new file

app.py

@@ -5,36 +5,118 @@ 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"]
 
-openai_api_key = os.environ["OPENAI_API_KEY"]
-# Initialize session state for storing messages if it doesn't already exist
+llm = ChatOpenAI(model="gpt-3.5-turbo")
+
+from langchain_core.runnables import RunnableConfig
+
+st.title("💬 ExpressMood")
+
+#@st.cache_resource
 if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
+    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.
+"""}]
 
 # Display all previous messages
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
 
-# 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()
+#initialize_session_state()
+
+st.image('el_pic.png')
+
+sideb=st.sidebar
+with st.sidebar:
+    prompt=st.text_input("Enter topic for sentiment analysis: ")
 
-    # Append the new user message to session state
+check1=sideb.button(f"analyze {prompt}")
+
+if check1:
+    # Add user message to chat history
     st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(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'])
 
-    # 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
+                # 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:
+    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})
 
-    # Append and display the assistant's response
-    st.session_state.messages.append({"role": "assistant", "content": msg})
-    st.chat_message("assistant").write(msg)