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torch2
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import streamlit as st
import requests
import os
import time

# Define the endpoint and API key
api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
api_key = os.getenv('HFSecret')

headers = {
    "Authorization": f"Bearer {api_key}"
}

# API call function
def call_huggingface_api(prompt):
    data = {"inputs": prompt, "parameters": {"max_length": 500, "temperature": 0.5}}
    response = requests.post(api_url, headers=headers, json=data)
    
    if response.status_code != 200:
        st.error(f"Error: {response.status_code} - {response.text}")
        return None
    
    return response.json()

# Function to load text from a URL
def load_text_from_url(url):
    response = requests.get(url)
    return response.text if response.status_code == 200 else ""

# Preset sample text options
options = ['None', 'Appreciation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
url_dict = {
    'Appreciation Letter': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Appreciation_Letter.txt",
    'Regret Letter': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Regret_Letter.txt",
    'Kindness Tale': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Kindness_Tale.txt",
    'Lost Melody Tale': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Lost_Melody_Tale.txt",
    'Twitter Example 1': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_1.txt",
    'Twitter Example 2': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_2.txt"
}

# Streamlit layout
st.title("Sentiment Analysis, Summarization, and Keyword Extraction")

# Dropdown to select a text file
selected_option = st.selectbox("Select a preset option", options)

# Initialize text_input
text_input = ""

# Load text based on dropdown selection
if selected_option != 'None':
    with st.spinner("Loading text..."):
        text_input = load_text_from_url(url_dict[selected_option])
        time.sleep(1)  # Simulate loading time
        st.success("Text loaded!")
else:
    text_input = st.text_area("Or enter your own text for analysis")

if st.button("Analyze"):
    if text_input:
        with st.spinner('Processing...'):
            # Sentiment Analysis
            sentiment_prompt = f"Perform sentiment analysis on the following text: {text_input}"
            sentiment_result = call_huggingface_api(sentiment_prompt)
            
            # Summarization
            summarization_prompt = f"Summarize the following text: {text_input}"
            summarization_result = call_huggingface_api(summarization_prompt)
            
            # Keyword Extraction
            keyword_prompt = f"Extract important keywords from the following text: {text_input}"
            keyword_result = call_huggingface_api(keyword_prompt)
            
            time.sleep(1)  # Simulate a small delay
            st.success('Analysis completed!')

        # Display Results in Collapsible Expanders
        if sentiment_result:
            with st.expander("Sentiment Analysis (Conclusion)"):
                st.write("Conclusion: Positive :) or Negative :( ")
                st.write(sentiment_result[0]['generated_text'])
        
        if summarization_result:
            with st.expander("Summarization"):
                st.write(summarization_result[0]['generated_text'])
        
        if keyword_result:
            with st.expander("Keyword Extraction"):
                st.write(keyword_result[0]['generated_text'].split(','))  # Display keywords as list
    else:
        st.warning("Please enter some text for analysis.")