evaluate.py

import pandas as pd
import google.generativeai as genai
import os

# Load responses from a file
def load_responses(filename="interview_responses_round_1.csv"):
    try:
        return pd.read_csv(filename)
    except FileNotFoundError:
        print("Response file not found. Please ensure that 'round1.py' has been run and responses have been saved.")
        exit()

# Configure and initialize Gemini
def configure_gemini():
    api_key = os.getenv("GOOGLE_GENERATIVE_AI_API_KEY")
    if not api_key:
        print("Google Generative AI API key not set. Please set your API key as an environment variable.")
        exit()

    genai.configure(api_key=api_key)

    generation_config = {
        "temperature": 0.9,
        "top_p": 1,
        "top_k": 50,
        "max_output_tokens": 512,
    }

    safety_settings = [
        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
        {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
    ]

    return genai.GenerativeModel(model_name="gemini-1.0-pro",
                                 generation_config=generation_config,
                                 safety_settings=safety_settings)

def evaluate_responses(model, responses):
    evaluations = []
    for index, row in responses.iterrows():
        try:
            response = model.generate_content([row['response']])
            evaluations.append(response.text)
        except Exception as e:
            print(f"An error occurred while evaluating response {index+1}: {e}")
            evaluations.append("Error during evaluation.")

    return evaluations

def print_evaluations(responses, evaluations):
    for index, (response, evaluation) in enumerate(zip(responses['response'], evaluations), start=1):
        print(f"Response {index}: {response}")
        print(f"Evaluation: {evaluation}")
        print("----------")

if __name__ == "__main__":
    responses = load_responses()
    if not responses.empty:
        model = configure_gemini()
        evaluations = evaluate_responses(model, responses)
        print_evaluations(responses, evaluations)