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from api import Model
import os
def initial_prompt(list_of_articles, POSITIVE, NEGATIVE,cleaned_text):
return f"""
You are a senior data analyst with 15+ years of expertise in comparative analysis. Your task is to analyze multiple articles from the given list and generate a structured **JSON summary** that highlights key differences, patterns, and sentiment insights.
### **Input Data:**
- **Articles:** {list_of_articles}
### Complete contents of the articles:
{cleaned_text}
### **Task Requirements:**
1. **Identify Key Differences:**
- Highlight the **strongest contrasting viewpoints** or **highest topic overlap** among the articles.
2. **Generate a Structured JSON Output:**
- **Coverage Differences:** Identify variations in focus across articles.
- **Impact Analysis:** Explain how these differences affect audience perception.
3. **Perform Sentiment Analysis:**
- Generate a summary based on the sentiment distribution.
### **Sentiment Overview:**
- **Positive Mentions:** {POSITIVE}
- **Negative Mentions:** {NEGATIVE}
"""
prompt1 = """
### **Expected JSON Output Format:**
{
"Coverage Differences": [
{
"Comparison": "{Article_X} focuses on {Key_Topic_X}, while {Article_Y} highlights {Key_Topic_Y}.",
"Impact": "{Effect_of_the_different_focuses_on_audience_perception}."
},
{
"Comparison": "{Article_X} emphasizes {Aspect_X}, whereas {Article_Y} discusses {Aspect_Y}.",
"Impact": "{Potential_market_or_public_reaction}."
},
{
"Comparison": "{Article_X} presents {Perspective_X}, but {Article_Y} contrasts this with {Perspective_Y}.",
"Impact": "{Implications_of_the_conflicting_perspectives}."
}
],
"Topic Overlap": {
"Common Topics": ["{Common_Topic_1}", "{Common_Topic_2}"],
"Unique Topics in {Article_X}": ["{Unique_Topic_X_1}", "{Unique_Topic_X_2}"],
"Unique Topics in {Article_Y}": ["{Unique_Topic_Y_1}", "{Unique_Topic_Y_2}"]
},
"Final Sentiment Analysis": "{Overall_sentiment_summary (Positive/Negative/Neutral) with a brief explanation}",
"Overall_Sentiment_Ssummarizing_Report" :"Complete contents of the articles content summarizing into give clean format."
}
### **Instructions:**
- **Strictly** follow this JSON structure in your output.
- Do **not** include any additional text besides the JSON.
"""
def generate_response(list_of_articles,POSITIVE,NEGATIVE,clean_text):
prompt = initial_prompt(list_of_articles,POSITIVE,NEGATIVE,clean_text) + "\n\n"+ prompt1
answer_text= Model.OPENAI_MODEL(prompt)
start_index_square = answer_text.find('[')
start_index_curly = answer_text.find('{')
if start_index_square != -1 and (start_index_curly == -1 or start_index_square < start_index_curly):
start_index = start_index_square
end_char = ']'
elif start_index_curly != -1 and (start_index_square == -1 or start_index_curly < start_index_square):
start_index = start_index_curly
end_char = '}'
else:
return ("Error: JSON data not found.")
extracted_json = None
end_index = answer_text.rfind(end_char)
if start_index != -1 and end_index != -1:
extracted_json = answer_text[start_index:end_index + 1]
return (extracted_json)
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