diff --git a/app/docker-compose.yml b/app/docker-compose.yml
index 69ae235..fb7640a 100644
--- a/app/docker-compose.yml
+++ b/app/docker-compose.yml
@@ -3,8 +3,8 @@ version: "3.9"
 services:
   streamlit:
     build: ./streamlit
+    platform: linux/amd64
     ports:
       - "8501:8501"
     volumes:
       - ./llmops/src/rag_cot/chroma_db:/app/llmops/src/rag_cot/chroma_db
-
diff --git a/app/streamlit/Dockerfile b/app/streamlit/Dockerfile
index 5890cba..b335644 100644
--- a/app/streamlit/Dockerfile
+++ b/app/streamlit/Dockerfile
@@ -2,9 +2,9 @@ FROM python:3.11-slim
 
 WORKDIR /app/streamlit
 
-COPY Pipfile ./
+COPY requirements.txt ./
 
-RUN pip install pipenv && pipenv install --system --deploy
+RUN pip install --no-cache-dir -r requirements.txt
 
 COPY Chatbot.py .
 COPY .env .
diff --git a/app/streamlit/app_test.py b/app/streamlit/app_test.py
index a4e25fd..f0ff4cf 100644
--- a/app/streamlit/app_test.py
+++ b/app/streamlit/app_test.py
@@ -25,22 +25,27 @@ def create_chat_completion(response: str, role: str = "assistant") -> ChatComple
     )
 
 
-# @patch("openai.resources.chat.Completions.create")
-# def test_Chatbot(openai_create):
-#     at = AppTest.from_file("Chatbot.py").run()
-#     assert not at.exception
-#     at.chat_input[0].set_value("Do you know any jokes?").run()
-#     assert at.info[0].value == "Please add your OpenAI API key to continue."
-
-#     JOKE = "Why did the chicken cross the road? To get to the other side."
-#     openai_create.return_value = create_chat_completion(JOKE)
-#     at.text_input(key="chatbot_api_key").set_value("sk-...")
-#     at.chat_input[0].set_value("Do you know any jokes?").run()
-#     print(at)
-#     assert at.chat_message[1].markdown[0].value == "Do you know any jokes?"
-#     assert at.chat_message[2].markdown[0].value == JOKE
-#     assert at.chat_message[2].avatar == "assistant"
-#     assert not at.exception
+@patch("langchain_deepseek.ChatDeepSeek.__call__")
+@patch("langchain_google_genai.ChatGoogleGenerativeAI.invoke")
+@patch("langchain_community.llms.moonshot.Moonshot.__call__")
+def test_Chatbot(moonshot_llm, gemini_llm, deepseek_llm):
+    at = AppTest.from_file("Chatbot.py").run()
+    assert not at.exception
+    
+    QUERY = "What is the best treatment for hypertension?"
+    RESPONSE = "The best treatment for hypertension is..."
+    
+    deepseek_llm.return_value.content = RESPONSE
+    gemini_llm.return_value.content = RESPONSE
+    moonshot_llm.return_value = RESPONSE
+    
+    at.chat_input[0].set_value(QUERY).run()
+    
+    assert any(mock.called for mock in [deepseek_llm, gemini_llm, moonshot_llm])
+    assert at.chat_message[1].markdown[0].value == QUERY
+    assert at.chat_message[2].markdown[0].value == RESPONSE
+    assert at.chat_message[2].avatar == "assistant"
+    assert not at.exception
 
