Youtube video finder using geminillm

Youtube_video_finder_using_geminillm/README.md

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+# YouTube Relevance Finder with Gemini AI
+
+This Python script searches YouTube for recent videos based on a user query and ranks them by relevance using Google's Gemini AI model and Youtube API. It filters results by duration and recency, scores video titles for relevance, and returns the top-ranked videos.
+
+## 🔍 Features
+
+- Searches YouTube for videos from the past 14 days using Youtube API which is publicly available.
+- Filters videos by duration (4–20 minutes)
+- Uses Gemini AI to score title relevance to a query
+- Prints the top relevant video links with scores and metadata
+
+## 🛠️ Setup
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/yourusername/your-repo-name.git
+   cd your-repo-name
+````
+
+2. **Install dependencies**:
+
+   ```bash
+   pip install google-api-python-client google-generativeai
+   ```
+
+3. **Set up environment variables**:
+   Create a `.env` file or export in terminal:
+
+   ```bash
+   export YT_API_KEY=your_youtube_api_key
+   export GEMINI_API_KEY=your_gemini_api_key
+   ```
+
+## 🚀 Usage
+
+Run the script:
+
+```bash
+python your_script_name.py
+```
+
+You'll be prompted to enter a search query. The script will then display a list of the top relevant YouTube videos based on that query.
+
+## 📄 Example Output
+
+```
+1. 
+ • Title: Learn Python in 10 Minutes
+ • URL: https://youtu.be/xyz123
+ • Score: 9.2
+ • Duration: 10m30s
+ • Published: 2025-05-01T12:34:56Z
+```
+
+## 📌 Notes
+
+* Make sure you have valid API keys for both YouTube Data API v3 and Google Gemini.
+* The script currently uses the `gemini-1.5-flash-latest` model.
+

Youtube_video_finder_using_geminillm/app.py

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+#!/usr/bin/env python3
+
+import os
+import re
+from typing import Dict
+from datetime import datetime, timedelta, timezone
+from googleapiclient.discovery import build
+import google.generativeai as genai
+
+
+# ——— ENV variables ———
+YT_API_KEY = os.environ.get('YT_API_KEY')
+GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY')
+
+# ——— CONSTANTS ———
+SERVICE_TYPE = 'youtube'
+SERVICE_VERSION = 'v3'
+MODEL_NAME = 'gemini-1.5-flash-latest'
+
+DEFAULT_MAX_API_CALLS = 5
+DEFAULT_MAX_RESULTS_PER_PAGE = 50
+DEFAULT_MAX_RESULTS = 20
+DEFAULT_MIN_VIDEO_DURATION_MINUTES = 10
+DEFAULT_MAX_VIDEO_DURATION_MINUTES = 60
+DEFAULT_NO_OF_PREV_DAYS = 14
+
+DEFAULT_MAX_RESULTS = 5
+REGEX_PATTERN = r'\b(10|[1-9](\.\d+)?|0?\.\d+)\b'
+DEFAULT_SCORE = 5.0
+
+
+class TimeUtils:
+    @staticmethod
+    def get_timestamp_n_days_from_now(days: int) -> str:
+        """
+        Get the timestamp for a date n days ago in ISO 8601 format.
+        """
+        date_before_n_days = datetime.now(timezone.utc) - timedelta(days=days)
+        formatted_date = date_before_n_days \
+            .isoformat('T') \
+            .replace('+00:00', 'Z')
+        return formatted_date
+
+    @staticmethod
+    def is_duration_in_mins(duration: str) -> bool:
+        """
+        Check if the duration is in minutes.
+        """
+        return 'H' in duration or 'M' not in duration
+
+    @staticmethod
+    def derive_total_seconds_from_duration(duration: str) -> int:
+        """
+        Derive total seconds from duration (ISO 8601 format, e.g. "PT5M30S").
