import random
import pandas as pd

from ollama import Client
import json

def dummy_sentiment_analysis(content, tag):
    if tag.startswith('VT -') or tag.startswith('CT -'):
        return random.choice([-1, 0, 1]), 'random dummy sentiment'  # Random sentiment for testing

    return 'test', 'not applicable'



def ollama_sentiment_analysis(content, theme, client: Client, model) -> tuple[list[str], int, str]:
    """
    Perform sentiment analysis using Ollama model.

    Parameters:
    - content: Text content to analyze
    - tag: Tag indicating the type of sentiment analysis (e.g., 'VT - Positive')

    Returns:
    - sentiment score and reason
    """
    prompt = f"""
    # Instructions
    You are an expert in sentiment analysis and natural language processing. You are given a quote from an interview along with a theme tag. Your task is to analyze the sentiment expressed in the quote in relation to the provided theme, and provide a short explanation for your assessment (max 10 words).
    
    You need to deliver three pieces of information:
    1. A list of keywords from the quote quantify or qualify the theme, and that influenced your sentiment analysis (if any).
    2. A sentiment score: -1 for negative, 0 for neutral, and 1 for positive sentiment.
    3. A brief reason (max 10 words) explaining your sentiment score.
    

    # Guidelines    
    Keywords should be directly relevant to the theme.
    
    The reason should be extremely concise and to the point:
    - Does not need to be a full sentence.
    - Sentiment itself does not need to be stated in the explanation.
    - If keywords are present in the quote that directly capture the sentiment, give that as the reason..
    
    
    # Input
    
    Theme: `{theme}`
    
    Quote:
    ```
    {content}
    ```
    
    # Response Format
    Provide your response in the following JSON format:
    {{
        "keywords": ["<list_of_relevant_keywords_if_any>"],
        "sentiment": <sentiment_score>,
        "reason": "<brief_explanation_max_10_words>"
    }}

    
    # Examples
    
    ** Example 1**
    - Theme: `Speed`
    - Quote: `It just was a little toned down. It was almost like he was talking like this. You know? It almost kind of this was a little slow for me.`

    - Response: {{"keywords": ["slow"], "sentiment": -1, "reason": "States speed is slow, indicates dissatisfaction"}}
    
    ** Example 2**
    - Theme: `Friendliness / Empathy`
    - Quote: `Sound very welcoming`
    
    - Response: {{ "keywords": ["welcoming"], "sentiment": 1, "reason": "Uses 'welcoming'" }}
    
    """

    resp = client.generate(
        model=model,
        prompt=prompt,
    )
    
    try:
        response_text = resp.response.strip()

        # Extract JSON from response
        start_index = response_text.find('{')
        end_index = response_text.rfind('}') + 1
        json_str = response_text[start_index:end_index]
        
        response_json = json.loads(json_str)
        keywords = response_json.get('keywords', [])
        sentiment = response_json.get('sentiment', 'test')
        reason = response_json.get('reason', 'no reason provided')
        return keywords, sentiment, reason
    except Exception as e:
        print(f"Error parsing response: {e}")
        return [], None, 'parsing error'


if __name__ == "__main__":

    client = Client(
            host="http://localhost:11434"
        )

    sentiment_df = pd.DataFrame({
        'content': [
            "I love this product!",
            "This is the worst service ever.",
            "It's okay, not great but not terrible."
        ],
        'tag': [
            'VT - Personal Experience',
            'VT - Personal Experience',
            'VT - Personal Experience'
        ],
        'manual_analysis': [False, False, True]
    })

    sentiment_df[['sentiment', 'reason']] = sentiment_df[~sentiment_df['manual_analysis']].apply(
        lambda row: pd.Series(ollama_sentiment_analysis(row['content'], row['tag'], client, model='llama3.2:latest')),
        axis=1
    )

    print(sentiment_df.head())