File size: 13,638 Bytes
5e5e890
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
import os
import time
import json
import requests
from typing import Dict, Any
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

class ScraperAgent:
    """Agent responsible for extracting data from LinkedIn profiles using Apify REST API"""
    
    def __init__(self):
        self.apify_token = os.getenv('APIFY_API_TOKEN')
        if not self.apify_token:
            raise ValueError("APIFY_API_TOKEN not found in environment variables")
        
        # Validate token format
        if not self.apify_token.startswith('apify_api_'):
            print(f"⚠️ Warning: Token doesn't start with 'apify_api_'. Current token starts with: {self.apify_token[:10]}...")
        
        # Use the new actor API endpoint
        self.api_url = f"https://api.apify.com/v2/acts/dev_fusion~linkedin-profile-scraper/run-sync-get-dataset-items?token={self.apify_token}"
        
        print(f"πŸ”‘ Using Apify token: {self.apify_token[:15]}...")  # Show first 15 chars for debugging
    
    def extract_profile_data(self, linkedin_url: str) -> Dict[str, Any]:
        """
        Extract profile data from LinkedIn URL using Apify REST API
        
        Args:
            linkedin_url (str): LinkedIn profile URL
            
        Returns:
            Dict[str, Any]: Extracted profile data
        """
        try:
            print(f"πŸ” Starting scraping for: {linkedin_url}")
            print(f"πŸ”— URL being processed: {linkedin_url}")
            print(f"⏰ Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}")
            
            # Clean and validate URL
            original_url = linkedin_url
            linkedin_url = linkedin_url.strip()
            if not linkedin_url.startswith('http'):
                linkedin_url = 'https://' + linkedin_url
            
            print(f"🧹 Cleaned URL: {linkedin_url}")
            
            # Verify URL consistency
            if original_url != linkedin_url:
                print(f"πŸ”„ URL normalized: {original_url} β†’ {linkedin_url}")
            
            # Configure the run input with fresh URL
            run_input = {
                "profileUrls": [linkedin_url],  # This actor expects profileUrls, not startUrls
                "slowDown": True,  # To avoid being blocked
                "includeSkills": True,
                "includeExperience": True,
                "includeEducation": True,
                "includeRecommendations": False,  # Optional, can be slow
                "saveHtml": False,
                "saveMarkdown": False
            }
            
            print(f"πŸ“‹ Apify input: {json.dumps(run_input, indent=2)}")
            
            # Make the API request
            print("πŸš€ Running Apify scraper via REST API...")
            response = requests.post(
                self.api_url,
                json=run_input,
                headers={'Content-Type': 'application/json'},
                timeout=180  # 3 minutes timeout
            )
            
            if response.status_code in [200, 201]:  # 201 is also success for Apify
                results = response.json()
                print(f"βœ… API Response received: {len(results)} items")
                
                if results and len(results) > 0:
                    # Process the first result (since we're scraping one profile)
                    raw_data = results[0]
                    processed_data = self._process_apify_data(raw_data, linkedin_url)
                    print("βœ… Successfully extracted and processed profile data")
                    return processed_data
                else:
                    error_msg = "No data returned from Apify API. The profile may be private or the scraper encountered an issue."
                    print(f"❌ {error_msg}")
                    raise ValueError(error_msg)
            else:
                error_details = ""
                try:
                    error_response = response.json()
                    error_details = f" - {error_response.get('error', {}).get('message', response.text)}"
                except:
                    error_details = f" - {response.text}"
                
                if response.status_code == 401:
                    error_msg = f"Authentication failed (401): Invalid or expired API token{error_details}"
                    print(f"❌ {error_msg}")
                    print(f"πŸ”‘ Token being used: {self.apify_token[:15]}...")
                    print(f"πŸ’‘ Please check your APIFY_API_TOKEN in your .env file")
                elif response.status_code == 404:
                    error_msg = f"Actor not found (404): The actor 'dev_fusion~linkedin-profile-scraper' may not exist{error_details}"
                    print(f"❌ {error_msg}")
                elif response.status_code == 429:
                    error_msg = f"Rate limit exceeded (429): Too many requests{error_details}"
                    print(f"❌ {error_msg}")
                else:
                    error_msg = f"API request failed with status {response.status_code}{error_details}"
                    print(f"❌ {error_msg}")
                
                raise requests.RequestException(error_msg)
                
        except requests.Timeout:
            error_msg = "Request timed out. The scraping operation took too long to complete."
            print(f"⏰ {error_msg}")
            raise requests.Timeout(error_msg)
        except Exception as e:
            error_msg = f"Error extracting profile data: {str(e)}"
            print(f"❌ {error_msg}")
            raise Exception(error_msg)
    
    def test_apify_connection(self) -> bool:
        """Test if Apify connection is working"""
        try:
            # Test with the actor endpoint
            test_url = f"https://api.apify.com/v2/acts/dev_fusion~linkedin-profile-scraper?token={self.apify_token}"
            print(f"πŸ”— Testing connection to: {test_url[:50]}...")
            
            response = requests.get(test_url, timeout=10)
            
            if response.status_code == 200:
                actor_info = response.json()
                print(f"βœ… Successfully connected to Apify actor: {actor_info.get('name', 'LinkedIn Profile Scraper')}")
                return True
            elif response.status_code == 401:
                print(f"❌ Authentication failed (401): Invalid or expired API token")
                print(f"πŸ”‘ Token being used: {self.apify_token[:15]}...")
                print(f"πŸ’‘ Please check your APIFY_API_TOKEN in your .env file")
                return False
            elif response.status_code == 404:
                print(f"❌ Actor not found (404): The actor 'dev_fusion~linkedin-profile-scraper' may not exist or be accessible")
                return False
            else:
                print(f"❌ Failed to connect to Apify: {response.status_code} - {response.text}")
                return False
        except Exception as e:
            print(f"❌ Failed to connect to Apify: {str(e)}")
            return False
    
    def _process_apify_data(self, raw_data: Dict[str, Any], url: str) -> Dict[str, Any]:
        """Process raw Apify data into standardized format"""
        
        print(f"πŸ“Š Processing data for URL: {url}")
        print(f"πŸ“‹ Raw data keys: {list(raw_data.keys())}")
        
