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| import streamlit as st | |
| import requests | |
| import os | |
| import time | |
| # Define the endpoint and API key | |
| api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct" | |
| api_key = os.getenv('HFSecret') | |
| headers = { | |
| "Authorization": f"Bearer {api_key}" | |
| } | |
| # API call function | |
| def call_huggingface_api(prompt): | |
| data = {"inputs": prompt, "parameters": {"max_length": 500, "temperature": 0.5}} | |
| response = requests.post(api_url, headers=headers, json=data) | |
| if response.status_code != 200: | |
| st.error(f"Error: {response.status_code} - {response.text}") | |
| return None | |
| return response.json() | |
| # Function to load text from a URL | |
| def load_text_from_url(url): | |
| response = requests.get(url) | |
| return response.text if response.status_code == 200 else "" | |
| # Preset sample text options | |
| options = ['None', 'Appreciation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2'] | |
| 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" | |
| } | |
| # Streamlit layout | |
| st.title("Sentiment Analysis, Summarization, and Keyword Extraction") | |
| # Dropdown to select a text file | |
| selected_option = st.selectbox("Select a preset option", options) | |
| # Initialize text_input | |
| text_input = "" | |
| # Load text based on dropdown selection | |
| if selected_option != 'None': | |
| with st.spinner("Loading text..."): | |
| text_input = load_text_from_url(url_dict[selected_option]) | |
| time.sleep(1) # Simulate loading time | |
| st.success("Text loaded!") | |
| else: | |
| text_input = st.text_area("Or enter your own text for analysis") | |
| if st.button("Analyze"): | |
| if text_input: | |
| with st.spinner('Processing...'): | |
| # Sentiment Analysis | |
| sentiment_prompt = f"Perform sentiment analysis on the following text: {text_input}" | |
| sentiment_result = call_huggingface_api(sentiment_prompt) | |
| # Summarization | |
| summarization_prompt = f"Summarize the following text: {text_input}" | |
| summarization_result = call_huggingface_api(summarization_prompt) | |
| # Keyword Extraction | |
| keyword_prompt = f"Extract important keywords from the following text: {text_input}" | |
| keyword_result = call_huggingface_api(keyword_prompt) | |
| time.sleep(1) # Simulate a small delay | |
| st.success('Analysis completed!') | |
| # Display Results in Collapsible Expanders | |
| if sentiment_result: | |
| with st.expander("Sentiment Analysis (Conclusion)"): | |
| st.write("Conclusion: Positive :) or Negative :( ") | |
| st.write(sentiment_result[0]['generated_text']) | |
| if summarization_result: | |
| with st.expander("Summarization"): | |
| st.write(summarization_result[0]['generated_text']) | |
| if keyword_result: | |
| with st.expander("Keyword Extraction"): | |
| st.write(keyword_result[0]['generated_text'].split(',')) # Display keywords as list | |
| else: | |
| st.warning("Please enter some text for analysis.") | |