export GEMINI_API_KEY="AIz...YahanApniKeyPasteKaro...xyz"

set GEMINI_API_KEY="AIz...YahanApniKeyPasteKaro...xyz"

# ai/gemini.py

import requests  # API calls ke liye
import os        # Environment variables parhne ke liye
import time      # Rate limiting ke liye (rukne ke liye)
import json      # Data ko handle karne ke liye (abhi use nahi ho raha, but good practice)

# Step 1: Configuration
GEMINI_KEY = os.environ.get('GEMINI_API_KEY', '')
MODEL = 'gemini-1.5-flash-latest' # Google recommends using the 'latest' tag
BASE_URL = 'https://generativelanguage.googleapis.com/v1beta'
_last_call = 0 # Track karta hai ke aakhri call kab ki thi

def gemini_call(prompt, module='unknown'):
    """Tier 1: Free AI call via Gemini Flash."""
    global _last_call
    
    # Step 2: API Key Check & Fallback Trigger
    if not GEMINI_KEY:
        print('[GEMINI] No API key — falling back to Haiku')
        return None # None return karne ka matlab hai: "Mujhse nahi ho paya, agle tier ko try karo"
    
    # Step 3: Rate Limiting - Sabr ka Phal Meetha
    # Google 15 RPM deta hai. 60 seconds / 15 = 4 seconds per call.
    elapsed = time.time() - _last_call
    if elapsed < 4:
        # Agar pichli call ko 4 second se kam hue hain, toh baaki time wait karo
        time.sleep(4 - elapsed)
    
    # Step 4: API Request Tayyar Karna
    url = f'{BASE_URL}/models/{MODEL}:generateContent?key={GEMINI_KEY}'
    body = {
        'contents': [{'parts': [{'text': prompt}]}],
        'generationConfig': {
            'temperature': 0.1,      # Kam temperature = more predictable, less "creative" output. Analysis ke liye best.
            'maxOutputTokens': 1000  # Response kitna lamba ho sakta hai
        }
    }
    
    # Step 5: Asal API Call aur Error Handling
    try:
        r = requests.post(url, json=body, timeout=30) # 30 second ka timeout, warna phans jayega
        _last_call = time.time() # Call ke foran baad time note karlo
        
        if r.status_code == 200:
            # Sab theek raha
            data = r.json()
            # Yeh response structure Google ki documentation se milta hai
            return data['candidates'][0]['content']['parts'][0]['text']
        
        # Agar koi aur status code aaya (e.g., 429 Too Many Requests)
        print(f'[GEMINI] Error {r.status_code}: {r.text}')
        return None # Phir se fallback
        
    except requests.exceptions.RequestException as e:
        # Network error, timeout, etc.
        print(f'[GEMINI] Failed: {e}')

Tier 1 filters provide initial screening cost-effectively, reducing computational load on premium models.

---

## 📺 Recommended Videos & Resources
- **[Google Gemini API Documentation](https://ai.google.dev/)** — Gemini models and API reference
  - Type: Official Documentation
  - Link description: Learn Gemini Flash, Pro, and rate limits
- **[Free API Keys & Rate Limits](https://www.youtube.com/results?search_query=google+gemini+free+api+key+rate+limits)** — Understanding free tier constraints
  - Type: YouTube
  - Link description: Search "Gemini API free tier rate limits"
- **[Sentiment Analysis with LLMs](https://en.wikipedia.org/wiki/Sentiment_analysis)** — News and text analysis
  - Type: Wikipedia
  - Link description: Learn sentiment analysis for market context
- **[Real-Time News APIs](https://newsapi.org/)** — Accessing market news data
  - Type: Official Website
  - Link description: NewsAPI provides free market headlines
- **[Pattern Recognition in Time Series](https://www.youtube.com/results?search_query=pattern+recognition+time+series+python)** — Identifying market patterns
  - Type: YouTube
  - Link description: Search "pattern recognition trading Python"

---

## 🎯 Mini-Challenge
**5-Minute Practical Task:** Create a function using Gemini Flash (free) that takes a market title and recent news headlines, then returns a JSON with keys: "proceed" (boolean), "reason" (string), and "confidence" (0-1 float). Test with 3 different market scenarios and verify JSON parsing works correctly.

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## 🖼️ Visual Reference

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