Update app.py

app.py

@@ -1,8 +1,4 @@
-import base64
-from itertools import cycle
-import time
 import streamlit as st
-from streamlit_carousel import carousel
 import os
 from dotenv import load_dotenv
 import time
@@ -15,124 +11,11 @@ from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains import LLMChain
 
-st.set_page_config(page_title="Dr. Radha: The Agro-Homeopath", page_icon="🌿")
-
 # Set persistent storage path
 PERSISTENT_DIR = "vector_db"
 
-
-# Add custom CSS to maintain consistent image height
-st.markdown("""
-<style>
-.stApp {
-    background-color: #1B4D3E !important;
-    color: white !important;
-    max-width: 100%;
-    padding: 1rem;
-}
-
-/* Make carousel images equal size */
-.carousel {
-    width: 100%;
-    height: 400px;
-}
-
-.carousel img {
-    width: 100%;
-    height: 400px;
-    object-fit: cover;
-    object-position: center;
-}
-
-/* Style the form and button */
-.stButton > button {
-    color: black !important;
-    background-color: yellow !important;
-    width: 100%;
-    padding: 0.5rem;
-}
-
-/* Make text input and output full width */
-.stTextInput > div > div > input {
-    color: black !important;
-    background-color: rgba(255,255,255,0.1) !important;
-    width: 100%;
-}
-
-/* Style the chat container */
-.chat-container {
-    width: 100%;
-    max-width: 1200px;
-    margin: 0 auto;
-    padding: 1rem;
-}
-
-/* Make title and headers full width */
-.stTitle, .stHeader {
-    width: 100%;
-    text-align: center;
-    margin: 1rem 0;
-}
-</style>
-""", unsafe_allow_html=True)
-
-# Define image paths
-HEADER_IMAGE = "i1.jpg"  # Organic farming landscape
-SIDE_IMAGE = "i2.JPG"       # Medicinal plants/herbs
-FOOTER_IMAGE = "i3.JPG"     # Sustainable farming practices
-
-# Define the images list before using the carousel
-images = [
-    dict(
-        title="",
-        text="",
-        img="i1.jpg",
-        imgStyle={
-            "width": "100%",
-            "height": "400px",
-            "objectFit": "cover",
-            "objectPosition": "center"
-        }
-    ),
-    dict(
-        title="",
-        text="",
-        img="i2.JPG",
-        imgStyle={
-            "width": "100%",
-            "height": "400px",
-            "objectFit": "cover",
-            "objectPosition": "center"
-        }
-    ),dict(
-        title="",
-        text="",
-        img="i3.JPG",
-        imgStyle={
-            "width": "100%",
-            "height": "400px",
-            "objectFit": "cover",
-            "objectPosition": "center"
-        }
-    ),
-    dict(
-        title="",
-        text="",
-        img="i5.JPG",
-        imgStyle={
-            "width": "100%",
-            "height": "400px",
-            "objectFit": "cover",
-            "objectPosition": "center"
-        }
-    )
-]
-
-carousel_container = st.container()
-with carousel_container:
-    carousel(items=images)
-
 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)
@@ -155,21 +38,65 @@ def initialize_vector_db():
     texts = text_splitter.split_documents(documents)
     
     embeddings = HuggingFaceEmbeddings()
-    vector_db = Chroma.from_documents(texts, embeddings, persist_directory=PERSISTENT_DIR)
+    vector_db = Chroma.from_documents(
+        texts, 
+        embeddings, 
+        persist_directory=PERSISTENT_DIR
+    )
     vector_db.persist()
     return documents, vector_db
 
+# 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
-2. Provide comprehensive diagnosis and treatment advice along with organic farming best practices applicable in the given context
-3. Base recommendations on homeopathic and organic farming principles
+3. Provide comprehensive diagnosis and treatment advice along with organic farming best practices applicable in the given context
+4. Base recommendations on homeopathic and organic farming principles
 """
 
 api_key1 = os.getenv("api_key")
 
