Update app.py

app.py

@@ -1,144 +1,146 @@
-import streamlit as st
-import os
-from dotenv import load_dotenv
-import time
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
-from langchain.chains import RetrievalQA
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-start_time = time.time()
-# Set page title and icon
-st.set_page_config(page_title="Dr. Radha: The Agro-Homeopath", page_icon="🚀", layout="wide")
-
-# Center the title
-st.markdown("""
-<style>
-    #the-title {
-        text-align: center;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Display the title
-st.title("📚 Ask Dr. Radha - World's First AI based Agro-Homeopathy Doctor")
-
-# Load images
-human_image = "human.png"
-robot_image = "bot.png"
-
-# Load environment variables
-load_dotenv()
-end_time = time.time()
-print(f"Loading environment variables took {end_time - start_time:.4f} seconds")
-
-start_time = time.time()
-# Set up Groq API
-llm = ChatGroq(api_key=os.getenv("GROQ_API_KEY"), max_tokens=None, timeout=None, max_retries=2, temperature=0.5, model="llama-3.1-70b-versatile")
-
-# Set up embeddings
-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:
-    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"] = ""
-
-def load_data():
-    pdf_folder = "docs"
-    loaders = [PyPDFLoader(os.path.join(pdf_folder, fn)) for fn in os.listdir(pdf_folder)]
-    documents = []
-    for loader in loaders:
-        documents.extend(loader.load())
-    
-    #text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=30)
-    text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size=1000,
-    chunk_overlap=200,
-    length_function=len,
-    separators=["\n\n", "\n", " ", ""]
-    )
-    texts = text_splitter.split_documents(documents)
-    # Set up vector database  
-    persist_directory = "db"
-    vector_db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
-
-    return documents, vector_db
-
-# Load and process PDFs
-start_time = time.time()
-# Load data if not already loaded
-if st.session_state["documents"] is None or st.session_state["vector_db"] is None:
-    with st.spinner("Loading data..."):
-        documents, vector_db = load_data()
-        st.session_state["documents"] = documents  
-        st.session_state["vector_db"] = vector_db
-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")
-
-# Set up retrieval chain
-start_time = time.time()
-retriever = vector_db.as_retriever()
-qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
-
-# Chat interface
-chat_container = st.container()
-
-# Create a form for the query input and submit button
-with st.form(key='query_form'):
-    query = st.text_input("Ask your question:", value="")#st.session_state["query"])
-    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() == "":
-        response = "I apologize, but I don't have enough information in the provided PDFs to answer your question."
-    else:
-        response = result['result']
-
-    # Display human image and question
-    col1, col2 = st.columns([1, 10])
-    with col1:
-        st.image(human_image, width=80)
-    with col2:
-        st.markdown(f"{query}")
-    # Display robot image and answer
-    col1, col2 = st.columns([1, 10])
-    with col1:
-        st.image(robot_image, width=80)
-    with col2:
-        st.markdown(f"{response}")
-    
-    st.markdown("---")
-    
-    # Clear the query input
-    st.session_state["query"] = ""
-#st.rerun()
-
-end_time = time.time()
-print(f"Actual query took {end_time - start_time:.4f} seconds")
-
-# Reload data button
-# if st.button("Reload Data"):
-#     with st.spinner("Reloading data..."):
-#         documents, vector_db = load_data()
-#         st.session_state["documents"] = documents
-#         st.session_state["vector_db"] = vector_db
+import streamlit as st
+import os
+from dotenv import load_dotenv
+import time
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+# Access the secret
+api_key1 = os.getenv("api_key")
+start_time = time.time()
+# Set page title and icon
+st.set_page_config(page_title="Dr. Radha: The Agro-Homeopath", page_icon="🚀", layout="wide")
+
+# Center the title
+st.markdown("""
+<style>
+    #the-title {
+        text-align: center;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Display the title
+st.title("📚 Ask Dr. Radha - World's First AI based Agro-Homeopathy Doctor")
+
+# Load images
+human_image = "human.png"
+robot_image = "bot.png"
+
+# Load environment variables
+load_dotenv()
+end_time = time.time()
+print(f"Loading environment variables took {end_time - start_time:.4f} seconds")
+
+start_time = time.time()
+# Set up Groq API
+llm = ChatGroq(api_key=api_key1, max_tokens=None, timeout=None, max_retries=2, temperature=0.5, model="llama-3.1-70b-versatile")
+
+# Set up embeddings
+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:
+    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"] = ""
+
+def load_data():
+    pdf_folder = "docs"
+    loaders = [PyPDFLoader(os.path.join(pdf_folder, fn)) for fn in os.listdir(pdf_folder)]
+    documents = []
+    for loader in loaders:
+        documents.extend(loader.load())
+    
+    #text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=30)
+    text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=200,
+    length_function=len,
+    separators=["\n\n", "\n", " ", ""]
+    )
+    texts = text_splitter.split_documents(documents)
+    # Set up vector database  
+    persist_directory = "db"
+    vector_db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
+
+    return documents, vector_db
+
+# Load and process PDFs
+start_time = time.time()
+# Load data if not already loaded
+if st.session_state["documents"] is None or st.session_state["vector_db"] is None:
+    with st.spinner("Loading data..."):
+        documents, vector_db = load_data()
+        st.session_state["documents"] = documents  
+        st.session_state["vector_db"] = vector_db
+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")
+
+# Set up retrieval chain
+start_time = time.time()
+retriever = vector_db.as_retriever()
+qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+
+# Chat interface
+chat_container = st.container()
+
+# Create a form for the query input and submit button
+with st.form(key='query_form'):
+    query = st.text_input("Ask your question:", value="")#st.session_state["query"])
+    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() == "":
+        response = "I apologize, but I don't have enough information in the provided PDFs to answer your question."
+    else:
+        response = result['result']
+
+    # Display human image and question
+    col1, col2 = st.columns([1, 10])
+    with col1:
+        st.image(human_image, width=80)
+    with col2:
+        st.markdown(f"{query}")
+    # Display robot image and answer
+    col1, col2 = st.columns([1, 10])
+    with col1:
+        st.image(robot_image, width=80)
+    with col2:
+        st.markdown(f"{response}")
+    
+    st.markdown("---")
+    
+    # Clear the query input
+    st.session_state["query"] = ""
+#st.rerun()
+
+end_time = time.time()
+print(f"Actual query took {end_time - start_time:.4f} seconds")
+
+# Reload data button
+# if st.button("Reload Data"):
+#     with st.spinner("Reloading data..."):
+#         documents, vector_db = load_data()
+#         st.session_state["documents"] = documents
+#         st.session_state["vector_db"] = vector_db
 #     st.success("Data reloaded successfully!")