Update app.py

app.py

@@ -4,24 +4,23 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.chains import RetrievalQA
 from langchain_community.vectorstores import FAISS
 from langchain_core.prompts import PromptTemplate
-
-from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFaceEndpoint
 import time
 import translators as ts
 from huggingface_hub import hf_hub_download
-from dotenv import load_dotenv  # Load .env locally
 
-# ================== CONFIGURATION ================== #
-# Load HF_TOKEN from Secrets or .env (for local development)
-load_dotenv()
-HF_TOKEN = os.getenv("HF_TOKEN")
+# Set page layout to wide
+st.set_page_config(layout="wide")
 
+# ================== CONFIGURATION ================== #
+HF_TOKEN = os.getenv("HF_TOKEN")  # From Spaces secrets
 VECTORSTORE_REPO_ID = "vashu2425/bhagavad-geeta-faiss-vectordb"
 MODEL_REPO_ID = "mistralai/Mistral-7B-Instruct-v0.3"
 
+    
 CUSTOM_PROMPT_TEMPLATE = """
 Use The Pieces Of Information Provided In The Context To Answer User's Question.
-If You Don't Know The Answer, Just Say "I Don't Have Information", except this do not say anything. 
+If You Don't Know The Answer, Just Say "I Don't Have Information",except this do not say anything. 
 Don't Try To Make Up An Answer. Don't Provide Anything Out Of The Given Context.
 
 Context: {context}
@@ -29,9 +28,9 @@ Question: {question}
 
 Start The Answer Directly., Please. The Answer Should Contain All 3 Contexts.
 Consider Yourself As God Krishna And Answer The Question Result Should Not Start With "Answer"
-"""
+"""  # Keep your template here
 
-# ---------- Session Management ---------- #
+# ---------- Session Management Functions ---------- #
 def initialize_session_states():
     session_defaults = {
         "messages": [],
@@ -39,16 +38,46 @@ def initialize_session_states():
         "show_predefined": True,
         "last_response": None,
         "translation_done": False,
-        "last_prompt": None
+        "last_prompt": None  # Add this line
     }
     for key, val in session_defaults.items():
         if key not in st.session_state:
             st.session_state[key] = val
 
-# ---------- Core Functionality ---------- #
+def render_chat_messages():
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"], avatar="🐿" if message["role"] == "user" else "🪈"):
+            content = message["content"]
+            if "hindi-text" in content:
+                st.markdown(content, unsafe_allow_html=True)
+            else:
+                st.markdown(content)
+
+def render_predefined_questions():
+    predefined_questions = [
+        "Meaning of Dharma?",
+        "What is the purpose of life?",
+        "How to find inner peace?",
+        "How can I be a better person?",
+        "What is the meaning of life?",
+        "How can I be a better friend?"
+    ]
+    st.markdown("### Or, try one of these:")
+    buttons = st.columns(len(predefined_questions))
+    for idx, question in enumerate(predefined_questions):
+        if buttons[idx].button(question, key=f"predefined_{idx}"):
+            st.session_state.selected_question = question
+            st.session_state.show_predefined = False
+
+# ---------- Core Functionality Functions ---------- #
 def translate_text(text, dest_language="hi"):
     try:
-        return ts.translate_text(text, to_language=dest_language, translator='google')
+        # Use the updated translation method
+        return ts.translate_text(
+            text, 
+            to_language=dest_language,
+            translator='google'
+        )
     except Exception as e:
         st.error(f"Translation failed: {str(e)}")
         return text
@@ -78,56 +107,70 @@ def get_vectorstore():
 def set_custom_prompt(custom_prompt_template):
     return PromptTemplate(template=custom_prompt_template, input_variables=["context", "question"])
 
-def load_llm():
+def load_llm(huggingface_repo_id, hf_token):
     return HuggingFaceEndpoint(
-        repo_id=MODEL_REPO_ID,
+        repo_id=huggingface_repo_id,
         temperature=0.5,
-        huggingfacehub_api_token=HF_TOKEN,
+        huggingfacehub_api_token=hf_token,
         model_kwargs={"max_length": 512}
     )
 
