pipeline.py

import os
import json
import time
from typing import Dict, Any, List

# In a real scenario, you would install these libraries:
# pip install google-cloud-aiplatform google-cloud-pubsub

# Mocking the library imports for demonstration purposes if not available in environment
try:
    from google.cloud import aiplatform
    import vertexai
    from vertexai.generative_models import GenerativeModel
except ImportError:
    print("Google Cloud SDK not found. This code requires 'google-cloud-aiplatform'.")
    # Define dummy classes to allow the code to be read/checked without crashing
    class GenerativeModel:
        def __init__(self, model_name): pass
        def generate_content(self, prompt): return type('obj', (object,), {'text': '{"niche": "Travel", "is_influencer": true, "contact_intent": true}'})

# Configuration
PROJECT_ID = "your-project-id"
LOCATION = "us-central1"
MODEL_NAME = "gemini-1.5-flash-001"

def init_vertex_ai():
    """Initializes the Vertex AI SDK."""
    try:
        vertexai.init(project=PROJECT_ID, location=LOCATION)
        print(f"Vertex AI initialized for project {PROJECT_ID}")
    except Exception as e:
        print(f"Failed to initialize Vertex AI: {e}")

def fetch_profile_data_via_api(user_id: str) -> Dict[str, Any]:
    """
    Simulates fetching a user profile using the official Instagram Graph API.
    
    In a production environment, this would use the `requests` library to hit:
    GET graph.facebook.com/{api-version}/{user-id}?fields=biography,media_count,followers_count&access_token={token}
    """
    print(f"Fetching data for User ID: {user_id} via Graph API...")
    
    # Mock response data representing a legitimate API payload
    # Dynamic data based on user_id for demonstration
    mock_response = {
        "id": user_id,
        "username": f"user_{user_id}_travels",
        "biography": "Exploring the world one city at a time. ✈️ Photographer | Blogger. Contact for collabs.",
        "media_count": 450 + int(user_id[-2:]),
        "followers_count": 12000 + (int(user_id[-1]) * 500)
    }
    
    # Simulate network latency
    time.sleep(0.5)
    return mock_response

def analyze_profile_with_vertex(profile_data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Uses Vertex AI (Gemini) to categorize the profile based on the biography.
    """
    print(f"Sending profile {profile_data['username']} to Vertex AI for analysis...")
    
    model = GenerativeModel(MODEL_NAME)
    
    # Construct a prompt for the AI
    prompt = 
    """
    Analyze the following Instagram profile data and output a valid JSON object (no markdown) with:
    1. 'niche': The likely niche of the user (e.g., Travel, Tech, Food).
    2. 'is_influencer': Boolean, based on follower count > 10000.
    3. 'contact_intent': Boolean, if they are open to business/collabs.

    Profile Data:
    Username: {profile_data['username']}
    Bio: {profile_data['biography']}
    Followers: {profile_data['followers_count']}
    """

    try:
        # Generate response
        response = model.generate_content(prompt)
        
        # Clean response text to ensure valid JSON parsing (strip markdown backticks)
        raw_text = response.text.strip()
        if raw_text.startswith("```json"):
            raw_text = raw_text[7:]
        if raw_text.startswith("```"):
            raw_text = raw_text[3:]
        if raw_text.endswith("```"):
            raw_text = raw_text[:-3]

        # Parse the cleaned string into a real dictionary
        analysis = json.loads(raw_text)
        
        # Merge the AI insights with the original data
        enriched_data = {
            **profile_data,
            "ai_analysis": analysis
        }
        return enriched_data

    except json.JSONDecodeError:
        print(f"Failed to parse JSON for {profile_data['username']}")
        return {**profile_data, "error": "AI response was not valid JSON"}
    except Exception as e:
        print(f"Error during Vertex AI analysis: {e}")
        return profile_data

def run_pipeline(user_ids: List[str]):
    """
    Orchestrates the pipeline: Ingest -> Process -> Output.
    """
    init_vertex_ai()
    
    processed_profiles = []
    
    for uid in user_ids:
        # Step 1: Ingest (Compliance: Using official API)
        raw_data = fetch_profile_data_via_api(uid)
        
        # Step 2: Process (Vertex AI)
        enriched_data = analyze_profile_with_vertex(raw_data)
        
        processed_profiles.append(enriched_data)
        print(f"Successfully processed {raw_data['username']}\n")

    # Step 3: Storage (Mocking database insertion)
    print("--- Pipeline Summary ---")
    print(json.dumps(processed_profiles, indent=2))
    print("Data ready for BigQuery or Firestore insertion.")

if __name__ == "__main__":
    # Example User IDs to process
    target_users = ["17841400000000001", "17841400000000002"]
    run_pipeline(target_users)