 
 @patch("langchain.llms.OpenAI.__call__")
diff --git a/app/streamlit/pages/1_File_Q&A.py b/app/streamlit/pages/1_File_Q&A.py
deleted file mode 100644
index 417474c..0000000
--- a/app/streamlit/pages/1_File_Q&A.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import streamlit as st
-import anthropic
-
-with st.sidebar:
-    anthropic_api_key = st.text_input("Anthropic API Key", key="file_qa_api_key", type="password")
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/1_File_Q%26A.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
-
-st.title("📝 File Q&A with Anthropic")
-uploaded_file = st.file_uploader("Upload an article", type=("txt", "md"))
-question = st.text_input(
-    "Ask something about the article",
-    placeholder="Can you give me a short summary?",
-    disabled=not uploaded_file,
-)
-
-if uploaded_file and question and not anthropic_api_key:
-    st.info("Please add your Anthropic API key to continue.")
-
-if uploaded_file and question and anthropic_api_key:
-    article = uploaded_file.read().decode()
-    prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n<article>
-    {article}\n\n</article>\n\n{question}{anthropic.AI_PROMPT}"""
-
-    client = anthropic.Client(api_key=anthropic_api_key)
-    response = client.completions.create(
-        prompt=prompt,
-        stop_sequences=[anthropic.HUMAN_PROMPT],
-        model="claude-v1",  # "claude-2" for Claude 2 model
-        max_tokens_to_sample=100,
-    )
-    st.write("### Answer")
-    st.write(response.completion)
diff --git a/app/streamlit/pages/2_Chat_with_search.py b/app/streamlit/pages/2_Chat_with_search.py
deleted file mode 100644
index 399c582..0000000
--- a/app/streamlit/pages/2_Chat_with_search.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import streamlit as st
-
-from langchain.agents import initialize_agent, AgentType
-from langchain.callbacks import StreamlitCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.tools import DuckDuckGoSearchRun
-
-with st.sidebar:
-    openai_api_key = st.text_input(
-        "OpenAI API Key", key="langchain_search_api_key_openai", type="password"
-    )
-    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
-
-st.title("🔎 LangChain - Chat with search")
-
-"""
-In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
-Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
-"""
-
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [
-        {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
-    ]
-
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["content"])
-
-if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-        st.stop()
-
-    llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, streaming=True)
-    search = DuckDuckGoSearchRun(name="Search")
-    search_agent = initialize_agent(
-        [search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True
-    )
-    with st.chat_message("assistant"):
-        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        st.write(response)
diff --git a/app/streamlit/pages/4_Langchain_PromptTemplate.py b/app/streamlit/pages/4_Langchain_PromptTemplate.py
deleted file mode 100644
index 3755419..0000000
--- a/app/streamlit/pages/4_Langchain_PromptTemplate.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import streamlit as st
-from langchain.llms import OpenAI
-from langchain.prompts import PromptTemplate
-
-st.title("🦜🔗 Langchain - Blog Outline Generator App")
-
-openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
-
-
-def blog_outline(topic):
-    # Instantiate LLM model
-    llm = OpenAI(model_name="text-davinci-003", openai_api_key=openai_api_key)
-    # Prompt
-    template = "As an experienced data scientist and technical writer, generate an outline for a blog about {topic}."
-    prompt = PromptTemplate(input_variables=["topic"], template=template)
-    prompt_query = prompt.format(topic=topic)
-    # Run LLM model
-    response = llm(prompt_query)
-    # Print results
-    return st.info(response)
-
-
-with st.form("myform"):
-    topic_text = st.text_input("Enter prompt:", "")
-    submitted = st.form_submit_button("Submit")
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-    elif submitted:
-        blog_outline(topic_text)
diff --git a/app/streamlit/pages/5_Chat_with_user_feedback.py b/app/streamlit/pages/5_Chat_with_user_feedback.py
deleted file mode 100644
index 5f58f13..0000000
--- a/app/streamlit/pages/5_Chat_with_user_feedback.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from openai import OpenAI
-import streamlit as st
-from streamlit_feedback import streamlit_feedback
-import trubrics
-
-with st.sidebar:
-    openai_api_key = st.text_input("OpenAI API Key", key="feedback_api_key", type="password")
-    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/5_Chat_with_user_feedback.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
-
-st.title("📝 Chat with feedback (Trubrics)")
-
-"""
-In this example, we're using [streamlit-feedback](https://github.com/trubrics/streamlit-feedback) and Trubrics to collect and store feedback
-from the user about the LLM responses.
-"""
-
-if "messages" not in st.session_state:
-    st.session_state.messages = [
-        {"role": "assistant", "content": "How can I help you? Leave feedback to help me improve!"}
-    ]
-if "response" not in st.session_state:
-    st.session_state["response"] = None
-
-messages = st.session_state.messages
-for msg in messages:
-    st.chat_message(msg["role"]).write(msg["content"])
-
-if prompt := st.chat_input(placeholder="Tell me a joke about sharks"):
-    messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-        st.stop()
-    client = OpenAI(api_key=openai_api_key)
-    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
-    st.session_state["response"] = response.choices[0].message.content
-    with st.chat_message("assistant"):
-        messages.append({"role": "assistant", "content": st.session_state["response"]})
-        st.write(st.session_state["response"])
-
-if st.session_state["response"]:
-    feedback = streamlit_feedback(
-        feedback_type="thumbs",
-        optional_text_label="[Optional] Please provide an explanation",
-        key=f"feedback_{len(messages)}",
-    )
-    # This app is logging feedback to Trubrics backend, but you can send it anywhere.
-    # The return value of streamlit_feedback() is just a dict.
-    # Configure your own account at https://trubrics.streamlit.app/
-    if feedback and "TRUBRICS_EMAIL" in st.secrets:
-        config = trubrics.init(
-            email=st.secrets.TRUBRICS_EMAIL,
-            password=st.secrets.TRUBRICS_PASSWORD,
-        )
-        collection = trubrics.collect(
-            component_name="default",
-            model="gpt",
-            response=feedback,
-            metadata={"chat": messages},
-        )
-        trubrics.save(config, collection)
-        st.toast("Feedback recorded!", icon="📝")
diff --git a/app/streamlit/requirements.txt b/app/streamlit/requirements.txt
index cac1b42..a7df356 100644
--- a/app/streamlit/requirements.txt
+++ b/app/streamlit/requirements.txt
@@ -11,4 +11,4 @@ python-decouple
 langchain_google_genai
 langchain-deepseek
 sentence_transformers
-mlflow
\ No newline at end of file
+watchdog
\ No newline at end of file