Create app.py

app.py

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+import os
+os.environ["OPENAI_API_KEY"] 
+
+from llama_index.llms.openai import OpenAI
+from llama_index.core.schema import MetadataMode
+import openai
+from openai import OpenAI as OpenAIOG
+import logging
+import sys
+llm = OpenAI(temperature=0.0, model="gpt-3.5-turbo")
+client = OpenAIOG()
+
+from deep_translator import GoogleTranslator
+
+# Load index
+from llama_index.core import VectorStoreIndex
+from llama_index.core import StorageContext
+from llama_index.core import load_index_from_storage
+storage_context = StorageContext.from_defaults(persist_dir="parse_metadata")
+index = load_index_from_storage(storage_context)
+query_engine = index.as_query_engine(similarity_top_k=3, llm=llm)
+retriever = index.as_retriever(similarity_top_k = 3)
+
+import gradio as gr
+import re
+import json
+from datetime import datetime
+
+acknowledgment_keywords = ["thanks", "thank you", "thx", "ok", "okay", "great", "got it",
+                              "appreciate", "good", "makes sense"]
+follow_up_keywords = ["but", "also", "and", "what", "how", "why", "when", "is", "?"]
+greeting_keywords = ["hi", "hello", "hey", "how's it", "what's up", "yo", "howdy"]
+
+def contains_exact_word_or_phrase(text, keywords):
+    text = text.lower()
+    for keyword in keywords:
+        if re.search(r'\b' + re.escape(keyword) + r'\b', text):
+            return True
+    return False
+
+def contains_greeting(question):
+    # Check if the question contains acknowledgment keywords
+    return contains_exact_word_or_phrase(question, greeting_keywords)
+
+def contains_acknowledgment(question):
+    # Check if the question contains acknowledgment keywords
+    return contains_exact_word_or_phrase(question, acknowledgment_keywords)
+
+def contains_follow_up(question):
+    # Check if the question contains follow-up indicators
+    return contains_exact_word_or_phrase(question, follow_up_keywords)
+
+def convert_to_date(date_str):
+    return datetime.strptime(date_str, "%Y%m%d")
+
+def idahun(question: str, conversation_history: list[str]):
+
+    # Get conversation history
+    context = " ".join([item["user"] + " " + item["chatbot"] for item in conversation_history])
+
+    source0 = "RAG not run"
+    source1 = "RAG not run"
+    source2 = "RAG not run"
+    
+    ## Process greeting
+    # greet_response = process_greeting_response(question)
+    if contains_greeting(question) and not contains_follow_up(question):
+            greeting = (
+                f" The user previously asked and answered the following: {context}. "
+                f" The user just provided the following greeting: {question}. "
+                "Please respond accordingly."
+            )
+            completion = client.chat.completions.create(
+              model="gpt-4o",
+                messages=[
+                {"role": "user", "content": greeting}
+              ]
+            )
+            reply_to_user = completion.choices[0].message.content
+            conversation_history.append({"user": question, "chatbot": reply_to_user})   
+            return reply_to_user, source0, source1, source2, conversation_history
+    
+    ## Process acknowledgment
+    if contains_acknowledgment(question) and not contains_follow_up(question):
+            acknowledgment = (
+                f" The user previously asked and answered the following: {context}. "
+                f" The user just provided the following acknowledgement: {question}. "
+                "Please respond accordingly in English."
+            )
+            completion = client.chat.completions.create(
+              model="gpt-4o",
+                messages=[
+                {"role": "user", "content": acknowledgment}
+              ]
+            )
+            reply_to_user = completion.choices[0].message.content
+            conversation_history.append({"user": question, "chatbot": reply_to_user})   
+            return reply_to_user, source0, source1, source2, conversation_history
+
+    ## If not greeting or acknowledgement, then proceed with RAG
+    
+    # Retrieve sources
+    sources = retriever.retrieve(question)
+    source0 = sources[0].text
+    source1 = sources[1].text
+    source2 = sources[2].text
+
+
+    background = ("The person who asked the question is a person living with HIV."
+          " They are asking questions about HIV. Do not talk about anything that is not related to HIV. "
+          " Recognize that they already have HIV and do not suggest that they have to get tested"
+          " for HIV or take post-exposure prophylaxis, as that is not relevant, though their partners perhaps should."  
+          " Do not suggest anything that is not relevant to someone who already has HIV."
+          " Do not mention in the response that the person is living with HIV.")
+
+    # Combine into final prompt - user background, conversation history, new question, retrieved sources
+    question_final = (
+        f" The user previously asked and answered the following: {context}. "
+        f" The user just asked the following question: {question}."
+        f" Please use the following content to generate a response: {source0} {source1} {source2}."
+        f" Please consider the following background information when generating a response: {background}."
+        " Keep answers brief and limited to the question that was asked."
+        " If they share a greeting, just greet them in return and ask if they have a question."
+        " Do not change the subject or address anything the user didn't directly ask about."
+        " If they respond with an acknowledgement, simply thank them."
+        " Do not discuss anything other than HIV. If they ask a question that is not about HIV, respond that"
+        " you are only able to discuss HIV."
+        " Keep the response to under 50 words and use simple language. The person asking the question does not know technical terms."
+    )
+
+    # Generate response
+    completion = client.chat.completions.create(
+      model="gpt-4o",
+        messages=[
+        {"role": "user", "content": question_final}
+      ]
+    )
+    # Collect response
+    reply_to_user = completion.choices[0].message.content
+
+    # add question and reply to conversation history
+    conversation_history.append({"user": question, "chatbot": reply_to_user})  
+
+    return reply_to_user, source0, source1, source2, conversation_history
+
+demo = gr.Interface(
+    title = "Idahun Chatbot Demo (English)",
+    fn=idahun,
+    inputs=["text", gr.State(value=[])],
+    outputs = [
+    gr.Textbox(label="Chatbot Response", type="text"),
+    gr.Textbox(label="Source 1", max_lines = 10, autoscroll = False, type="text"),
+    gr.Textbox(label="Source 2", max_lines = 10, autoscroll = False, type="text"),
+    gr.Textbox(label="Source 3", max_lines = 10, autoscroll = False, type="text"),
+    gr.State()
+],
+)
+
+demo.launch()