+start_time = time.time()
+st.set_page_config(page_title="Dr. Radha: The Agro-Homeopath", page_icon="🚀", layout="wide")
+
+# 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;
+        color: white;
+    }
+</style>
+""", unsafe_allow_html=True)
+
 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
@@ -179,6 +106,7 @@ Please provide complete details about the issue, including:
 human_image = "human.png"
 robot_image = "bot.jpg"
 
+# Set up Groq API with temperature 0.7
 llm = ChatGroq(
     api_key=api_key1, 
     max_tokens=None, 
@@ -189,6 +117,8 @@ 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
 if "documents" not in st.session_state:
@@ -197,7 +127,8 @@ 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"] = ""
-
+    
+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()
@@ -207,18 +138,15 @@ else:
     documents = st.session_state["documents"]
     vector_db = st.session_state["vector_db"]
 
-retriever = vector_db.as_retriever()
+end_time = time.time()
+print(f"Loading and processing PDFs & vector database took {end_time - start_time:.4f} seconds")
 
-# Add footer image before the form
-#st.image(FOOTER_IMAGE, use_container_width=True)
+start_time = time.time()
+retriever = vector_db.as_retriever()
 
-# Rest of your prompt templates and chain setup remains the same
 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}
-
 Question: {question}
-
 Provide your response in the following format:
 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. 
@@ -243,18 +171,26 @@ experiences. Suggested doses are:
 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.
-
 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.
-
 Question: {question}
-
 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.
@@ -279,80 +215,55 @@ experiences. Suggested doses are:
 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
-
 Maintain a professional tone and ensure all medicine recommendations include specific potency.
-
 Answer:"""
 
-qa = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=retriever,
-    chain_type_kwargs={
-        "prompt": PromptTemplate(
-            template=prompt_template,
-            input_variables=["context", "question"]
-        )
-    }
-)
-
 fallback_prompt = PromptTemplate(template=fallback_template, input_variables=["question"])
 fallback_chain = LLMChain(llm=llm, prompt=fallback_prompt)
 
-st.markdown("""
-    <style>
-    .stTextArea label {
-        color: white !important;
-        font-size: 1rem !important;
-    }
-    .stButton button {
-        color: black !important;
-    }
-    </style>
-""", unsafe_allow_html=True)
-
 chat_container = st.container()
+
 st.markdown("""
-    <style>
-    .stButton button {
-        color: red !important;
-        background-color: transparent;
-        border: 1px solid red;
-    }
-    .stButton button:hover {
-        color: red !important;
-        border-color: red;
-    }
-    </style>
+<style>
+.stButton>button {
+    color: black !important;
+    background-color: yellow !important;
+}
+</style>
 """, unsafe_allow_html=True)
-with st.form(key='query_form', clear_on_submit=True):
-    query = st.text_area("Ask your question:", height=100)
-    col1, col2, col3 = st.columns([1,6,1])
-    with col2:
-        submit_button = st.form_submit_button(label='Submit')
 
+with st.form(key='query_form'):
+    query = st.text_input("Ask your question:", value="")
+    submit_button = st.form_submit_button(label='Submit')
+
+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:
-    chat_container = st.container()
-    with chat_container:
-        with st.spinner("Generating response..."):
-            result = qa({"query": query})
-            if result['result'].strip() == "":
-                response = fallback_chain.run(query)
-            else:
-                response = result['result']
-            
-            st.markdown("---")
-            cols = st.columns([1,10])
-            with cols[0]:
-                st.image(human_image, width=80)
-            with cols[1]:
-                st.markdown(f"**Question:** {query}")
-            
-            cols = st.columns([1,10])
-            with cols[0]:
-                st.image(robot_image, width=80)
-            with cols[1]:
-                st.markdown(f"**Answer:** {response}")
+    with st.spinner("Generating response..."):
+        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']
+
+    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()