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| import streamlit as st | |
| from meta_ai_api import MetaAI | |
| from urllib.parse import urlparse | |
| import pandas as pd | |
| import plotly.express as px | |
| from nltk.sentiment.vader import SentimentIntensityAnalyzer | |
| import nltk | |
| import json | |
| # Initialize Meta AI API | |
| ai = MetaAI() | |
| PAGE_CONFIG = { | |
| "page_title": "Meta AI Query Analysis - a Free SEO Tool by WordLift", | |
| "page_icon": "img/fav-ico.png", | |
| "layout": "centered" | |
| } | |
| def local_css(file_name): | |
| with open(file_name) as f: | |
| st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True) | |
| st.set_page_config(**PAGE_CONFIG) | |
| local_css("style.css") | |
| def fetch_response(query): | |
| response = ai.prompt(message=query) | |
| return response | |
| def display_sources(sources): | |
| if sources: | |
| for source in sources: | |
| # Parse the domain from the URL | |
| domain = urlparse(source['link']).netloc | |
| # Format and display the domain and title | |
| st.markdown(f"- **{domain}**: [{source['title']}]({source['link']})", unsafe_allow_html=True) | |
| else: | |
| st.write("No sources available.") | |
| # ---------------------------------------------------------------------------- # | |
| # Sentiment Analysis Function | |
| # ---------------------------------------------------------------------------- # | |
| # Download the VADER lexicon for sentiment analysis | |
| nltk.download('vader_lexicon') | |
| # Initialize the Sentiment Intensity Analyzer | |
| sid = SentimentIntensityAnalyzer() | |
| def sentiment_analysis(text): | |
| # Split the text into sentences | |
| sentences = [sentence.strip() for sentence in text.split('.') if sentence] | |
| # Create a DataFrame to hold the content and sentiment scores | |
| df = pd.DataFrame(sentences, columns=['content']) | |
| # Calculate sentiment scores for each sentence | |
| df['sentiment_scores'] = df['content'].apply(lambda x: sid.polarity_scores(x)) | |
| # Split sentiment_scores into separate columns | |
| df = pd.concat([df.drop(['sentiment_scores'], axis=1), df['sentiment_scores'].apply(pd.Series)], axis=1) | |
| # Determine the dominant sentiment and its confidence | |
| df['dominant_sentiment'] = df[['neg', 'neu', 'pos']].idxmax(axis=1) | |
| df['confidence'] = df[['neg', 'neu', 'pos']].max(axis=1) | |
| return df | |
| # ---------------------------------------------------------------------------- # | |
| # Advanced Analysis | |
| # ---------------------------------------------------------------------------- # | |
| def fetch_advanced_analysis(query, msg): | |
| analysis_prompt = f""" | |
| Analyze the user's request: '{query}', and the response: '{msg}'. | |
| Based on this analysis, generate a detailed JSON response including: | |
| 1. The user's intent, | |
| 2. Up to four follow-up questions, | |
| 3. The main entities mentioned in the response. | |
| Example of expected JSON format: | |
| {{ | |
| "user_intent": "Identify the effects of climate change on polar bears", | |
| "follow_up_questions": [ | |
| "What are the primary threats to polar bears today?", | |
| "How does the melting ice affect their habitat?", | |
| "What conservation efforts are in place for polar bears?", | |
| "How can individuals contribute to these efforts?" | |
| ], | |
| "entities": {{ | |
| "animal": ["polar bears"], | |
| "issue": ["climate change"], | |
| "actions": ["conservation efforts"] | |
| }} | |
| }} | |
| """ | |
| # Assume ai is an initialized MetaAI instance that can send prompts to the AI service | |
| advanced_response = ai.prompt(message=analysis_prompt) | |
| return advanced_response | |
| def parse_analysis(analysis_message): | |
| try: | |
| start = analysis_message.find('{') | |
| end = analysis_message.rfind('}') + 1 # Find the last '}' and include it | |
| if start != -1 and end != -1: | |
| json_str = analysis_message[start:end] | |
| print("Debug JSON String:", json_str) # Continue to use this for debugging | |
| analysis_data = json.loads(json_str) | |
| return analysis_data | |
| else: | |
| return {"error": "Valid JSON data not found in the response"} | |
| except json.JSONDecodeError as e: | |
| return {"error": "Failed to decode JSON", "details": str(e)} | |
| # ---------------------------------------------------------------------------- # | |
| # Main Function | |
| # ---------------------------------------------------------------------------- # | |
| def main(): | |
| # Path to the image | |
| image_path = 'img/meta-ai-logo.png' # Replace with your image's filename and extension | |
| # Create two columns | |
| col1, col2 = st.columns([1, 2]) # Adjust the ratio as needed for your layout | |
| # Use the first column to display the image | |
| with col1: | |
| st.image(image_path, width=60) | |
| # Use the second column to display the title and other content | |
| with col2: | |
| st.title("Meta AI SEO Tool") | |
| # User input | |
| user_query = st.text_area("Enter your query:", height=150) | |
| submit_button = st.button("Analyze Query") | |
| # Create tabs | |
| tab1, tab2, tab3 = st.tabs(["Overview", "Analysis", "Sentiment"]) | |
| # Tab 1: Overview - Showing the initial response and sources | |
| with tab1: | |
| user_query = st.text_area("Enter your query:", height=150, key="query_overview") | |
| submit_button = st.button("Analyze Query", key="submit_overview") | |
| if submit_button and user_query: | |
| response = fetch_response(user_query) | |
| msg = response.get('message', 'No response message.') | |
| st.write(msg) | |
| with st.expander("Show Sources"): | |
| display_sources(response.get('sources', [])) | |
| # Tab 2: Analysis - Showing the result of the advanced analysis | |
| with tab2: | |
| # In case you need inputs here as well, ensure they have unique keys | |
| if 'submit_overview' in st.session_state and st.session_state.submit_overview: | |
| advanced_response = fetch_advanced_analysis(st.session_state.query_overview, msg) | |
| advanced_msg = advanced_response.get('message', 'No advanced analysis available.') | |
| analysis_data = parse_analysis(advanced_msg) | |
| if "error" not in analysis_data: | |
| st.write("### User Intent") | |
| st.write(analysis_data['user_intent']) | |
| st.write("### Follow-up Questions") | |
| for question in analysis_data['follow_up_questions']: | |
| st.write("- " + question) | |
| st.write("### Identified Entities") | |
| for entity_type, entities in analysis_data['entities'].items(): | |
| st.write(f"**{entity_type.capitalize()}**: {', '.join(entities)}") | |
| # Tab 3: Sentiment - Displaying sentiment analysis of the response | |
| with tab3: | |
| if 'submit_overview' in st.session_state and st.session_state.submit_overview: | |
| df_sentiment = sentiment_analysis(msg) | |
| fig = px.scatter(df_sentiment, y='dominant_sentiment', color='dominant_sentiment', size='confidence', | |
| hover_data=['content'], | |
| color_discrete_map={"neg": "firebrick", "neu": "navajowhite", "pos": "darkgreen"}, | |
| labels={'dominant_sentiment': 'Sentiment'}, | |
| title='Sentiment Analysis of the Response') | |
| fig.update_layout(width=800, height=300) | |
| st.plotly_chart(fig) | |
| if __name__ == "__main__": | |
| main() |