prompt.py

import datetime
import traceback
def keyword_prompt(video_info, summarization):
    keyword_prompt = f"""
    You are given a summary of a YouTube video.
    Your task is to identify the **main subject (person, company, or concept)** that the video is about.
    Only return a **single keyword** (preferably a named entity such as a person, brand, or organization).
        
    Video Info:
    {video_info}

    Video Summary:
    {summarization}

    Return only one keyword that best represents the **main focus** of the video content.
    """
    return keyword_prompt

def analysis_prompt(video_info, summarization, news, comments_text):
    analysis_prompt = f"""
    Analyze YouTube video information, summary, comments, and related latest news to create a Markdown format report.

    Video Info: {video_info}

    Video Summary:
    {summarization}

    Latest News:
    {news}

    Comments:
    {comments_text}

    Please write in the following format:

    # 🎬 YouTube Video Analysis Report

    ## 📌 Key Keywords
    `keyword` 

    ## 🎯 Video Overview
    [Summary of main video content]

    ## 💬 Comment Sentiment Analysis

    ### 📊 Sentiment Distribution
    - **Positive**: X%
    - **Negative**: Y%  
    - **Neutral**: Z%

    ### 🔍 Key Comment Insights
    1. **Positive Reactions**: [Summary of main positive comments]
    2. **Negative Reactions**: [Summary of main negative comments]
    3. **Core Issues**: [Main topics found in comments]

    ### 🔍 Comments
    1. Positive Comments: [Positive comments with sentiment classification and reasoning]
    2. Negative Comments: [Negative comments with sentiment classification and reasoning]
    3. Neutral Comments: [Neutral comments with sentiment classification and reasoning]

    ## 📰 Latest News Relevance
    [Analysis of correlation between news and video/comments]

    ## 💡 Key Insights
    1. [First major finding]
    2. [Second major finding]
    3. [Third major finding]

    # ## 🎯 Business Intelligence

    # ### Opportunity Factors
    # - [Business opportunity 1]
    # - [Business opportunity 2]

    # ### Risk Factors
    # - [Potential risk 1]
    # - [Potential risk 2]

    # ## 📈 Recommended Actions
    # 1. **Immediate Actions**: [Actions needed within 24 hours]
    # 2. **Short-term Strategy**: [Execution plan within 1 week]
    # 3. **Long-term Strategy**: [Long-term plan over 1 month]
    ---
    **Analysis Completed**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
    """

    return analysis_prompt

def analysis_prompt(video_info, summarization, news, comments_text):
    analysis_prompt = f"""
    Analyze YouTube video information, summary, comments, and related latest news to create a Markdown format report.

    Video Info: {video_info}

    Video Summary:
    {summarization}

    Latest News:
    {news}

    Comments:
    {comments_text}

    Please write in the following format:

    # 🎬 YouTube Video Analysis Report

    ## 📌 Key Keywords
    `keyword` 

    ## 🎯 Video Overview
    [Summary of main video content]

    ## 💬 Comment Sentiment Analysis

    ### 📊 Sentiment Distribution
    - **Positive**: X%
    - **Negative**: Y%  
    - **Neutral**: Z%

    ### 🔍 Key Comment Insights
    1. **Positive Reactions**: [Summary of main positive comments]
    2. **Negative Reactions**: [Summary of main negative comments]
    3. **Core Issues**: [Main topics found in comments]

    ### 🔍 Comments
    1. Positive Comments: [Positive comments with sentiment classification and reasoning]
    2. Negative Comments: [Negative comments with sentiment classification and reasoning]
    3. Neutral Comments: [Neutral comments with sentiment classification and reasoning]

    ## 📰 Latest News Relevance
    [Analysis of correlation between news and video/comments]

    ## 💡 Key Insights
    1. [First major finding]
    2. [Second major finding]
    3. [Third major finding]

    # ## 🎯 Business Intelligence

    # ### Opportunity Factors
    # - [Business opportunity 1]
    # - [Business opportunity 2]

    # ### Risk Factors
    # - [Potential risk 1]
    # - [Potential risk 2]

    # ## 📈 Recommended Actions
    # 1. **Immediate Actions**: [Actions needed within 24 hours]
    # 2. **Short-term Strategy**: [Execution plan within 1 week]
    # 3. **Long-term Strategy**: [Long-term plan over 1 month]
    ---
    **Analysis Completed**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
    """

    return analysis_prompt



def error_message(video_id):
    error_msg = f"""
    # ❌ Analysis Failed

    **Error Message:** {str(e)}

    **Debug Information:**
    - Video ID: {video_id}
    - Time: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

    **Check Items:**
    1. Verify YouTube Video ID is correct
    2. Verify API key is valid  
    3. Check network connection

    **Detailed Error:**
    ```
    {traceback.format_exc()}
    ```
    """
    return error_msg


def analysis_prompt2(content_type, all_comments ):
    analysis_prompt = f"""
Please analyze the sentiment of the following {content_type} comments in detail:

{all_comments}

Please write detailed analysis results in the following format:

### 📊 Sentiment Distribution
- **Positive**: X% (specific numbers)
- **Negative**: Y% (specific numbers)  
- **Neutral**: Z% (specific numbers)

### 🔍 Sentiment-based Comment Analysis

#### 😊 Positive Comments
**Representative Comment Examples:**
- "Actual comment 1" → Reason for positive classification
- "Actual comment 2" → Reason for positive classification
- "Actual comment 3" → Reason for positive classification

**Main Positive Keywords:** keyword1, keyword2, keyword3

#### 😡 Negative Comments  
**Representative Comment Examples:**
- "Actual comment 1" → Reason for negative classification
- "Actual comment 2" → Reason for negative classification
- "Actual comment 3" → Reason for negative classification

**Main Negative Keywords:** keyword1, keyword2, keyword3

#### 😐 Neutral Comments
**Representative Comment Examples:**
- "Actual comment 1" → Reason for neutral classification
- "Actual comment 2" → Reason for neutral classification

**Main Neutral Keywords:** keyword1, keyword2, keyword3

### 💡 Key Insights
1. **Sentiment Trends**: [Overall sentiment trend analysis]
2. **Main Topics**: [Most mentioned issues in comments]
3. **Viewer Reactions**: [Main interests or reactions of viewers]

### 📈 Summary
**One-line Summary:** [Summarize overall comment sentiment and main content in one sentence]"""    
    return analysis_prompt



def channel_markdown_result(videos, total_video_views, avg_video_views, videos_text, shorts, total_shorts_views, avg_shorts_views, shorts_text, video_sentiment, shorts_sentiment):
    markdown_result = f"""# 📊 YouTube Channel Analysis Report

## 🎬 Latest Regular Videos ({len(videos)} videos)
**Total Views**: {total_video_views:,} | **Average Views**: {avg_video_views:,.0f}

{videos_text}

---

## 🎯 Latest Shorts ({len(shorts)} videos)  
**Total Views**: {total_shorts_views:,} | **Average Views**: {avg_shorts_views:,.0f}

{shorts_text}

---

## 💬 Comment Sentiment Analysis

### 📺 Regular Video Comment Reactions
{video_sentiment}

### 📱 Shorts Comment Reactions  
{shorts_sentiment}

---

## 💡 Key Insights
- **Regular Video Average**: {avg_video_views:,.0f} views
- **Shorts Average**: {avg_shorts_views:,.0f} views
- **Performance Comparison**: {"Regular videos perform better" if avg_video_views > avg_shorts_views else "Shorts perform better" if avg_shorts_views > avg_video_views else "Similar performance"}

---
**Analysis Completed**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
"""
    return markdown_result