+        """
+        parts = duration.split('M')
+        mins = int(parts[0])
+        secs = parts[1].replace('S', '') if len(parts) > 1 else '0'
+        seconds = int(secs) if secs else 0
+        total_seconds = mins * 60 + seconds
+        return total_seconds
+
+    @staticmethod
+    def is_video_duration_in_range(
+            total_seconds: int,
+            *,
+            min_duration: int = DEFAULT_MIN_VIDEO_DURATION_MINUTES,
+            max_duration: int = DEFAULT_MAX_VIDEO_DURATION_MINUTES) -> bool:
+        """
+        Check if the video duration is within the specified range in minutes.
+        """
+        return min_duration * 60 <= total_seconds <= max_duration * 60
+
+
+class VideoDetailsExtractor:
+    """
+    A class to encapsulate YouTube video extraction logic.
+    This class can be extended or modified for more complex behaviors.
+    """
+
+    __platform_conn = build(
+        serviceName=SERVICE_TYPE,
+        version=SERVICE_VERSION,
+        developerKey=YT_API_KEY
+    )
+
+    def __init__(
+            self,
+            query: str,
+            *,
+            no_prev_days: int = DEFAULT_NO_OF_PREV_DAYS,
+            max_pages: int = DEFAULT_MAX_API_CALLS,
+            max_results: int = DEFAULT_MAX_RESULTS) -> None:
+        """
+        Initialize the VideoDetailsExtractor.
+        """
+        self.__filtered_videos = []
+        self.__next_page_token = None
+        self.__page_count = 0
+        self.__max_pages = max_pages
+
+        self.query = query
+        self.__targeted_date = TimeUtils \
+            .get_timestamp_n_days_from_now(no_prev_days)
+        self.__search_response = self.get_new_search_response()
+        self.__max_results = max_results
+
+        self.scan_videos()
+
+    def get_new_search_response(self) -> dict:
+        """
+        Fetch a new search response for the given query.
+        """
+        search_config = {
+            "q": self.query,
+            "part": "id,snippet",
+            "type": "video",
+            "order": "relevance",
+            "publishedAfter": self.__targeted_date,
+            "maxResults": DEFAULT_MAX_RESULTS_PER_PAGE,
+            "pageToken": self.__next_page_token
+        }
+
+        new_search_response = VideoDetailsExtractor.__platform_conn \
+            .search() \
+            .list(**search_config) \
+            .execute()
+
+        self.__page_count += 1
+
+        return new_search_response
+
+    def get_video_ids_from_search_response(self) -> list:
+        """
+        Extract video IDs from the search response.
+        """
+        items_list = self.__search_response.get('items', [])
+        return [item['id']['videoId'] for item in items_list]
+
+    def filter_videos(self) -> None:
+        """
+        Filter videos based on duration and recency.
+        This method processes the search response to filter videos that meet the criteria.
+        """
+        video_ids = self.get_video_ids_from_search_response()
+
+        if not video_ids:
+            print("No video IDs found in the search response.")
+            return
+
+        details_config = {
+            "part": "contentDetails,snippet",
+            "id": ",".join(video_ids)
+        }
+
+        details = VideoDetailsExtractor.__platform_conn \
+            .videos() \
+            .list(**details_config) \
+            .execute()
+
+        for item in details.get('items', []):
+            try:
+                duration = item['contentDetails']['duration'].replace('PT', '')
+
+                if TimeUtils.is_duration_in_mins(duration):
+                    continue
+
+                total_seconds = TimeUtils \
+                    .derive_total_seconds_from_duration(duration)
+
+                if TimeUtils.is_video_duration_in_range(total_seconds):
+                    video_details = {
+                        'id': item['id'],
+                        'title': item['snippet']['title'],
+                        'duration': total_seconds,
+                        'publishedAt': item['snippet']['publishedAt']
+                    }
+                    self.__filtered_videos.append(video_details)
+
+                    if len(self.__filtered_videos) >= DEFAULT_MAX_RESULTS:
+                        break
+
+            except Exception as e:
+                print(f"Error processing video {item.get('id', 'N/A')}: {e}")
+                continue
+
+        print(
+            f"Filtered {len(self.__filtered_videos)} videos based on criteria.")
+
+    def has_filtered_videos_reached_limit(self) -> bool:
+        """
+        Check if the maximum number of filtered videos has been reached.
