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import google.generativeai as genai
from prompts.prompts import PromptGenerator
from dotenv import load_dotenv
load_dotenv
import os
api_key = os.getenv("GOOGLE_API_KEY")
class AnswerEvaluator:
def __init__(self, api_key):
"""Initialize with the Gemini API key."""
genai.configure(api_key=api_key)
self.model = genai.GenerativeModel("gemini-pro")
def evaluate_answers(self, all_answers):
"""Evaluates answers based on their relevance, accuracy, and clarity."""
prompt = self._construct_prompt(all_answers)
try:
# Evaluate the answers using Gemini's model
response = self.model.generate_content(
prompt
)
# Return the response generated by the model
return response.text.strip()
except Exception as e:
return f"Error in evaluation: {str(e)}"
def _construct_prompt(self, all_answers):
"""Constructs a detailed prompt for the Gemini model."""
prompt = PromptGenerator()
evaluation_prompt = prompt.evaluate_prompt()
for idx, item in enumerate(all_answers, 1):
evaluation_prompt += f"\n{idx}. Question: {item['question']}\n Answer: {item['answer']}\n"
evaluation_prompt += "\nProvide your detailed evaluation below:"
return evaluation_prompt
# Initialize with your Gemini API key
api_key = os.getenv("GOOGLE_API_KEY")
evaluator = AnswerEvaluator(api_key)
# Example answers
all_answers = [
{"question": "What is machine learning?", "answer": "Machine learning is a subset of AI."},
{"question": "Explain logistic regression.", "answer": "It is used to classify data points into classes."},
]
# Evaluate the answers
evaluation_result = evaluator.evaluate_answers(all_answers)
print(evaluation_result)
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