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converter.py

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+import csv
+import json
+import re
+
+def parse_description(description_text):
+    """
+    Parses the description text into a structured dictionary.
+    """
+    # Headers to look for in the description text.
+    # The order is important for splitting.
+    headers = [
+        "Background",
+        "Problem Description",
+        "Description",
+        "Impact / Why this problem needs to be solved",
+        "Impact",
+        "Expected Solution",
+        "Expected Outcomes",
+        "Relevant Stakeholders / Beneficiaries",
+        "Supporting Data",
+        "Objective",
+        "Technical Scope",
+        "Data Acquisition & Sampling",
+        "Data Processing & Analysis",
+        "Output & Storage",
+        "Key Performance Parameters",
+        "Eligibility",
+        "Evaluation Criteria",
+        "Deliverables",
+        "Conclusion",
+        "Innovative Features",
+        "Key Features",
+        "Additional Features",
+        "Digital Tourist ID Generation Platform",
+        "Mobile Application for Tourists",
+        "AI-Based Anomaly Detection",
+        "Tourism Department & Police Dashboard",
+        "IoT Integration (Optional)",
+        "Multilingual Support",
+        "Data Privacy & Security"
+    ]
+
+    # Create a regex pattern to split the text by the headers.
+    # The pattern looks for a header at the beginning of a line.
+    pattern = r'^\s*(' + '|'.join(re.escape(h) for h in headers) + r')\s*$'
+    
+    # Split the text by the headers
+    parts = re.split(pattern, description_text, flags=re.MULTILINE)
+    
+    details = {}
+    # The first part is the introduction if it's not a header
+    if parts[0].strip():
+        details['introduction'] = parts[0].strip()
+
+    # The rest of the parts are pairs of (header, content)
+    it = iter(parts[1:])
+    for header in it:
+        content = next(it, "").strip()
+        if header and content:
+            # Normalize header to be used as a json key
+            key = header.strip().lower().replace(' ', '_').replace('/', '_').replace('(', '').replace(')', '')
+            details[key] = content
+
+    # If details is empty, it means no headers were found.
+    # In this case, the whole text is the description.
+    if not details:
+        return {'full_text': description_text.strip()}
+        
+    return details
+
+def convert_csv_to_json(csv_file_path, json_file_path):
+    """
+    Converts a CSV file to a structured JSON file.
+    """
+    problems = []
+    with open(csv_file_path, mode='r', encoding='utf-8') as csv_file:
+        # Use DictReader to read CSV rows as dictionaries
+        csv_reader = csv.DictReader(csv_file)
+        
+        for row in csv_reader:
+            # Clean up keys from the CSV header
+            cleaned_row = {key.strip().replace(' ', '_').replace('.', ''): value for key, value in row.items()}
+            
+            problem_data = {
+                's_no': int(cleaned_row.get('SNo', 0)),
+                'organization': cleaned_row.get('Organization', ''),
+                'title': cleaned_row.get('Problem_Statement_Title', ''),
+                'category': cleaned_row.get('Category', ''),
+                'ps_number': cleaned_row.get('PS_Number', ''),
+                'submitted_ideas_count': int(cleaned_row.get('Submitted_Ideas_Count', 0)),
+                'theme': cleaned_row.get('Theme', ''),
+                'details': parse_description(cleaned_row.get('Problem_Description', ''))
+            }
+            problems.append(problem_data)
+
+    with open(json_file_path, mode='w', encoding='utf-8') as json_file:
+        json.dump({"problems": problems}, json_file, indent=2)
+
+if __name__ == '__main__':
+    csv_input_file = 'SIH_Problem_Statements.csv'
+    json_output_file = 'SIH_Problem_Statements_structured.json'
+    
+    convert_csv_to_json(csv_input_file, json_output_file)
+    
+    print(f"Successfully converted '{csv_input_file}' to '{json_output_file}'")

simple_converter.py

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+import csv
+import json
+
+def convert_csv_to_json_simple(csv_file_path, json_file_path):
+    """
+    Converts a CSV file to a JSON file, keeping the problem description as a single text block.
+    """
+    problems = []
+    with open(csv_file_path, mode='r', encoding='utf-8') as csv_file:
+        csv_reader = csv.DictReader(csv_file)
+        
+        for row in csv_reader:
+            # Clean up keys from the CSV header
+            cleaned_row = {key.strip().replace(' ', '_').replace('.', ''): value for key, value in row.items()}
+            
+            problem_data = {
+                's_no': int(cleaned_row.get('SNo', 0)),
+                'organization': cleaned_row.get('Organization', ''),
+                'title': cleaned_row.get('Problem_Statement_Title', ''),
+                'category': cleaned_row.get('Category', ''),
+                'ps_number': cleaned_row.get('PS_Number', ''),
+                'submitted_ideas_count': int(cleaned_row.get('Submitted_Ideas_Count', 0)),
+                'theme': cleaned_row.get('Theme', ''),
+                'problem_description': cleaned_row.get('Problem_Description', '').strip()
+            }
+            problems.append(problem_data)
+
+    with open(json_file_path, mode='w', encoding='utf-8') as json_file:
+        json.dump({"problems": problems}, json_file, indent=4)
+
+if __name__ == '__main__':
+    csv_input_file = 'SIH_Problem_Statements.csv'
+    json_output_file = 'SIH_Problem_Statements.json'
+    
+    convert_csv_to_json_simple(csv_input_file, json_output_file)
+    
+    print(f"Successfully converted '{csv_input_file}' to '{json_output_file}'")