+def format_source_docs(source_documents):
+    formatted_docs = []
+    for idx, doc in enumerate(source_documents, start=1):
+        content = doc.page_content.replace('\t', ' ').replace('\n', ' ').strip()
+        formatted_doc = f"**Source {idx}** (Page {doc.metadata['page']}):\n\n{content[:500]}..." 
+        formatted_docs.append(formatted_doc)
+    return "\n\n".join(formatted_docs)
+
 def handle_user_input(prompt, qa_chain):
     if prompt:
+        # Check if this prompt has already been processed
         if st.session_state.get("last_prompt") == prompt:
             return
+        
+        # Store the current prompt to prevent reprocessing
         st.session_state.last_prompt = prompt
-
+        
         with st.chat_message("user", avatar="🐿"):
             st.markdown(prompt)
         st.session_state.messages.append({"role": "user", "content": prompt})
 
         try:
+            # Add temporary assistant message
             with st.chat_message("assistant", avatar="🪈"):
                 response_placeholder = st.empty()
             
+            # Process query and generate response
             response = qa_chain.invoke({"query": prompt})
             result = response["result"]
             source_documents = response["source_documents"]
 
+            # Build response incrementally
             accumulated_text = ""
             for char in result:
                 accumulated_text += char
                 response_placeholder.markdown(f'<div class="english-text">{accumulated_text}</div>', unsafe_allow_html=True)
                 time.sleep(0.01)
             
+            # Update session state with final response
             st.session_state.messages.append({
                 "role": "assistant",
                 "content": f'<div class="english-text">{accumulated_text}</div>',
                 "original": accumulated_text
             })
-
+            
             st.session_state.last_response = accumulated_text
             st.session_state.show_predefined = False
             st.session_state.translation_done = False
 
             if "don't have information" not in result.lower():
                 with st.expander("Source Documents"):
-                    for idx, doc in enumerate(source_documents, start=1):
-                        content = doc.page_content.replace('\t', ' ').replace('\n', ' ').strip()
-                        st.markdown(f"**Source {idx}**: {content[:500]}...")
+                    st.markdown(format_source_docs(source_documents))
 
         except Exception as e:
             st.error(f"Error: {str(e)}")
+            # Remove temporary assistant message on error
             if st.session_state.messages and st.session_state.messages[-1]["role"] == "assistant":
                 st.session_state.messages.pop()
 
@@ -137,45 +180,146 @@ def handle_translation():
             if not st.session_state.get("translation_done", False):
                 translated_text = translate_text(st.session_state.last_response, "hi")
                 
+                # Update messages
                 for msg in reversed(st.session_state.messages):
                     if msg["role"] == "assistant":
                         msg["content"] = f'<div class="hindi-text">{translated_text}</div>'
                         break
                 
                 st.session_state.translation_done = True
-                st.rerun()
-
+                st.rerun()  # Corrected rerun method
+                
         except Exception as e:
             st.error(f"Translation error: {str(e)}")
 
-def main():
-    st.set_page_config(layout="wide")
-    st.title("Ask Krishna! 🦚")
-    st.markdown('<p class="hindi-text" style="color:#666666; font-size:20px;">शांति स्वीकृति से शुरू होती है</p>', 
-                unsafe_allow_html=True)
-
-    initialize_session_states()
-
+def render_chat_messages():
     for message in st.session_state.messages:
         with st.chat_message(message["role"], avatar="🐿" if message["role"] == "user" else "🪈"):
-            content = message.get("original", message["content"])
+            content = message.get("original", message["content"])  # Show original if available
             if "hindi-text" in message["content"]:
                 st.markdown(message["content"], unsafe_allow_html=True)
             else:
                 st.markdown(content)
 
