Create app.py

app.py

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+import os 
+
+from langchain_core.prompts import ChatPromptTemplate
+from langsmith import Client, traceable
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langsmith import Client, traceable
+from langchain_core.output_parsers import StrOutputParser
+
+from langchain_nomic.embeddings import NomicEmbeddings
+from langchain_groq import ChatGroq
+
+load_dotenv()
+
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+HF_API_KEY = os.getenv("HF_API_KEY")
+COHERE_API_KEY = os.getenv("COHERE_API_KEY")
+
+
+
+
+LANGSMITH_TRACING="true"
+LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+LANGSMITH_API_KEY=os.getenv("LANGSMITH_API_KEY")
+LANGSMITH_PROJECT="pr-smug-rancher-51"
+
+model_name="llama-3.1-70b-versatile"
+llm = ChatGroq(
+    temperature=0,
+    model= "llama-3.3-70b-versatile", #"llama-3.1-70b-versatile", #"llama3-70b-8192",
+    api_key=GROQ_API_KEY,
+
+)
+
+
+@traceable
+def get_answer(question):
+
+  prompt = ChatPromptTemplate.from_messages([
+      ("system", "You are a medical expert called Dr.Med! Here are some info about cancer: {facts}"),
+      ("user", "{question}")
+  ])
+
+  chain = prompt | llm
+
+  parser = StrOutputParser()
+
+  chain = prompt | llm | parser
+
+  answer= chain.invoke({"question": question, "facts": fake_db_retrieval()})
+  return answer
+
+
+
+
+
+embedding_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+db = "db1"
+from langchain.vectorstores import Chroma
+
+vector_store = Chroma(
+    collection_name="chromadb3",
+    persist_directory=db,
+    embedding_function=embedding_model,
+)
+
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain.chains import RetrievalQA
+
+conversational_memory = ConversationBufferWindowMemory(
+    memory_key='chat_history',
+    k=5, #Number of messages stored in memory
+    return_messages=True #Must return the messages in the response.
+)
+
+qa = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=vector_store.as_retriever(k=5)
+)
+
+from langchain.agents import Tool
+
+#Defining the list of tool objects to be used by LangChain.
+tools = [
+   Tool(
+       name='Medical_KB',
+       func=qa.run,
+       description=(
+           'use this tool when answering medical knowledge queries to get '
+           'more information about the topic'
+       )
+   )
+]
+
+from langchain.agents import create_react_agent
+from langchain import hub
+
+prompt = hub.pull("hwchase17/react-chat")
+agent = create_react_agent(
+   tools=tools,
+   llm=llm,
+   prompt=prompt,
+)
+
+
+
+# Create an agent executor by passing in the agent and tools
+from langchain.agents import AgentExecutor
+agent_executor = AgentExecutor(agent=agent,
+                               tools=tools,
+                               verbose=True,
+                               memory=conversational_memory,
+                               max_iterations=30,
+                               max_execution_time=600,
+                               #early_stopping_method='generate',
+                               handle_parsing_errors=True
+                               )
+
+
+# Function for continuing the conversation
+import streamlit as st
+
+# Function for continuing the conversation
+def continue_conversation(input, history):
+    # Invoke the agent and get the response
+    response = agent_executor.invoke({"input": input})
+    output = response['output']
+
+    # Prepend the new input and output to the history (latest conversation comes first)
+    history.insert(0, {"role": "Patient", "message": input})
+    history.insert(0, {"role": "Doctor", "message": output})
+
+    # Return the current response and the full history (hidden state)
+    return output, history
+
+# Streamlit UI
+def main():
+    st.set_page_config(page_title="Medical Chatbot", page_icon="👨‍⚕️")
+    st.title("Medical Chatbot")
+
+    # Initialize the conversation history
+    if 'history' not in st.session_state:
+        st.session_state.history = []
+
+    # Sidebar for memory display
+    with st.sidebar:
+        st.header("Conversation History")
+        st.write("This section contains the conversation history.")
+
+    # Create a container for the chat
+    chat_container = st.container()
+
+    # Display the chat history with the latest conversation at the top
+    for chat in st.session_state.history:
+        if chat['role'] == 'Patient':
+            chat_container.markdown(f"**Patient:** {chat['message']}")
+        else:
+            chat_container.markdown(f"**Doctor:** {chat['message']}")
+
+    # User input text box at the bottom
+    user_input = st.text_input("Ask a question:", key="input", placeholder="Describe your symptoms or medical questions ?")
+
+    if user_input:
+        # Get the response and update the conversation history
+        output, updated_history = continue_conversation(user_input, st.session_state.history)
+        
+        # Update the session state with the new history
+        st.session_state.history = updated_history
+
+    # Display memory of past conversation in an expandable section
+    with st.expander("Memory", expanded=True):
+        for chat in st.session_state.history:
+            st.write(f"**{chat['role']}:** {chat['message']}")
+
+if __name__ == "__main__":
+    main()