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| import streamlit as st | |
| import requests | |
| from utils import ( | |
| fetch_from_web, | |
| analyze_sentiment, | |
| generate_comparative_sentiment, | |
| generate_final_report, | |
| get_summaries_by_sentiment, | |
| translate, | |
| text_to_speech, | |
| ) | |
| st.title("Company Sentiment Analyzer") | |
| company_name = st.text_input("Enter Company Name", "Tesla") | |
| model_provider = st.selectbox("Model Provider", options=["Ollama", "Groq"]) | |
| if st.button("Fetch Sentiment Data"): | |
| web_results = fetch_from_web(company_name) | |
| if "sources" not in web_results: | |
| return {"error": "No sources found."} | |
| sentiment_output = [ | |
| analyze_sentiment(article, model_provider) | |
| for article in web_results["sources"][:5] | |
| ] | |
| comparative_sentiment = generate_comparative_sentiment(sentiment_output) | |
| positive_summary, negative_summary, neutral_summary = get_summaries_by_sentiment( | |
| sentiment_output | |
| ) | |
| final_report = generate_final_report( | |
| positive_summary, | |
| negative_summary, | |
| neutral_summary, | |
| comparative_sentiment, | |
| model_provider, | |
| ) | |
| hindi_translation = translate(final_report, model_provider) | |
| audio_path = text_to_speech(hindi_translation) | |
| output_dict = { | |
| "company_name": company_name, | |
| "articles": sentiment_output, | |
| "comparative_sentiment": comparative_sentiment, | |
| "final_report": final_report, | |
| "hindi_translation": hindi_translation, | |
| "audio_url": audio_path, | |
| } | |
| st.subheader("Company Name") | |
| st.write(output_dict.get("company_name")) | |
| st.subheader("Final Report") | |
| st.write(output_dict.get("final_report")) | |
| st.subheader("๐ Audio Output") | |
| audio_file = "output.mp3" | |
| if audio_file: | |
| st.audio(audio_file) | |
| except requests.exceptions.RequestException as e: | |
| st.error(f"Error fetching data: {e}") | |
| # |