Update app.py

app.py

@@ -10,12 +10,12 @@ from langchain.chains import RetrievalQA
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains import LLMChain
-from langchain.memory import ConversationBufferMemory
 
 # Set persistent storage path
 PERSISTENT_DIR = "vector_db"
 
 def initialize_vector_db():
+    # Check if vector database already exists in persistent storage
     if os.path.exists(PERSISTENT_DIR) and os.listdir(PERSISTENT_DIR):
         embeddings = HuggingFaceEmbeddings()
         vector_db = Chroma(persist_directory=PERSISTENT_DIR, embedding_function=embeddings)
@@ -46,15 +46,6 @@ def initialize_vector_db():
     vector_db.persist()
     return documents, vector_db
 
-# Initialize session state for chat history and memory
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-if "memory" not in st.session_state:
-    st.session_state.memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        return_messages=True
-    )
-
 # System instructions for the LLM
 system_prompt = """You are an expert organic farming consultant with specialization in Agro-Homeopathy. When providing suggestions and remedies:
 1. Always specify medicine potency as 6c unless the uploaded text mentions some other value explicitly
@@ -64,25 +55,36 @@ system_prompt = """You are an expert organic farming consultant with specializat
 
 api_key1 = os.getenv("api_key")
 
-# Page configuration
 start_time = time.time()
 st.set_page_config(page_title="Dr. Radha: The Agro-Homeopath", page_icon="🚀", layout="wide")
 
-# CSS styling
+# CSS for dark green background and white text
 st.markdown("""
 <style>
+    /* Set background color for entire app */
     .stApp {
         background-color: #1B4D3E !important;
         color: white !important;
     }
+    
+    /* Style input fields */
     .stTextInput>div>div>input {
         color: black !important;
         background-color: rgba(255,255,255,0.1) !important;
     }
+    
+    /* Style buttons */
     .stButton>button {
         color: black !important;
         background-color: yellow !important;
     }
+    
+    }
+</style>
+""", unsafe_allow_html=True)
+
+st.markdown("""
+<style>
     #the-title {
         text-align: center;
         font-size: 24px;
@@ -91,25 +93,10 @@ st.markdown("""
 </style>
 """, unsafe_allow_html=True)
 
-# Sidebar
-st.sidebar.title("Chat History")
-
-# Clear Session button
-if st.sidebar.button("Clear Session"):
-    st.session_state.chat_history = []
-    st.session_state.memory.clear()
-    st.experimental_rerun()
-
-# Display chat history in sidebar
-for i, chat in enumerate(st.session_state.chat_history):
-    st.sidebar.text(f"Q{i+1}: {chat['question']}")
-    if st.sidebar.button(f"Go to Q{i+1}", key=f"btn_{i}"):
-        st.session_state.query = chat['question']
-
-# Main content
 st.title("🌿 Dr. Radha: AI-Powered Organic Farming Consultant")
 st.subheader("Specializing in Agro-Homeopathy | Free Consultation")
 
+# Add information request message
 st.markdown("""
 Please provide complete details about the issue, including:
 - Detailed description of plant problem
@@ -119,7 +106,7 @@ Please provide complete details about the issue, including:
 human_image = "human.png"
 robot_image = "bot.jpg"
 
-# Set up Groq API
+# Set up Groq API with temperature 0.7
 llm = ChatGroq(
     api_key=api_key1, 
     max_tokens=None, 
@@ -130,16 +117,18 @@ llm = ChatGroq(
 )
 
 embeddings = HuggingFaceEmbeddings()
+end_time = time.time()
+print(f"Setting up Groq LLM & Embeddings took {end_time - start_time:.4f} seconds")
 
-# Initialize session state for documents and vector_db
+# Initialize session state
 if "documents" not in st.session_state:
     st.session_state["documents"] = None
 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"] = ""
-
-# Initialize vector database
+    
+start_time = time.time()
 if st.session_state["documents"] is None or st.session_state["vector_db"] is None:
     with st.spinner("Loading data..."):
         documents, vector_db = initialize_vector_db()
@@ -149,13 +138,16 @@ else:
     documents = st.session_state["documents"]
     vector_db = st.session_state["vector_db"]
 
+end_time = time.time()
+print(f"Loading and processing PDFs & vector database took {end_time - start_time:.4f} seconds")
+
+start_time = time.time()
 retriever = vector_db.as_retriever()
 
-# Prompt templates
 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}
-Chat History: {chat_history}
+
 Question: {question}
 
 Provide your response in the following format:
@@ -189,15 +181,27 @@ Remember to maintain a professional, clear tone and ensure all medicine recommen
 
 Answer:"""
 
+# Create the QA chain with correct variables
+qa = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    chain_type_kwargs={
+        "prompt": PromptTemplate(
+            template=prompt_template,
+            input_variables=["context", "question"]
+        )
+    }
+)
 
+# 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.
 