+        """
+        return len(self.__filtered_videos) < self.__max_results
+
+    def has_page_token_reached_limit(self) -> bool:
+        """
+        Check if the maximum number of API calls has been reached.
+        """
+        return self.__page_count >= self.__max_pages
+
+    def update_next_page_token(self) -> None:
+        """
+        Update the next page token based on the search response.
+        """
+        self.__next_page_token = \
+            self.__search_response.get('nextPageToken', None)
+
+    def scan_videos(self) -> None:
+        """
+        Scan for videos that meet the specified criteria.
+        This method keeps searching until it finds enough videos that meet the criteria
+        or exhausts the search results.
+        """
+        while self.has_filtered_videos_reached_limit() and self.has_page_token_reached_limit():
+            self.__search_response = self.get_new_search_response()
+            self.filter_videos()
+            self.update_next_page_token()
+            if not self.__next_page_token:
+                break
+
+    def get_video_details(self) -> list:
+        """
+        Fetch video details for a list of filtered video based that were previously computed.
+        """
+        if not self.__filtered_videos:
+            print('No suitable videos found after applying filters.')
+        return self.__filtered_videos
+
+
+class GenModel:
+    """
+    A class to encapsulate the Gemini model for scoring video titles.
+    This class can be extended or modified for more complex behaviors.
+    """
+    _model = None
+
+    @classmethod
+    def _initialize_model(cls):
+        """
+        Initialize the Gemini model if it hasn't been initialized yet.
+        """
+        if cls._model is None:
+            genai.configure(api_key=GEMINI_API_KEY)
+            cls._model = genai.GenerativeModel(MODEL_NAME)
+
+    @staticmethod
+    def get_prompt_for_title(title: str, query: str) -> str:
+        """
+        Generate a prompt for the Gemini model to score the title based on the query.
+        """
+        return (
+            f"Query: {query}\n"
+            f"Title: {title}\n"
+            "Rate relevance & quality 1–10 (just give the number)."
+        )
+
+    @classmethod
+    def get_score_for_title(cls, title: str, query: str) -> float:
+        """
+        Get the score for a video title based on the query using the Gemini model.
+        If the model is not initialized, it will initialize it first.
+        If the score cannot be parsed, it returns a default score.
+        """
+        cls._initialize_model()
+        prompt = cls.get_prompt_for_title(title, query)
+        try:
+            response = cls._model.generate_content(prompt)
+            score_text = response.text.strip()
+            match = re.search(REGEX_PATTERN, score_text)
+            return float(match.group()) if match else DEFAULT_SCORE
+        except (ValueError, AttributeError) as e:
+            print(f"[Error] Failed to parse score for '{title}': {e}")
+            return DEFAULT_SCORE
+        except Exception as e:
+            print(f"[Error] API call failed for '{title}': {e}")
+            return DEFAULT_SCORE
+
+
+class VideoProcessor:
+    """
+    A class to process video details and rank them based on a scoring model.
+    This class can be extended or modified for more complex behaviors.
+    """
+    def __init__(self, scorer=GenModel):
+        """
+        Initialize the VideoProcessor with a scoring model.
+        """
+        self.scorer = scorer
+
+    def find_and_rank_videos(self, query: str, num_results: int = DEFAULT_MAX_RESULTS):
+        """
+        Find and rank videos based on the query.
+        This method uses the VideoDetailsExtractor to find videos and the scoring model to rank them.
+        """
+        videos = VideoDetailsExtractor(query).get_video_details()
+        if not videos:
+            return []
+
+        for video in videos:
+            video['score'] = self.scorer.get_score_for_title(
+                video['title'], query)
+
+        return sorted(videos, key=lambda v: v['score'], reverse=True)[:num_results]
+
+
+if __name__ == '__main__':
+    required_env_vars = ['YT_API_KEY', 'GEMINI_API_KEY']
+
+    if any([env_var not in os.environ for env_var in required_env_vars]):
+        raise KeyError(
+            "Error: YouTube and/or Gemini API keys not set in environment variables.")
+
+    user_query = input("Enter your search: ")
+
+    video_processor = VideoProcessor()
+    pick_best = video_processor.find_and_rank_videos(user_query)
+