import vertexai
from vertexai.generative_models import GenerativeModel, Image
import streamlit as st
import os 
# export STREAMLIT_SERVER_MAX_UPLOAD_SIZE=200
PROJECT_ID = "agileai-poc"
REGION = "us-central1"
vertexai.init(project=PROJECT_ID, location=REGION)
# upload_image = st.file_uploader("Upload an image", type=["jpeg","png","jpg"],accept_multiple_files=True)
# print(upload_image)
# IMAGE_FILE = "image-path"
image =st.file_uploader("upload file",type=["png","jpg","jpeg"],accept_multiple_files=True)
# if file:
#     file_bytes = file.read()
# image = Image.load_from_file(file_bytes)
# if upload_image:
#     image = Image.load_from_file(upload_image)

generative_multimodal_model = GenerativeModel("gemini-1.0-pro-vision")
response = generative_multimodal_model.generate_content(["Describe the image", image])
# prompt="""Here are a few of the things that aren\'t allowed on this chat:
# 1. Nudity or other sexually suggestive content
# 2. Hate speech, credible threats or direct attacks on an individual or group
# 3. Content that contains self-harm or excessive violence
# 4. Fake or impostor profiles
# 5. Spam

# The following behaviour isn\'t allowed on this chat:
# 1. Posting things that don\'t follow the Community Standards (e.g. threats, hate speech, graphic violence).
# 2. Using Community to bully, impersonate or harass anyone.

# User-1: Hold onto your hearts. \"Tiger 3\" will make you laugh, cry, and feel everything in between. In cinemas soon.
# #love #heartbreak #feelgoodmovie
# User-2: Loved your look in \"Tiger 3\"! You always look so hot and sexy.

# User-1: Instead of politicians, let the monkeys govern the countries; at least they will steal only the bananas!
# User-2: User-2\'s comment is not allowed on this chat because it contains sexually suggestive content.

# User-1: Loved your look in \"Tiger 3\"! You always look so hot and sexy.
# User-2: The response was blocked because the input or response may contain descriptions of violence, sexual themes, or otherwise derogatory content. Please try rephrasing your prompt.

# User-1: Be courageous. Challenge orthodoxy. Stand up for what you believe in. When you are in your rocking chair talking to your grandchildren many years from now, be sure you have a good story to tell
# User-2:
# stricly follow prompt analyse {response.text} and declare output as "positive or negative" 
# """
prompt=""" Analyse the {response.text} and understand the content provided in {response.text} 
whether the content is sarcastic postive or negative etc and
 declare the output as "positive" or "negative"
"""
print(response.text)
result=generative_multimodal_model.generate_content([prompt,response.text])
print(result.text)

# import http.client
# import typing
# import urllib.request
# from vertexai.generative_models import GenerativeModel, Image

# # create helper function
# def load_image_from_url(image_url: str) -> Image:
#     with urllib.request.urlopen(image_url) as response:
#         response = typing.cast(http.client.HTTPResponse, response)
#         image_bytes = response.read()
#     return Image.from_bytes(image_bytes)

# # Load images from Cloud Storage URI
# landmark1 = load_image_from_url(
#     "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
# )
# landmark2 = load_image_from_url(
#     "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png"
# )
# landmark3 = load_image_from_url(
#     "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png"
# )

# # Pass multimodal prompt
# model = GenerativeModel("gemini-1.0-pro-vision")
# response = model.generate_content(
#     [
#         landmark1,
#         "city: Rome, Landmark: the Colosseum",
#         landmark2,
#         "city: Beijing, Landmark: Forbidden City",
#         landmark3,
#     ]
# )
# print(response)