Update app.py

app.py

@@ -14,8 +14,12 @@ from langchain.chains import LLMChain
 # Set persistent storage path
 PERSISTENT_DIR = "vector_db"
 
+# Define image paths
+HEADER_IMAGE = "i1.jpg"  # Organic farming landscape
+SIDE_IMAGE = "i2.JPG"       # Medicinal plants/herbs
+FOOTER_IMAGE = "i3.JPG"     # Sustainable farming practices
+
 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)
@@ -38,15 +42,10 @@ 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
 3. Provide comprehensive diagnosis and treatment advice along with organic farming best practices applicable in the given context
@@ -55,48 +54,43 @@ system_prompt = """You are an expert organic farming consultant with specializat
 
 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")
+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)
+# Add header image
+st.image(HEADER_IMAGE, use_column_width=True)
 
+# Create main layout columns
+left_col, right_col = st.columns([3, 1])
+
+with left_col:
+    st.title("🌿 Dr. Radha: AI-Powered Organic Farming Consultant")
+    st.subheader("Specializing in Agro-Homeopathy | Free Consultation")
+
+with right_col:
+    st.image(SIDE_IMAGE, width=250, caption="Natural Healing Solutions")
+
+# CSS styling
 st.markdown("""
 <style>
-    #the-title {
-        text-align: center;
-        font-size: 24px;
-        color: white;
-    }
+.stApp {
+    background-color: #1B4D3E !important;
+    color: white !important;
+}
+.stTextInput>div>div>input {
+    color: black !important;
+    background-color: rgba(255,255,255,0.1) !important;
+}
+.stButton>button {
+    color: black !important;
+    background-color: yellow !important;
+}
+.stImage {
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+}
 </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
@@ -106,7 +100,6 @@ 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, 
@@ -117,8 +110,6 @@ 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:
@@ -127,8 +118,7 @@ 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()
@@ -138,12 +128,12 @@ 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()
 
+# Add footer image before the form
+st.image(FOOTER_IMAGE, use_column_width=True, caption="Sustainable Farming Practices")
+
+# 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}
@@ -181,20 +171,7 @@ 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.
 
 Question: {question}
@@ -230,33 +207,31 @@ Maintain a professional tone and ensure all medicine recommendations include spe
 
 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)
 
 chat_container = st.container()
 
-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="")
     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:
     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:
@@ -274,8 +249,4 @@ if submit_button and query:
         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")