Spaces:
Running
Running
File size: 3,579 Bytes
01c75ae eca9523 01c75ae 7c4ae4d eca9523 01c75ae 7c4ae4d 01c75ae 7c4ae4d 01c75ae 7c4ae4d 918a703 01c75ae 918a703 c207609 7c4ae4d 918a703 7c4ae4d eca9523 7c4ae4d eca9523 7c4ae4d 918a703 7c4ae4d 918a703 01c75ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import os
from typing import List, Tuple
from flask import Flask, request, jsonify
from google.cloud import vertex_ai # Ensure to install the Google Cloud SDK (vertex-ai)
# Initialize Flask app
app = Flask(__name__)
# Set the Google Cloud project ID and location (Make sure to replace with your own)
project_id = os.getenv("GOOGLE_CLOUD_PROJECT_ID") # Make sure to set this in your environment
location = os.getenv("GOOGLE_CLOUD_LOCATION", "us-central1") # Default location if not set
# Initialize Vertex AI client
vertex_ai_client = vertex_ai.PredictionServiceClient(client_options={"api_endpoint": f"{location}-aiplatform.googleapis.com"})
# Define the endpoint for your model deployment
endpoint = "projects/{project_id}/locations/{location}/endpoints/{endpoint_id}" # Replace with your actual endpoint ID
# Define a system message (if necessary)
SYSTEM_MESSAGE = "You are a helpful assistant."
# Function to generate AI response using Google Gemini (Vertex AI)
def generate_response(
user_input: str,
history: List[Tuple[str, str]],
max_tokens: int = 150,
temperature: float = 0.7,
top_p: float = 1.0
) -> str:
"""
Generates a response using the Google Gemini (Vertex AI) API.
Args:
user_input: The user's input message.
history: A list of tuples containing the conversation history
(user input, AI response).
max_tokens: The maximum number of tokens in the generated response.
temperature: Controls the randomness of the generated response.
top_p: Controls the nucleus sampling probability.
Returns:
str: The generated response from the AI model.
"""
try:
# Prepare the history and current input for the model
conversation = [{"role": "system", "content": SYSTEM_MESSAGE}]
for user_message, assistant_message in history:
conversation.append({"role": "user", "content": user_message})
conversation.append({"role": "assistant", "content": assistant_message})
# Add the current user input
conversation.append({"role": "user", "content": user_input})
# Prepare the payload for the request to Vertex AI
instances = [{"content": conversation}]
parameters = {
"temperature": temperature,
"max_output_tokens": max_tokens,
"top_p": top_p,
}
# Send the request to the Vertex AI API
response = vertex_ai_client.predict(endpoint=endpoint, instances=instances, parameters=parameters)
# Extract the response from the API output
ai_response = response.predictions[0].get('content', 'Sorry, I couldn’t generate a response.')
return ai_response
except Exception as e:
print(f"An error occurred: {e}")
return "Error: An unexpected error occurred while processing your request."
# Route to handle user input and generate responses
@app.route("/chat", methods=["POST"])
def chat():
try:
# Get user input from the request
user_input = request.json.get("user_input", "")
history = request.json.get("history", [])
# Generate the AI response
response = generate_response(
user_input=user_input,
history=history
)
# Return the response as JSON
return jsonify({"response": response})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
# Run the app
app.run(debug=True, host="0.0.0.0", port=5000)
|