fixed gemini client

ai_generate.py

@@ -3,6 +3,10 @@ from openai import OpenAI
 import os
 from transformers import pipeline
 from groq import Groq
+import base64
+import vertexai
+from vertexai.generative_models import GenerativeModel, Part, FinishReason
+import vertexai.preview.generative_models as generative_models
 import google.generativeai as genai
 import anthropic
 from langchain_community.document_loaders import PyMuPDFLoader
@@ -31,9 +35,8 @@ openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
 # give access to all APIs for GCP instance
 # gcloud auth application-default login
 genai.configure(api_key=os.environ.get("GENAI_API_KEY"))
-gemini_client = genai.GenerativeModel(
-    model_name=f"models/gemini-1.5-pro", system_instruction="You are helpful assistant."
-)
+vertexai.init(project="proprietary-info-detection", location="us-central1")
+gemini_client = GenerativeModel("gemini-1.5-pro-001")
 claude_client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
 
 
@@ -109,14 +112,24 @@ def generate_openai(text, model, openai_client):
 
 
 def generate_gemini(text, model, gemini_client):
+    safety_settings = {
+        generative_models.HarmCategory.HARM_CATEGORY_HATE_SPEECH: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+        generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+        generative_models.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+        generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+    }
+    generation_config = {
+        "max_output_tokens": 1024,
+        "temperature": 1.0,
+        "top_p": 1.0,
+    }
     response = gemini_client.generate_content(
-        text,
-        generation_config={
-            "max_output_tokens": 1024,
-            "temperature": 1,
-        },
+        [text],
+        generation_config=generation_config,
+        safety_settings=safety_settings,
+        stream=False,
     )
-    return response.text.strip()
+    return response.text
 
 
 def generate_claude(text, model, claude_client):

app.py

@@ -1,3 +1,6 @@
+"""
+nohup python3 app.py &
+"""
 import openai
 import gradio as gr
 from typing import Dict, List