import vertexai
import http.client
import typing
import urllib.request
from vertexai.preview.generative_models import GenerativeModel ,ChatSession
import streamlit as st 
from io import StringIO
import pandas as pd
from streamlit_extras.let_it_rain import rain
import time
import nltk
from textblob import TextBlob

st.set_page_config (page_icon="icon.jpg",page_title="Content Moderation",layout="wide")
st.markdown("<h1 style='color: var(--black-100, var(--black-100, #1C1C1C));text-align: center;font-feature-settings: 'cv11' on, 'cv01' on, 'ss01' on;font-family: Poppins;font-size: 48px;font-style: normal;font-weight: 600;line-height: 58px;'>Content Moderation</h1>",
            unsafe_allow_html=True)
project_id = "agileai-poc"
loc = "us-central1"
model= GenerativeModel("gemini-pro")

# prompt="""understand the content provided and if any spaces found in content ignore and generate the output only in the given format  
#                             format: 1.Tone:find the tone of content 
#                                2.Negative sentences :"Only find the negative sentences based on semantic analysis" to ensure accuracy in detecting negative words in pointwise and "must highlight the negative words in bold" in same sentence 
#                                if the content is in positive tone give output as "No changes required"
#                 """
# prompt2=""" understand the content provided and generate the output only in the given format
#                     format: 1.Tone :provide the generated content tone
#                         2.Content:"Don't explain the content" just modify the same content by only "Replacing the negative words by converting the tone into formal",if the content doesn't have any negative words give output as "No changes required" """
prompt="""find the tone of content state whether it is positive or negative and generate the output only in the given format
            format: Tone:just specify the tone is positive or negative """
# if input_text:
## Define layout and containers
HEIGHT = 1000

cols = st.columns(2)

with cols[0]:
    left_panel = st.container(height=HEIGHT + 15, border=True)

with cols[1]:
    right_panel=st.container(height=HEIGHT +15,border=True)

## Add contents
def offensive_text(input):
        if input :
            response=model.generate_content([prompt,input]) 
            tone=response.text
            if tone=="negative":
                # print(response)
                output=st.write(response.text)
                check=st.write("negative")
            elif tone=="positive":
                with right_panel:
                    # print(response)
                    # st.write(response.text)
                    check=st.write("positive")
            else :
                check=st.write("Your content needs to be modified ..!!")
        return check
# with left_panel:
#     st.markdown("<h5 style='font-style:bold,color:blue'>Fans Dashboard</h5>",unsafe_allow_html=True)
#     input=st.chat_input("Type your comment")
#     offensive_text(input)
    # model = GenerativeModel("gemini-pro")
chat = model.start_chat(history=[])

# def get_chat_response(chat: ChatSession, prompt: str) -> str:
#     response = chat.send_message(prompt)
#     return response.text



# Function to determine the tone (positive/negative) of the input
def determine_tone(input_text):
    # Perform sentiment analysis using TextBlob
    analysis = TextBlob(input_text)
    
    # Determine the polarity of the sentiment
    polarity = analysis.sentiment.polarity
    
    # Check if the sentiment polarity is positive, negative, or neutral
    if polarity > 0:
        return "Positive"
    elif polarity < 0:
        return "Negative"
    else:
        return "Neutral"
if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []
with left_panel:
    st.markdown("<h5 style='font-style:bold,color:blue'>Fans Dashboard</h5>",unsafe_allow_html=True)
with right_panel:
    st.markdown("<h5 style='font-style:bold,color:blue'>Celeb Dashboard</h5>",unsafe_allow_html=True)

# Example usage
input_text = st.chat_input("type your comment")
if input_text:
 with left_panel:
    # st.markdown("<h5 style='font-style:bold,color:blue'>Fans Dashboard</h5>",unsafe_allow_html=True)
    if input_text:
        tone = determine_tone(input_text)
        # st.write(tone)
        if tone=="Negative":
            st.write(":x:",input_text)
            # st.text_area(input_text)
        elif tone=="Positive":
            st.write(":heavy_check_mark:",input_text)
        else:
            st.write("your content need to modifed")
with right_panel:
    # st.markdown("<h5 style='font-style:bold,color:blue'>Celeb Dashboard</h5>",unsafe_allow_html=True)
    tone = determine_tone(input_text)
    if tone=="Positive":
        st.write(":heavy_check_mark:",input_text)
# print("The tone of the input is:", tone)

#print("the tone is not required :")
    # prompt = st.chat_input("Type your comment")
    # # if prompt:
    # #     # st.write(get_chat_response(chat, prompt))
    # st.write(offensive_text(prompt))
# prompt = "What are all the colors in a rainbow?"
# print(get_chat_response(chat, prompt))

# prompt = "Why does it appear when it rains?"
# print(get_chat_response(chat, prompt))
# # try:
#     with upper_right_panel:
#         st.markdown("<h6 style='color:red;font-style:bold'>Analyzed Content:</h6>",unsafe_allow_html=True)
#         st.write(response.text)
#     with lower_right_panel:
#         st.markdown("<h6 style='color:green;font-style:bold'>Rephrased content:</h6>",unsafe_allow_html=True)
#         reference=model.generate_content([prompt2,response.text])
#         st.write(reference.text)
# except:
#     st.error("Check the input text ,avoid empty spaces if any.")
#     st.stop()
# if "messages" not in st.session_state:
#     st.session_state.messages = []

# for message in st.session_state.messages:
#     with st.chat_message(message["role"]):
#         st.markdown(message["content"])
# queries = st.chat_input("Ask your queries here!")
# if prompt := queries:
#     # Display user message in chat message container
#     with st.chat_message("user"):
#         st.markdown(prompt)
#     # Add user message to chat history
#     st.session_state.messages.append({"role": "user", "content": prompt})

#     ask_url = "http://127.0.0.1:5000/predict"
#     question_data = {"questions": prompt}
#     with st.spinner("..."):
#         response = requests.post(
#             url=ask_url,
#             json=question_data,
#         )
#     # response=prompt

#     answer = response.json().get("predicted_answer")
#     with st.chat_message("Assistant"):
#         print("Ans : ", answer)
#         st.write(answer)
#     st.session_state.messages.append({"role": "Assistant", "content": answer})