gemini

ai_model.py

@@ -0,0 +1,23 @@
+import logging
+import json
+import time
+import io
+import os
+import re
+import requests
+import random
+import hashlib
+from datetime import datetime
+
+from openai import OpenAI
+
+from google.auth.transport.requests import Request
+from google.oauth2.service_account import Credentials
+from google import auth
+from google.cloud import bigquery
+from google.cloud import storage
+
+from google.cloud import aiplatform
+from vertexai.preview.generative_models import GenerativeModel
+from google.oauth2.service_account import Credentials
+

app.py

@@ -4,17 +4,39 @@ import pandas as pd
 from openai import OpenAI
 import json
 
+
+from google.cloud import aiplatform
+from vertexai.preview.generative_models import GenerativeModel
+from google.oauth2.service_account import Credentials
+
+
 # 設置 OpenAI API 客戶端
 IS_ENV_LOCAL = os.getenv("IS_ENV_LOCAL", False)
-
 if IS_ENV_LOCAL:
     local_json = json.load(open("local.json"))
     openai_api_key = local_json["OPENAI_API_KEY"]
+    GOOGLE_SERVICE_ACCOUNT_INFO = local_json["GBQ_TOKEN"]
+    google_service_account_info_dict = GOOGLE_SERVICE_ACCOUNT_INFO
 else:
     openai_api_key = os.getenv("OPENAI_API_KEY")
+    GOOGLE_SERVICE_ACCOUNT_INFO = os.getenv("GBQ_TOKEN")
+    google_service_account_info_dict = json.loads(GOOGLE_SERVICE_ACCOUNT_INFO)
 
+# OPENAI
+OPENAI_CLIENT = OpenAI(api_key=openai_api_key)
+
+# GOOGLE
+GOOGPE_SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]
+google_creds = Credentials.from_service_account_info(
+    google_service_account_info_dict, scopes=GOOGPE_SCOPES
+)
+aiplatform.init(
+    project="junyiacademy",
+    service_account=google_service_account_info_dict,
+    credentials=google_creds,
+)
+GEMINI_MODEL = GenerativeModel("gemini-pro")
 
-openai_client = OpenAI(api_key=openai_api_key)
 
 def extract_article_from_content(article_text):
     start_markers = ["新文章：", "New Article:", "Here it is:"]
@@ -70,6 +92,8 @@ def generate_new_article(lesson_words, original_article, original_word_count, ba
         print(f"Attempt {attempt} to generate new article")
         print("===========================================")
 
+        system_prompt = "You are a creative writer specialized in Chinese Children book. You will help me write Chinese Articles."
+
         prompt = f"""
             Please write a new and original Chinese article tailored for first-grade students. Here's a summary of the key points that you should follow:
 
@@ -88,16 +112,23 @@ def generate_new_article(lesson_words, original_article, original_word_count, ba
             "Original Article" for Reference: The example provided is {original_article}。This article serves as a model for the spirit, style, and rhythmic pattern to be emulated.
         """
         
-        response = openai_client.chat.completions.create(
-            model=model_name,
-            messages=[
-                {"role": "system", "content": "You are a creative writer specialized in Chinese Children book. You will help me write Chinese Articles."},
-                {"role": "user", "content": prompt}
-            ],
-            max_tokens=1000
-        )
-
-        generated_text = response.choices[0].message.content.strip()
+
+        if model_name in ["gpt-4-0125-preview", "gpt-4", "gpt-3.5-turbo"]:
+            response = OPENAI_CLIENT.chat.completions.create(
+                model=model_name,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt}
+                ],
+                max_tokens=1000
+            )
+            generated_text = response.choices[0].message.content.strip()
+        elif model_name == "gemini-pro":
+            model_response = GEMINI_MODEL.generate_content(
+                f"{system_prompt}, {prompt}"
+            )
+            generated_text = model_response.candidates[0].content.parts[0].text
+
         generated_article = extract_article_from_content(generated_text)
 
         validate_article_result = validate_article(generated_article, lesson_words, base_chars, original_word_count)
@@ -181,8 +212,8 @@ with gr.Blocks() as demo:
         base_chars_input = gr.Textbox(label="Base Characters")
     
     with gr.Row():
+        model_list = ["gpt-4-0125-preview", "gpt-3.5-turbo", "gpt-4", "gemini-pro"]
         with gr.Column():
-            model_list = ["gpt-4-0125-preview", "gpt-3.5-turbo", "gpt-4"]
             model_1 = gr.Dropdown(label="Model 1", choices=model_list, value="gpt-4-0125-preview")
             generate_button1 = gr.Button("Generate Article - gpt-4-0125-preview")
         with gr.Column(): 
@@ -206,6 +237,14 @@ with gr.Blocks() as demo:
         with gr.Column():
             # validate_article_result_3 Json format
             validate_article_result_3 = gr.JSON()
+    with gr.Row():
+        with gr.Column():
+            model_4 = gr.Dropdown(label="Model 4", choices=model_list, value="gemini-pro")
+            generate_button4 = gr.Button("Generate Article - gemini-pro")
+        with gr.Column():
+            output_text4 = gr.Textbox(label="Generated Article - gemini-pro")
+        with gr.Column():
+            validate_article_result_4 = gr.JSON()
 
     generate_button1.click(
         generate_new_article,
@@ -222,6 +261,11 @@ with gr.Blocks() as demo:
         inputs=[lesson_words_input, original_article_input, original_word_count_input, base_chars_input, model_3],
         outputs=[output_text3, validate_article_result_3]
     )
+    generate_button4.click(
+        generate_new_article,
+        inputs=[lesson_words_input, original_article_input, original_word_count_input, base_chars_input, model_4],
+        outputs=[output_text4, validate_article_result_4]
+    )
 
     # 為其他模型添加點擊事件
     lesson_csv_file_input.change(

requirements.txt

@@ -1,3 +1,9 @@
 gradio
 pandas
 openai>=1.0.0
+
+google-cloud
+google-cloud-aiplatform
+google-cloud-bigquery
+google-cloud-bigquery[pandas]
+google-cloud-storage