new file

app.py

@@ -5,112 +5,36 @@ from tools import sentiment_analysis_util
 import numpy as np
 from dotenv import load_dotenv
 import os
+st.title("💬 Chatbot")
+st.caption("")
 
-st.set_page_config(page_title="LangChain Agent", layout="wide")
-load_dotenv()
-OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
 
-llm = ChatOpenAI(model="gpt-3.5-turbo")
+openai_api_key = os.environ["OPENAI_API_KEY"]
+# Initialize session state for storing messages if it doesn't already exist
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
 
-from langchain_core.runnables import RunnableConfig
+# Display all previous messages
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
 
-st.title("💬 ExpressMood")
+# Input for new prompts
+prompt = st.chat_input("Enter your question:")
+if prompt:
+    if not openai_api_key:
+        st.error("No OpenAI API key found. Please set the OPENAI_API_KEY environment variable.")
+        st.stop()
 
-#@st.cache_resource
-if "chat_history" not in st.session_state:
-    st.session_state["messages"] = [{"role":"system", "content":"""
-You are a sentiment analysis expert. Answer all questions related to the topic. Say I don't know if you don't know.
-"""}]
-
-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
+    # Append the new user message to session state
     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')
+    st.chat_message("user").write(prompt)
 
-                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:
-    st.session_state["openai_model"] = "gpt-3.5-turbo"
-
-if prompt := st.chat_input("Any other questions? "):
-    # 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)
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        stream = client.chat.completions.create(
-            model=st.session_state["openai_model"],
-            messages=[
-                {"role": m["role"], "content": m["content"]}
-                for m in st.session_state.messages
-            ],
-            stream=True,
-        )
-        response = st.write_stream(stream)
-    st.session_state.messages.append({"role": "assistant", "content": response})
+    # Use a spinner to indicate that the model is generating a response
+    with st.spinner('Thinking...'):
+        client = OpenAI(api_key=openai_api_key)
+        response = client.chat.completions.create(model="gpt-3.5-turbo", messages=st.session_state.messages)
+        msg = response.choices[0].message.content
 
+    # Append and display the assistant's response
+    st.session_state.messages.append({"role": "assistant", "content": msg})
+    st.chat_message("assistant").write(msg)