Update app.py

app.py

@@ -1,5 +1,7 @@
 import streamlit as st
 import os
+from langchain.memory import ConversationBufferMemory
+import uuid
 from dotenv import load_dotenv
 import time
 from langchain.vectorstores import Chroma
@@ -42,8 +44,16 @@ st.markdown("""
         background-color: rgba(255, 255, 255, 0.05) !important;
     }
     
-    /* Text colors */
-    .stMarkdown, .stText, .stTitle, .stHeader, .stSubheader {
+    /* Style all text elements in white */
+    .stMarkdown, .stText, .stTitle, .stHeader, .stSubheader, 
+    .stTextInput label, .stSelectbox label, .st-emotion-cache-10trblm,
+    .st-emotion-cache-1a7jz76, .st-emotion-cache-1629p8f,
+    [data-testid="stTitle"], [data-testid="stSubheader"] {
+        color: white !important;
+    }
+    
+    /* Additional specific selectors for title and subheader */
+    h1, h2, h3 {
         color: white !important;
     }
     
@@ -164,7 +174,13 @@ if "vector_db" not in st.session_state:
     st.session_state["vector_db"] = None
 if "query" not in st.session_state:
     st.session_state["query"] = ""
-    
+if "session_id" not in st.session_state:
+    st.session_state.session_id = str(uuid.uuid4())
+if "conversation_memory" not in st.session_state:
+    st.session_state.conversation_memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
 start_time = time.time()
 if st.session_state["documents"] is None or st.session_state["vector_db"] is None:
     with st.spinner("Loading data..."):
@@ -183,6 +199,7 @@ retriever = vector_db.as_retriever()
 
 prompt_template = """As an expert organic farming consultant with specialization in Agro-Homeopathy, analyze the following context and question to provide a clear, structured response.
 Context: {context}
+Previous conversation:{chat_history}
 Question: {question}
 Provide your response in the following format:
 Analysis: Analyze the described plant condition
@@ -217,16 +234,18 @@ qa = RetrievalQA.from_chain_type(
     llm=llm,
     chain_type="stuff",
     retriever=retriever,
+    memory=st.session_state.conversation_memory,
     chain_type_kwargs={
         "prompt": PromptTemplate(
             template=prompt_template,
-            input_variables=["context", "question"]
+            input_variables=["context", "question", "chat_history"]
         )
     }
 )
 
 # Create a separate LLMChain for fallback
 fallback_template = """As an expert organic farming consultant with specialization in Agro-Homeopathy, analyze the following context and question to provide a clear, structured response.
+Previous conversation:{chat_history}
 Question: {question}
 Format your response as follows:
 Analysis: Analyze the described plant condition
@@ -256,8 +275,38 @@ Recommendations: Provide couple of key pertinent recommendations based on the qu
 Maintain a professional tone and ensure all medicine recommendations include specific potency.
 Answer:"""
 
-fallback_prompt = PromptTemplate(template=fallback_template, input_variables=["question"])
-fallback_chain = LLMChain(llm=llm, prompt=fallback_prompt)
+fallback_prompt = PromptTemplate(
+    template=fallback_template,
+    input_variables=["question", "chat_history"]
+)
+fallback_chain = LLMChain(
+    llm=llm,
+    prompt=fallback_prompt,
+    memory=st.session_state.conversation_memory
+)
+
+with st.sidebar:
+    st.title("Conversation History")
+    
+    if st.button("New Session 🔄"):
+        # Clear all conversation related session states
+        st.session_state.messages = []
+        st.session_state.messages.append({
+            "role": "assistant",
+            "content": "👋 Hello! I'm Dr. Radha, your AI-powered Organic Farming Consultant. How can I assist you today?"
+        })
+        st.session_state.conversation_memory.clear()
+        st.session_state.session_id = str(uuid.uuid4())
+        st.rerun()
+        # Display conversation history
+    st.subheader("Previous Conversations")
+    for message in st.session_state.messages[1:]:  # Skip the initial greeting
+        if message["role"] == "user":
+            st.text("You:")
+            st.text_area("", message["content"], height=50, disabled=True, key=f"hist_{uuid.uuid4()}")
+        else:
+            st.text("Dr. Radha:")
+            st.text_area("", message["content"], height=100, disabled=True, key=f"hist_{uuid.uuid4()}")
 
 # Replace your existing chat container and form section with this:
 chat_container = st.container()
@@ -275,6 +324,9 @@ with st.form(key='query_form', clear_on_submit=True):
     )
     submit_button = st.form_submit_button(label='Send Message 📤')
 
+human_image = "human.png"
+robot_image = "bot.jpg"
+
 if submit_button and query:
     # Add user message to history
     st.session_state.messages.append({"role": "user", "content": query})
@@ -283,18 +335,29 @@ if submit_button and query:
     with st.chat_message("user", avatar="👤"):
         st.markdown(query)
     
-    # Show typing indicator while generating response
-    with st.chat_message("assistant", avatar="🌿"):
+    # Show typing indicator while generating response "🌿"
+    with st.chat_message("assistant", avatar=robot_image):  
         with st.status("Analyzing your query...", expanded=True):
             st.write("🔍 Retrieving relevant information...")
             st.write("📝 Generating personalized response...")
             
             # Generate response
-            result = qa({"query": query})
+            result = qa({
+            "query": query,
+            "chat_history": st.session_state.conversation_memory.load_memory_variables({})["chat_history"]
+            })
+            
             if result['result'].strip() == "":
-                response = fallback_chain.run(query)
+                response = fallback_chain.run(
+                    question=query,
+                    chat_history=st.session_state.conversation_memory.load_memory_variables({})["chat_history"])
             else:
                 response = result['result']
+
+            st.session_state.conversation_memory.save_context(
+                {"input": query},
+                {"output": response}
+            )
             
             # Display final response
             st.markdown(response)