, "fetching", f"Data is being fetched from {str(sources)}")
+    dataset_manager.set_dataset_status(dataset_id, 
+                                       "fetching", 
+                                       f"Data is being fetched from {str(source["name"] + "," for source in source_configs)}"
+                                       )
 
     try:
-        fetch_and_process_dataset.delay(dataset_id, per_source, search, category, default_topic_list)
+        fetch_and_process_dataset.delay(dataset_id, source_configs, default_topic_list)
 
         return jsonify(
             {

server/queue/tasks.py

@@ -1,5 +1,5 @@
 import pandas as pd
-import json
+import logging
 
 from server.queue.celery_app import celery
 from server.analysis.enrichment import DatasetEnrichment
@@ -7,6 +7,8 @@ from server.db.database import PostgresConnector
 from server.core.datasets import DatasetManager
 from server.connectors.registry import get_available_connectors
 
+logger = logging.getLogger(__name__)
+
 @celery.task(bind=True, max_retries=3)
 def process_dataset(self, dataset_id: int, posts: list, topics: dict):
     db = PostgresConnector()
@@ -26,9 +28,7 @@ def process_dataset(self, dataset_id: int, posts: list, topics: dict):
 @celery.task(bind=True, max_retries=3)
 def fetch_and_process_dataset(self, 
                               dataset_id: int, 
-                              per_source: dict[str, int], 
+                              source_info: list[dict],
-                              search: str, 
-                              category: str, 
                               topics: dict):
     connectors = get_available_connectors()
     db = PostgresConnector()
@@ -36,13 +36,18 @@ def fetch_and_process_dataset(self,
     posts = []
 
     try:
-        for source_name, source_limit in per_source.items():
+        for metadata in source_info:
-            connector = connectors[source_name]()
+            name = metadata["name"]
+            search = metadata.get("search")
+            category = metadata.get("category")
+            limit = metadata.get("limit", 100)
+
+            connector = connectors[name]()
             raw_posts = connector.get_new_posts_by_search(
                 search=search,
                 category=category,
-                post_limit=source_limit,
+                post_limit=limit,
-                comment_limit=source_limit
+                comment_limit=limit
             )
             posts.extend(post.to_dict() for post in raw_posts)