Delete pages

pages/Comparision.py

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-import streamlit as st
-from openai import OpenAI
-import os
-import requests
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# Initialize the client with HuggingFace
-client = OpenAI(
-    base_url="https://api-inference.huggingface.co/v1",
-    api_key=os.environ.get('HFSecret')  # Replace with your HuggingFace token
-)
-
-# Define the Llama 3 8B model
-repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
-
-# Title of the App
-st.title("Text Analysis with Llama 3: Sentiment, Summarization, and Keyword Extraction")
-
-# Dropdown options to choose a text file
-options = ['None', 'Appreciation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
-
-# Create a dropdown menu to select options
-selected_option = st.selectbox("Select a preset option", options)
-
-# Define URLs for different text options
-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"
-}
-
-# Function to fetch text content
-def fetch_text_content(option):
-    if option in url_dict:
-        response = requests.get(url_dict[option])
-        return response.text if response.status_code == 200 else "Error fetching the text"
-    return ""
-
-# Fetch the selected text
-jd = fetch_text_content(selected_option)
-
-# Display fetched text
-text = st.text_area('Enter the text to analyze', jd)
-
-# Function to call Llama 3 for analysis
-def call_llama_analysis(task, text):
-    prompt = f"Perform {task} on the following text:\n\n{text}"
-    
-    # Call Llama 3 for the task
-    response = client.completions.create(
-        model=repo_id,
-        prompt=prompt,
-        max_tokens=3000,
-        temperature=0.5
-    )
-    
-    return response['choices'][0]['text']
-
-# Start analysis on button click
-if st.button("Start Analysis"):
-    with st.spinner("Analyzing Sentiment..."):
-        try:
-            sentiment_result = call_llama_analysis("sentiment analysis", text)
-            with st.expander("Sentiment Analysis - ✅ Completed", expanded=True):
-                st.write(sentiment_result)
-        except Exception as e:
-            st.error(f"Error in Sentiment Analysis: {str(e)}")
-
-    with st.spinner("Summarizing..."):
-        try:
-            summary_result = call_llama_analysis("summarization", text)
-            with st.expander("Summarization - ✅ Completed", expanded=True):
-                st.write(summary_result)
-        except Exception as e:
-            st.error(f"Error in Summarization: {str(e)}")
-
-    with st.spinner("Extracting Keywords..."):
-        try:
-            keywords_result = call_llama_analysis("keyword extraction", text)
-            with st.expander("Keywords Extraction - ✅ Completed", expanded=True):
-                st.write(keywords_result)
-        except Exception as e:
-            st.error(f"Error in Keyword Extraction: {str(e)}")