Update app.py

app.py

@@ -278,7 +278,7 @@ def upload_file(file_obj):
     return list_file_path
 
 
-import gradio as gr
+
 
 def demo():
     with gr.Blocks(theme="base") as demo:
@@ -287,15 +287,14 @@ def demo():
         collection_name = gr.State()
         
         gr.Markdown(
-            """<center><h2>PDF-based Chatbot</h2></center>
-            <h3>Ask any questions about your PDF documents</h3>"""
-        )
+        """<center><h2>BookMyDarshan: Your Personalized Spiritual Assistant</h2></center>
+        <h3>Explore Sacred Texts and Enhance Your Spiritual Journey</h3>""")
+        
         gr.Markdown(
-            """<b>Note:</b> This AI assistant uses LangChain and open-source LLMs for retrieval-augmented generation (RAG) from your PDF documents. 
-            The UI shows multiple steps to help you understand the RAG workflow. This chatbot considers past questions when generating answers (via conversational memory) 
-            and includes document references for clarity.<br>
-            <br><b>Warning:</b> This demo uses free CPU Basic hardware from Hugging Face, so some steps may take time."""
-        )
+        """<b>About BookMyDarshan.in:</b> We are a Hyderabad-based startup dedicated to providing pilgrims with exceptional temple darshan experiences. 
+        Our platform offers a comprehensive suite of spiritual and religious services, customized to meet your devotional needs.<br><br>
+        <b>Note:</b> This spiritual assistant uses state-of-the-art AI to help you explore and understand your uploaded spiritual documents. 
+        With a blend of technology and tradition, this tool assists in connecting you more deeply with your faith.<br>""")
 
         with gr.Tab("Step 1: Upload PDF"):
             document = gr.Files(label="Upload your PDF documents", file_count="multiple", file_types=["pdf"], interactive=True)
@@ -308,14 +307,22 @@ def demo():
             db_progress = gr.Textbox(label="Vector Database Initialization Status", value="None", interactive=False)
             generate_db_btn = gr.Button("Generate Vector Database")
 
-        with gr.Tab("Step 3: Initialize QA Chain"):
-            llm_btn = gr.Radio(["LLM Model 1", "LLM Model 2"], label="Select LLM Model", value="LLM Model 1", info="Choose the LLM model")
-            with gr.Accordion("Advanced Options: LLM Model", open=False):
-                slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Adjust the model's creativity level")
-                slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Set the maximum number of tokens")
-                slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Top-K Samples", info="Select the number of top-k samples")
-            llm_progress = gr.Textbox(label="QA Chain Initialization Status", value="None", interactive=False)
-            qachain_btn = gr.Button("Initialize QA Chain")
+         with gr.Tab("Step 3 - Initialize QA chain"):
+            with gr.Row():
+                llm_btn = gr.Radio(list_llm_simple, \
+                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                with gr.Row():
+                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
+                with gr.Row():
+                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                with gr.Row():
+                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+            with gr.Row():
+                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
+            with gr.Row():
+                qachain_btn = gr.Button("Initialize Question Answering chain")
+
 
         with gr.Tab("Step 4: Chatbot"):
             chatbot = gr.Chatbot(label="Chat with your PDF", height=300)