-    predefined_questions = [
-        "Meaning of Dharma?", "What is the purpose of life?",
-        "How to find inner peace?", "How can I be a better person?"
-    ]
+def main():
+    st.markdown( """
+        <style>
+            @import url('https://fonts.googleapis.com/css2?family=Noto+Sans+Devanagari:wght@400;700&display=swap');
+            .hindi-text {
+                font-family: 'Noto Sans Devanagari', sans-serif;
+                font-size: 16px;
+                line-height: 1.8;
+                direction: ltr;
+                text-align: left;
+            }
+            
+            .english-text {
+                font-family: Arial, sans-serif;
+                font-size: 16px;
+                line-height: 1.6;
+            }
+            
+             .translate-btn {
+             background-color: #4CAF50 !important;
+             color: white !important;
+             border-radius: 20px;  /* Reduced from 25px */
+             padding: 6px 20px;    /* Reduced from 8px 25px */
+             margin: 6px 0;        /* Reduced from 10px 0 */
+             border: none;
+             transition: all 0.3s ease;
+             font-size: 14px;      /* Added font-size control */
+             min-width: 120px;     /* Added for better proportions */
+             }
+
+             .translate-btn:hover {
+                 background-color: #45a049 !important;
+                 transform: scale(1.03); /* Reduced from 1.05 */
+             }
+            
+             .top-left-button {
+                position: auto;
+                top: 50px;
+                left: 20px;
+                z-index: 100;
+                padding: 10px 20px;
+                background-color: #e0162e;
+                color: white !important;
+                text-decoration: none !important;
+                border-radius: 50px;
+                margin-top: 10px;
+                font-size: 16px;
+                text-align: center;
+            }
+            .top-left-button:hover {
+                background-color: #f7525a;
+            }
+        
+            /* Fullscreen styles */
+            body {
+                margin: 0;
+                padding: 0;
+                width: 100vw;
+                height: 100vh;
+                display: flex;
+                justify-content: center;
+                align-items: center;
+                background-color: #1e1e30; /* Change the background color to #1e1e30 */
+            }
+        
+            [data-testid="stAppViewContainer"] > .main {
+                background-size: cover;
+                background-position: center center;
+                background-repeat: no-repeat;
+                background-attachment: local;
+            }
+        
+            /* Header background */
+            [data-testid="stHeader"] {
+                background: #1e1e30;
+            }
+        
+            /* Apply background color to the whole Streamlit app */
+            .stApp {
+                width: 100%;
+                max-width: 100vw;
+                display: flex;
+                justify-content: center;
+                align-items: flex-start;
+                padding: 20px;
+                background-color: #1e1e30; /* This will apply the background color to the entire app */
+            }
+
+             .custom-paragraph {
+                font-size: 20px !important;
+                line-height: 0.2;
+                color: #666666;
+            } 
+        
+            /* Apply background color to stBottomBlockContainer */
+            [data-testid="stBottomBlockContainer"] {
+                background-color: #1e1e30; /* Set the same color for bottom block */
+            }
+        
+            /* Hover effect for textarea (optional) */
+            .stTextArea>div>textarea:hover {
+                box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.3); /* Change shadow on hover */
+        </style>
+        <a href="https://iskconmangaluru.com/wp-content/uploads/2021/04/English-Bhagavad-gita-His-Divine-Grace-AC-Bhaktivedanta-Swami-Prabhupada.pdf" target="_blank" class="top-left-button">
+            Source Bhagavad Gita PDF
+        </a>
+        """,
+        unsafe_allow_html=True
+    )
     
+    st.title("Ask Krishna! 🦚")
+    st.markdown('<p class="hindi-text" style="color:#666666; font-size:20px;">शांति स्वीकृति से शुरू होती है</p>', 
+                unsafe_allow_html=True)
+
+    initialize_session_states()
+    render_chat_messages()
+
     if st.session_state.show_predefined:
-        st.markdown("### Or, try one of these:")
-        buttons = st.columns(len(predefined_questions))
-        for idx, question in enumerate(predefined_questions):
-            if buttons[idx].button(question, key=f"predefined_{idx}"):
-                st.session_state.selected_question = question
-                st.session_state.show_predefined = False
+        render_predefined_questions()
 
     prompt = st.chat_input("What's your curiosity?") or st.session_state.selected_question
     st.session_state.selected_question = None
@@ -183,7 +327,7 @@ def main():
     try:
         vectorstore = get_vectorstore()
         qa_chain = RetrievalQA.from_chain_type(
-            llm=load_llm(),
+            llm=load_llm(MODEL_REPO_ID, HF_TOKEN),
             chain_type="stuff",
             retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
             return_source_documents=True,
@@ -205,5 +349,4 @@ def main():
     except Exception as e:
         st.error(f"Initialization error: {str(e)}")
 
-if __name__ == "__main__":
-    main()
+if __name__ == "__main__"