Create pages/Comparision.py

pages/Comparision.py

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+import streamlit as st
+import requests
+import nltk
+from transformers import pipeline
+from rake_nltk import Rake
+from nltk.corpus import stopwords
+from fuzzywuzzy import fuzz
+from openai import OpenAI
+import os
+from dotenv import load_dotenv
+
+# Load environment variables for OpenAI
+load_dotenv()
+
+# Initialize OpenAI client for Llama 3 model
+client = OpenAI(
+  base_url="https://api-inference.huggingface.co/v1",
+  api_key=os.environ.get('HFSecret')  # Replace with your token
+)
+repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+st.title("Parallel Sentiment Analysis: Transformers vs. Llama 3")
+
+# Define the options for the dropdown menu, selecting a remote txt file already created to analyze the text
+options = ['None', 'Apprecitation 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 options
+urls = {
+    "Apprecitation 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 based on selected option
+def fetch_text_content(selected_option):
+    return requests.get(urls.get(selected_option, "")).text if selected_option in urls else ""
+
+# Fetch text content
+jd = fetch_text_content(selected_option)
+
+# Download NLTK resources
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Initialize transformer sentiment analysis pipeline
+pipe_sent = pipeline('sentiment-analysis')
+
+# Function to extract keywords
+def extract_keywords(text):
+    r = Rake()
+    r.extract_keywords_from_text(text)
+    phrases_with_scores = r.get_ranked_phrases_with_scores()
+    stop_words = set(stopwords.words('english'))
+    keywords = []
+    for score, phrase in phrases_with_scores:
+        if phrase.lower() not in stop_words:
+            keywords.append((score, phrase))
+    keywords.sort(key=lambda x: x[0], reverse=True)
+    unique_keywords = []
+    seen_phrases = set()
+    for score, phrase in keywords:
+        if phrase not in seen_phrases:
+            similar_phrases = [seen_phrase for seen_phrase in seen_phrases if fuzz.ratio(phrase, seen_phrase) > 70]
+            if similar_phrases:
+                merged_phrase = max([phrase] + similar_phrases, key=len)
+                unique_keywords.append((score, merged_phrase))
+            else:
+                unique_keywords.append((score, phrase))
+            seen_phrases.add(phrase)
+    return unique_keywords[:10]
+
+# Text input
+text = st.text_area('Enter the text to analyze', jd)
+
+if st.button("Start Analysis"):
+    col1, col2 = st.columns(2)
+
+    # Transformers (Column 1)
+    with col1:
+        st.header("Transformers Model")
+        with st.spinner("Analyzing with Transformers..."):
+            out_sentiment = pipe_sent(text)
+            sentiment_score = out_sentiment[0]['score']
+            sentiment_label = out_sentiment[0]['label']
+            sentiment_emoji = '😊' if sentiment_label == 'POSITIVE' else '😞'
+            st.write(f"Sentiment Score: {sentiment_score}, Sentiment Label: {sentiment_label.capitalize()} {sentiment_emoji}")
+
+        st.subheader("Keywords")
+        keywords = extract_keywords(text)
+        st.write([keyword[1] for keyword in keywords])
+
+    # Llama 3 Model (Column 2)
+    with col2:
+        st.header("Llama 3 Model")
+        with st.spinner("Analyzing with Llama 3..."):
+            try:
+                stream = client.chat.completions.create(
+                    model=repo_id,
+                    messages=[{"role": "user", "content": text}],
+                    temperature=0.5,
+                    stream=True,
+                    max_tokens=3000
+                )
+                response = ''.join([chunk['choices'][0]['text'] for chunk in stream])
+                st.write(response)
+            except Exception as e:
+                st.error("Error occurred while fetching response from Llama 3")
+