Update pages/Comparision.py

pages/Comparision.py

@@ -1,105 +1,88 @@
 import streamlit as st
-import requests
+from openai import OpenAI
 import os
 from dotenv import load_dotenv
-from nltk.corpus import stopwords
-from fuzzywuzzy import fuzz
-from rake_nltk import Rake
-import nltk
-from openai import OpenAI
 
 # Load environment variables
 load_dotenv()
 
-# Download NLTK resources
-nltk.download('punkt')
-nltk.download('stopwords')
-
-# Initialize OpenAI client for Hugging Face Llama 3
+# Initialize the client with HuggingFace
 client = OpenAI(
     base_url="https://api-inference.huggingface.co/v1",
-    api_key=os.environ.get('HFSecret')  # Replace with your token
+    api_key=os.environ.get('HFSecret')  # Replace with your HuggingFace token
 )
 
-# Define the Llama 3 model repo ID
+# Define the Llama 3 8B model
 repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
 
-# Function to fetch text content based on selected option
-def fetch_text_content(selected_option):
-    url_mapping = {
-        '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"
-    }
-    return requests.get(url_mapping.get(selected_option, "")).text
-
-# 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 = [(score, phrase) for score, phrase in phrases_with_scores if phrase.lower() not in stop_words]
-    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]
-            merged_phrase = max([phrase] + similar_phrases, key=len) if similar_phrases else phrase
-            unique_keywords.append((score, merged_phrase))
-            seen_phrases.add(phrase)
-    return unique_keywords[:10]
-
-# Function to interact with Llama 3 for analysis
-def llama3_analysis(text, task):
-    prompt_mapping = {
-        "sentiment": f"Analyze the sentiment of the following text: {text}",
-        "summarization": f"Summarize the following text: {text}"
-    }
-
-    prompt = prompt_mapping[task]
-
-    try:
-        response = client.completions.create(
-            model=repo_id,
-            prompt=prompt,
-            max_tokens=500,
-            temperature=0.5
-        )
-        return response.choices[0].text.strip()
-    except Exception as e:
-        return f"Error: {str(e)}"
+# Title of the App
+st.title("Text Analysis with Llama 3: Sentiment, Summarization, and Keyword Extraction")
 
-# Streamlit App UI
-st.title("Sentiment Analysis & Summarization with Llama 3")
+# Dropdown options to choose a text file
+options = ['None', 'Appreciation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
 
-# Dropdown menu to select the text source
-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)
 
-# Fetch the text based on selection
+# 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)
 
-# Text area for manual input or displaying fetched content
+# 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..."):
-        sentiment_result = llama3_analysis(text, "sentiment")
-        with st.expander("Sentiment Analysis - ✅ Completed", expanded=False):
-            st.write(sentiment_result)
+        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..."):
-        summary_result = llama3_analysis(text, "summarization")
-        with st.expander("Summarization - ✅ Completed", expanded=False):
-            st.write(summary_result)
+        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..."):
-        keywords = extract_keywords(text)
-        with st.expander("Keywords Extraction - ✅ Completed", expanded=False):
-            st.write([kw[1] for kw in 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)}")