-Chat History: {chat_history}
 Question: {question}
 
-Provide your response in the following format:
+Format your response as follows:
 Analysis: Analyze the described plant condition
-Treatment: Recommend relevant organic farming principles and specific homeopathic medicine(s) with exact potency and repetition frequency. Suggest a maximum of 4 medicines in the order of relevance for any single problem. 
+Treatment: Recommend relevant organic farming principles and specific homeopathic medicine(s) with exact potency and repetition frequency. Suggest a maximum of 4 medicines in the order of relevance for any single problem.
 Instructions for Use:
 Small Plots or Gardens: Make sure your dispensing equipment is not contaminated with
 other chemicals or fertilisers as these may antidote the energetic effects of the treatment—
@@ -214,95 +218,64 @@ water or steam clean before use if necessary. Avoid using other chemicals or fer
 10 days following treatment.
 Dosage rates are approximate and may vary according to different circumstances and
 experiences. Suggested doses are:
-10 pills or 10ml/10 litre on small areas,
-500 pills or 125ml/500l per hectare,
-1000 pills or 250ml/500l per hectare,
-2500 pills or 500ml/500l per hectare,
+10 pills or 10ml/10 litre on small areas
+500 pills or 125ml/500l per hectare
+1000 pills or 250ml/500l per hectare
+2500 pills or 500ml/500l per hectare
 Add pills or liquid to your water and mix (with a stick if necessary for large containers).
 
 Recommendations: Provide couple of key pertinent recommendations based on the query
 
-Remember to maintain a professional, clear tone and ensure all medicine recommendations include specific potency.
+Maintain a professional tone and ensure all medicine recommendations include specific potency.
 
 Answer:"""
 
-# Create QA chain with memory
-qa = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=retriever,
-    memory=st.session_state.memory,
-    chain_type_kwargs={
-        "prompt": PromptTemplate(
-            template=prompt_template,
-            input_variables=["context", "question", "chat_history"]
-        )
-    }
-)
+fallback_prompt = PromptTemplate(template=fallback_template, input_variables=["question"])
+fallback_chain = LLMChain(llm=llm, prompt=fallback_prompt)
 
-# Create fallback chain with memory
-fallback_prompt = PromptTemplate(
-    template=fallback_template, 
-    input_variables=["question", "chat_history"]
-)
-fallback_chain = LLMChain(
-    llm=llm, 
-    prompt=fallback_prompt,
-    memory=st.session_state.memory
-)
-
-# Chat container
 chat_container = st.container()
 
-# Query form
+st.markdown("""
+<style>
+.stButton>button {
+    color: black !important;
+    background-color: yellow !important;
+}
+</style>
+""", unsafe_allow_html=True)
+
 with st.form(key='query_form'):
-    query = st.text_input("Ask your question:", value=st.session_state.get("query", ""))
+    query = st.text_input("Ask your question:", value="")
     submit_button = st.form_submit_button(label='Submit')
 
-# Handle form submission
+end_time = time.time()
+#print(f"Setting up retrieval chain took {end_time - start_time:.4f} seconds")
+start_time = time.time()
+
 if submit_button and query:
     with st.spinner("Generating response..."):
-        # Add query to chat history
-        st.session_state.chat_history.append({
-            "question": query,
-            "timestamp": time.time()
-        })
-        
-        # Get response with memory context
-        result = qa({
-        "question": query,
-        "chat_history": st.session_state.memory.chat_memory.messages if st.session_state.memory else []
-        })
-        
+        result = qa({"query": query})  
         if result['result'].strip() == "":
+            # If no result from PDF, use fallback chain
             fallback_result = fallback_chain.run(query)
             response = fallback_result
         else:
             response = result['result']
-        
-        # Store the interaction in memory
-        st.session_state.memory.save_context(
-            {"input": query},
-            {"output": response}
-        )
-
-    # Display chat messages
-    for chat in st.session_state.chat_history:
-        col1, col2 = st.columns([1, 10])
-        with col1:
-            st.image(human_image, width=80)
-        with col2:
-            st.markdown(f"{chat['question']}")
-            
-        col1, col2 = st.columns([1, 10])
-        with col1:
-            st.image(robot_image, width=80)
-        with col2:
-            if chat['question'] == query:
-                st.markdown(f"{response}")
-            else:
-                prev_response = st.session_state.memory.load_memory_variables({})
-                st.markdown(f"{prev_response.get('chat_history', '')}")
 
+    col1, col2 = st.columns([1, 10])
+    with col1:
+        st.image(human_image, width=80)
+    with col2:
+        st.markdown(f"{query}")
+    col1, col2 = st.columns([1, 10])
+    with col1:
+        st.image(robot_image, width=80)
+    with col2:
+        st.markdown(f"{response}")
+    
     st.markdown("---")
+    
     st.session_state["query"] = ""
+
+end_time = time.time()
+print(f"Actual query took {end_time - start_time:.4f} seconds")