wip

app.py

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+import streamlit as st
+import random
+import time
+
+from openai import OpenAI
+import pandas as pd
+
+import elemeta.nlp.runners.metafeature_extractors_runner as metafeature_extractors_runner
+
+from elemeta.nlp.runners.metafeature_extractors_runner import MetafeatureExtractorsRunner
+from elemeta.nlp.extractors.high_level.text_length import TextLength
+from elemeta.nlp.extractors.high_level.text_complexity import TextComplexity
+from elemeta.nlp.extractors.high_level.word_count import WordCount
+from elemeta.nlp.extractors.high_level.detect_language_langdetect import DetectLanguage
+from elemeta.nlp.extractors.high_level.sentiment_polarity import SentimentPolarity
+from elemeta.nlp.extractors.high_level.toxicity_extractor import ToxicityExtractor
+runner = MetafeatureExtractorsRunner(metafeature_extractors=[TextLength(),WordCount(),DetectLanguage()
+                                                             ,SentimentPolarity(),TextComplexity(),ToxicityExtractor()])
+
+
+def ask_gpt(messages,model="gpt-4"):
+    ret =  client.chat.completions.create(model=model,
+                                          messages=messages
+                                          )
+    return ret.choices[0].message.content
+
+client = OpenAI()
+
+st.title("GPT-4 Chatbot")
+system_prompt = st.text_input("Enter your system's prompt",value="Translate the following into russian")
+user_prompt = st.text_input("Enter your user's prompt",value="Hello, how are you?")
+
+messages = [
+    {"role": "system", "content": system_prompt},
+
+    {"role": "user", "content": user_prompt}
+  ]
+
+output = ask_gpt(messages)
+
+
+st.header("Output")
+st.write(output)
+
+st.header("Metafeatures")
+
+
+df = pd.DataFrame([
+    runner.run(system_prompt),
+    runner.run(user_prompt),
+    runner.run(output)])
+df["prompt"] = ["system","user","output"]
+df = df.set_index("prompt")
+
+st.dataframe(df)
+
+st.header("Chat Monitoring")
+
+st.subheader("Chat")
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    # 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"):
+        message_placeholder = st.empty()
+        full_response = ""
+        assistant_response = ask_gpt(messages=st.session_state.messages)
+        # Simulate stream of response with milliseconds delay
+        for chunk in assistant_response.split():
+            full_response += chunk + " "
+            time.sleep(0.05)
+            # Add a blinking cursor to simulate typing
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+
+st.subheader("Chat Logs")
+
+user_messages = [message["content"] for message in st.session_state.messages if message["role"] == "user"]  
+assistant_messages = [message["content"] for message in st.session_state.messages if message["role"] == "assistant"]    
+# st.write("User Messages",user_messages)
+# st.write("Assistant Messages",assistant_messages)
+
+user_df = pd.DataFrame([runner.run(user_prompt) for user_prompt in user_messages])
+user_df["prompt"] = user_messages
+user_df.columns = 'user_' + user_df.columns.values
+# st.dataframe(user_df)
+
+
+
+assistant_df = pd.DataFrame([runner.run(assistant_prompt) for assistant_prompt in assistant_messages])
+assistant_df["prompt"] = assistant_messages
+assistant_df.columns = 'assistant_' + assistant_df.columns.values
+# st.dataframe(assistant_df)
+
+
+st.subheader("Logs Metafeatures")
+st.dataframe(pd.concat([user_df,assistant_df],axis=1))

requirements.txt

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+elemeta
+openai