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import os
import json
from PIL import Image
import google.generativeai as genai
# working directory path
working_dir = os.path.dirname(os.path.abspath(__file__))
# path of config_data file
config_file_path = f"{working_dir}/Config.json"
with open(config_file_path, "r") as f:
config_data = json.load(f)
# config_data = json.load(open("Config.json"))
# loading the GOOGLE_API_KEY
GOOGLE_API_KEY = config_data["GOOGLE_API_KEY"]
# configuring google.generativeai with API key
genai.configure(api_key=GOOGLE_API_KEY)
def load_gemini_pro_model():
gemini_pro_model = genai.GenerativeModel("gemini-2.0-flash")
return gemini_pro_model
# get response from Gemini-Pro-Vision model - image/text to text
def gemini_pro_vision_response(prompt, image):
gemini_pro_vision_model = genai.GenerativeModel("gemini-2.0-flash")
response = gemini_pro_vision_model.generate_content([prompt, image])
result = response.text
return result
# get response from embeddings model - text to embeddings
def embeddings_model_response(input_text):
embedding_model = "models/embedding-001"
embedding = genai.embed_content(model=embedding_model,
content=input_text,
task_type="retrieval_document")
embedding_list = embedding["embedding"]
return embedding_list
# get response from Gemini-Pro model - text to text
def gemini_pro_response(user_prompt):
gemini_pro_model = genai.GenerativeModel("gemini-2.0-flash")
response = gemini_pro_model.generate_content(user_prompt)
result = response.text
return result
# result = gemini_pro_response("What is Machine Learning")
# print(result)
# print("-"*50)
#
#
# image = Image.open("test_image.png")
# result = gemini_pro_vision_response("Write a short caption for this image", image)
# print(result)
# print("-"*50)
#
#
# result = embeddings_model_response("Machine Learning is a subset of Artificial Intelligence")
# print(result) |