pages/Comparision.py

import streamlit as st
import requests
from transformers import pipeline
import concurrent.futures
import os
import json
from dotenv import load_dotenv
from requests.exceptions import JSONDecodeError

# Load environment variables
load_dotenv()

# Initialize Hugging Face API for Llama 3
HF_API_URL = "https://api-inference.huggingface.co/v1"
HF_API_KEY = os.getenv('HFSecret')

# Initialize pipelines for Transformers
pipe_sent_transformers = pipeline('sentiment-analysis')
pipe_summ_transformers = pipeline("summarization", model="facebook/bart-large-cnn")

# Define the Llama 3 model ID
LLAMA_MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"

# Function to fetch text content from Transformers app
def fetch_text_content(selected_option):
    options_urls = {
        '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"
    }
    return requests.get(options_urls[selected_option]).text if selected_option in options_urls else ""

# Function to analyze sentiment using Llama
def analyze_with_llama(text):
    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
    data = {
        "inputs": text,
        "options": {
            "use_cache": False,
            "wait_for_model": True
        }
    }
    
    try:
        response = requests.post(f"{HF_API_URL}/models/{LLAMA_MODEL_ID}", headers=headers, json=data)
        response.raise_for_status()
        return response.json()  # Ensure valid JSON
    except (requests.RequestException, json.JSONDecodeError):
        return {"error": "Error occurred while processing Llama model response."}

# Function to run Transformer-based analysis
def transformer_analysis(text):
    # Sentiment analysis
    sentiment_result = pipe_sent_transformers(text)
    sentiment_score = sentiment_result[0]['score']
    sentiment_label = sentiment_result[0]['label']
    
    # Summarization
    summary_result = pipe_summ_transformers(text)
    summary = summary_result[0]['summary_text']
    
    return sentiment_score, sentiment_label, summary

# Function to run Llama-based analysis
def llama_analysis(text):
    llama_response = analyze_with_llama(text)
    
    if "error" in llama_response:
        return "Error", "Error", "Error"
    
    # Extract sentiment and summary if valid JSON
    sentiment_label = llama_response.get('sentiment', 'UNKNOWN')
    sentiment_score = llama_response.get('sentiment_score', 0.0)
    summary = llama_response.get('summary', 'No summary available.')
    
    return sentiment_score, sentiment_label, summary

# Streamlit app layout with two columns
st.title("Parallel Sentiment Analysis with Transformers and Llama")

# Select text to analyze from dropdown
options = ['None', 'Appreciation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
selected_option = st.selectbox("Select a preset option", options)

# Fetch text content for analysis
jd = fetch_text_content(selected_option)
text = st.text_area('Enter the text to analyze', jd)

if st.button("Start Analysis"):
    # Set up the two columns for parallel analysis
    col1, col2 = st.columns(2)

    with st.spinner("Running sentiment analysis..."):
        with concurrent.futures.ThreadPoolExecutor() as executor:
            # Execute analyses in parallel
            future_transformer = executor.submit(transformer_analysis, text)
            future_llama = executor.submit(llama_analysis, text)

            # Retrieve results from both transformers and Llama
            sentiment_score_transformer, sentiment_label_transformer, summary_transformer = future_transformer.result()
            sentiment_score_llama, sentiment_label_llama, summary_llama = future_llama.result()




    # Ensure that the score is properly handled as a float, or display the string as-is
    def display_score(score):
        try:
            # Attempt to format as float if it's a valid number
            return f"{float(score):.2f}"
        except ValueError:
            # If it's not a number, just return the score as is (probably a string error message)
            return score
    
    # Display results for Transformers-based analysis in the first column
    with col1:
        st.subheader("Transformers Analysis")
        with st.expander("Sentiment Analysis - Transformers"):
            sentiment_emoji = '😊' if sentiment_label_transformer == 'POSITIVE' else '😞'
            st.write(f"Sentiment: {sentiment_label_transformer} ({sentiment_emoji})")
            st.write(f"Score: {display_score(sentiment_score_transformer)}")  # Use the display_score function
    
        with st.expander("Summarization - Transformers"):
            st.write(summary_transformer)
    
    # Display results for Llama-based analysis in the second column
    with col2:
        st.subheader("Llama Analysis")
        with st.expander("Sentiment Analysis - Llama"):
            sentiment_emoji = '😊' if sentiment_label_llama == 'POSITIVE' else '😞'
            st.write(f"Sentiment: {sentiment_label_llama} ({sentiment_emoji})")
            st.write(f"Score: {display_score(sentiment_score_llama)}")  # Use the display_score function
    
        with st.expander("Summarization - Llama"):
            st.write(summary_llama)