        # Extract basic information - using the correct field names from API
        profile_data = {
            'name': raw_data.get('fullName', ''),
            'headline': raw_data.get('headline', ''),
            'location': raw_data.get('addressWithCountry', raw_data.get('addressWithoutCountry', '')),
            'about': raw_data.get('about', ''),  # API uses 'about' not 'summary'
            'connections': raw_data.get('connections', 0),
            'followers': raw_data.get('followers', 0),
            'email': raw_data.get('email', ''),
            'url': url,  # Use the URL that was actually requested
            'profile_image': raw_data.get('profilePic', ''),
            'profile_image_hq': raw_data.get('profilePicHighQuality', ''),
            'scraped_at': time.strftime('%Y-%m-%d %H:%M:%S'),
            'job_title': raw_data.get('jobTitle', ''),
            'company_name': raw_data.get('companyName', ''),
            'company_industry': raw_data.get('companyIndustry', ''),
            'company_website': raw_data.get('companyWebsite', ''),
            'company_size': raw_data.get('companySize', ''),
            'current_job_duration': raw_data.get('currentJobDuration', ''),
            'top_skills': raw_data.get('topSkillsByEndorsements', '')
        }
        
        print(f"βœ… Extracted profile for: {profile_data.get('name', 'Unknown')}")
        print(f"πŸ”— Profile URL stored: {profile_data['url']}")
        
        # Process experience - API uses 'experiences' array
        experience_list = []
        for exp in raw_data.get('experiences', []):
            experience_item = {
                'title': exp.get('title', ''),
                'company': exp.get('subtitle', '').replace(' Β· Full-time', '').replace(' Β· Part-time', ''),
                'duration': exp.get('caption', ''),
                'description': '',  # Extract from subComponents if available
                'location': exp.get('metadata', ''),
                'company_logo': exp.get('logo', ''),
                'is_current': 'Present' in exp.get('caption', '') or 'Β·' not in exp.get('caption', '')
            }
            
            # Extract description from subComponents
            if 'subComponents' in exp and exp['subComponents']:
                for sub in exp['subComponents']:
                    if 'description' in sub and sub['description']:
                        descriptions = []
                        for desc in sub['description']:
                            if isinstance(desc, dict) and desc.get('text'):
                                descriptions.append(desc['text'])
                        experience_item['description'] = ' '.join(descriptions)
            
            experience_list.append(experience_item)
        profile_data['experience'] = experience_list
        
        # Process education - API uses 'educations' array
        education_list = []
        for edu in raw_data.get('educations', []):
            education_item = {
                'degree': edu.get('subtitle', ''),
                'school': edu.get('title', ''),
                'field': '',  # Extract from subtitle
                'year': edu.get('caption', ''),
                'logo': edu.get('logo', ''),
                'grade': ''  # Extract from subComponents if available
            }
            
            # Split degree and field from subtitle
            subtitle = edu.get('subtitle', '')
            if ' - ' in subtitle:
                parts = subtitle.split(' - ', 1)
                education_item['degree'] = parts[0]
                education_item['field'] = parts[1] if len(parts) > 1 else ''
            elif ', ' in subtitle:
                parts = subtitle.split(', ', 1)
                education_item['degree'] = parts[0]
                education_item['field'] = parts[1] if len(parts) > 1 else ''
            
            # Extract grade from subComponents
            if 'subComponents' in edu and edu['subComponents']:
                for sub in edu['subComponents']:
                    if 'description' in sub and sub['description']:
                        for desc in sub['description']:
                            if isinstance(desc, dict) and desc.get('text', '').startswith('Grade:'):
                                education_item['grade'] = desc['text']
            
            education_list.append(education_item)
        profile_data['education'] = education_list
        
        # Process skills - API uses 'skills' array with title
        skills_list = []
        for skill in raw_data.get('skills', []):
            if isinstance(skill, dict) and 'title' in skill:
                skills_list.append(skill['title'])
            elif isinstance(skill, str):
                skills_list.append(skill)
        profile_data['skills'] = skills_list
        
        # Process certifications - API uses 'licenseAndCertificates'
        certifications_list = []
        for cert in raw_data.get('licenseAndCertificates', []):
            cert_item = {
                'title': cert.get('title', ''),
                'issuer': cert.get('subtitle', ''),
                'date': cert.get('caption', ''),
                'credential_id': cert.get('metadata', ''),
                'logo': cert.get('logo', '')
            }
            certifications_list.append(cert_item)
        profile_data['certifications'] = certifications_list
        
        # Process languages (if available)
        profile_data['languages'] = raw_data.get('languages', [])
        
        # Process volunteer experience (if available)
        volunteer_list = []
        for vol in raw_data.get('volunteerAndAwards', []):
            if isinstance(vol, dict):
                volunteer_list.append(vol)
        profile_data['volunteer_experience'] = volunteer_list
        
        # Additional rich data
        profile_data['honors_awards'] = raw_data.get('honorsAndAwards', [])
        profile_data['projects'] = raw_data.get('projects', [])
        profile_data['publications'] = raw_data.get('publications', [])
        profile_data['recommendations'] = raw_data.get('recommendations', [])
        profile_data['interests'] = raw_data.get('interests', [])
        
        return profile_data