new file

app.py

@@ -25,74 +25,6 @@ You are a sentiment analysis expert. Answer all questions related to cryptocurre
 
 initialize_session_state()
 
-#st.image('el_pic.png')
-
-sideb=st.sidebar
-with st.sidebar:
-    prompt=st.text_input("Enter topic for sentiment analysis: ")
-
-check1=sideb.button(f"analyze {prompt}")
-
-if check1:
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    # Display user message in chat message container
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    # ========================== Sentiment analysis
-    #Perform sentiment analysis on the cryptocurrency news & predict dominant sentiment along with plotting the sentiment breakdown chart
-    # Downloading from reddit
-
-    # Downloading from alpaca
-    if len(prompt.split(' '))<2:
-        print('here')
-        st.write('I am analyzing Google News ...')
-        news_articles = sentiment_analysis_util.fetch_news(str(prompt))
-    st.write('Now, I am analyzing Reddit ...')
-    reddit_news_articles=sentiment_analysis_util.fetch_reddit_news(prompt)
-    analysis_results = []
-
-    #Perform sentiment analysis for each product review
-    if len(prompt.split(' '))<2:
-        print('here')
-        for article in news_articles:
-            if prompt.lower()[0:6] in article['News_Article'].lower():
-                sentiment_analysis_result = sentiment_analysis_util.analyze_sentiment(article['News_Article'])
-
-                # Display sentiment analysis results
-                #print(f'News Article: {sentiment_analysis_result["News_Article"]} : Sentiment: {sentiment_analysis_result["Sentiment"]}', '\n')
-
-                result = {
-                            'News_Article': sentiment_analysis_result["News_Article"],
-                            'Sentiment': sentiment_analysis_result["Sentiment"][0]['label'],
-                            'Index': sentiment_analysis_result["Sentiment"][0]['score'],
-                            'URL': article['URL']
-                        }
-                
-                analysis_results.append(result)
-
-    articles_url=[]
-    for article in reddit_news_articles:
-        if prompt.lower()[0:6] in article.lower():
-            sentiment_analysis_result_reddit = sentiment_analysis_util.analyze_sentiment(article)
-
-            # Display sentiment analysis results
-            #print(f'News Article: {sentiment_analysis_result_reddit["News_Article"]} : Sentiment: {sentiment_analysis_result_reddit["Sentiment"]}', '\n')
-
-            result = {
-                        'News_Article': sentiment_analysis_result_reddit["News_Article"],
-                        'Index':np.round(sentiment_analysis_result_reddit["Sentiment"][0]['score'],2)
-                    }
-            analysis_results.append(np.append(result,np.append(article.split('URL:')[-1:], ((article.split('Date: ')[-1:])[0][0:10]))))
-    #pd.DataFrame(analysis_results).to_csv('analysis_results.csv')
-
-    #Generate summarized message rationalize dominant sentiment
-    summary = sentiment_analysis_util.generate_summary_of_sentiment(analysis_results) #, dominant_sentiment)
-    st.chat_message("assistant").write((summary))
-    st.session_state.messages.append({"role": "assistant", "content": summary})
-    #answers=np.append(res["messages"][-1].content,summary)
-
 client = OpenAI(api_key=OPENAI_API_KEY)
 
 if "openai_model" not in st.session_state: