Update app.py

app.py

@@ -1,445 +1,445 @@
-import os
-import gradio as gr
-import logging
-import asyncio
-from dotenv import load_dotenv
-from langchain.prompts import PromptTemplate
-from langchain_qdrant import QdrantVectorStore
-from langchain.chains import RetrievalQA
-from langchain_groq import ChatGroq
-from qdrant_client.models import PointStruct, VectorParams, Distance
-import uuid
-from qdrant_client.http import models
-from datetime import datetime
-from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
-from qdrant_client import QdrantClient
-import cohere
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain_cohere import CohereRerank
-import re
-from translation_service import TranslationService
-
-# Load environment variables
-load_dotenv()
-
-# Initialize logging with INFO level and detailed format
-logging.basicConfig(
-    filename='app.log',
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s'
-)
-
-# Initialize services
-translator = TranslationService()
-
-def initialize_database_client():
-    """Initialize Qdrant client"""
-    try:
-        client = QdrantClient(
-            url=os.getenv("QDURL"),
-            api_key=os.getenv("API_KEY1"),
-            verify=True # Set to True if using SSL
-        )
-        logging.info("Qdrant client initialized successfully.")
-        return client
-    except Exception as e:
-        logging.error(f"Failed to initialize Qdrant client: {e}")
-        raise
-
-def initialize_llm():
-    """Initialize LLM with fallback"""
-    try:
-        llm = ChatGroq(
-            temperature=0, 
-            model_name="llama3-8b-8192", 
-            api_key=os.getenv("GROQ_API_KEY")
-        )
-        logging.info("ChatGroq initialized with model llama3-8b-8192.")
-        return llm
-    except Exception as e:
-        logging.warning(f"Failed to initialize ChatGroq with llama3: {e}. Falling back to mixtral.")
-        try:
-            llm = ChatGroq(
-                temperature=0, 
-                model_name="mixtral-8x7b-32768", 
-                api_key=os.getenv("GROQ_API_KEY")
-            )
-            logging.info("ChatGroq initialized with fallback model mixtral-8x7b-32768.")
-            return llm
-        except Exception as fallback_e:
-            logging.error(f"Failed to initialize fallback LLM: {fallback_e}")
-            raise
-
-def initialize_services():
-    """Initialize all services"""
-    try:
-        # Initialize Qdrant client
-        client = initialize_database_client()
-        
-        # Initialize embeddings
-        embeddings = FastEmbedEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5-Q")
-        logging.info("FastEmbedEmbeddings initialized successfully.")
-        
-        # Initialize Qdrant DB
-        db = QdrantVectorStore(
-            client=client,
-            embedding=embeddings,
-            collection_name="RR3"
-        )
-        logging.info("QdrantVectorStore initialized with collection 'RR3'.")
-        
-        # Initialize retriever with reranker
-        cohere_client = cohere.Client(api_key=os.getenv("COHERE_API_KEY"))
-        reranker = CohereRerank(
-            client=cohere_client,
-            top_n=3,
-            model="rerank-multilingual-v3.0"
-        )
-        base_retriever = db.as_retriever(search_kwargs={"k": 14})
-        retriever = ContextualCompressionRetriever(
-            base_compressor=reranker, 
-            base_retriever=base_retriever
-        )
-        logging.info("Retriever with reranker initialized successfully.")
-        
-        # Initialize LLM
-        llm = initialize_llm()
-        
-        return retriever, llm
-    except Exception as e:
-        logging.error(f"Service initialization error: {str(e)}")
-        raise
-
-def initialize_feedback_collection():
-    """Initialize and verify feedback collection"""
-    try:
-        client = initialize_database_client()
-        
-        # Check if collection exists
-        collections = client.get_collections().collections
-        collection_exists = any(c.name == "chat_feedback" for c in collections)
-        
-        if not collection_exists:
-            # Create collection with proper configuration
-            client.create_collection(
-                collection_name="chat_feedback",
-                vectors_config=VectorParams(
-                    size=768,  # Ensure this matches the embedding size
-                    distance=Distance.COSINE
-                )
-            )
-            logging.info("Created 'chat_feedback' collection with vector size 768 and Cosine distance.")
-        else:
-            logging.info("'chat_feedback' collection already exists.")
-        
-        # Verify collection exists and has correct configuration
-        collection_info = client.get_collection("chat_feedback")
-        if collection_info.config.params.vectors.size != 768:
-            raise ValueError("Incorrect vector size in 'chat_feedback' collection.")
-        logging.info("'chat_feedback' collection verified successfully with correct vector size.")
-        
-        return True
-    except Exception as e:
-        logging.error(f"Failed to initialize feedback collection: {e}")
-        raise
-
-async def submit_feedback(feedback_type, chat_history, language_choice):
-    """Submit feedback with improved error handling and logging."""
-    try:
-        if not chat_history or len(chat_history) < 2:
-            logging.warning("Attempted to submit feedback with insufficient chat history.")
-            return "No recent interaction to provide feedback for."
-
-        # Get last question and answer
-        last_interaction = chat_history[-2:]
-        question = last_interaction[0].get("content", "").strip()
-        answer = last_interaction[1].get("content", "").strip()
-
-        if not question or not answer:
-            logging.warning("Question or answer content is missing.")
-            return "Incomplete interaction data. Cannot submit feedback."
-
-        logging.info(f"Processing feedback for question: {question[:50]}...")
-
-        # Initialize client
-        client = initialize_database_client()
-
-        # Create point ID
-        point_id = str(uuid.uuid4())
-
-        # Create payload
-        payload = {
-            "question": question,
-            "answer": answer,
-            "language": language_choice,
-            "timestamp": datetime.utcnow().isoformat(),
-            "feedback": feedback_type
-        }
-
-        # Initialize embeddings
-        embeddings = FastEmbedEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5-Q")
-
-        # Create embeddings for the Q&A pair
-        try:
-            embedding_text = f"{question} {answer}"
-            vector = await asyncio.to_thread(embeddings.embed_query, embedding_text)
-            logging.info(f"Generated embedding vector of length {len(vector)}.")
-        except Exception as embed_error:
-            logging.error(f"Embedding generation failed: {embed_error}")
-            return "Failed to generate embeddings for your feedback."
-
-        if not isinstance(vector, list) or not vector:
-            logging.error("Invalid vector generated from embeddings.")
-            return "Failed to generate valid embeddings for your feedback."
-
-        # Create point
-        point = PointStruct(
-            id=point_id,
-            payload=payload,
-            vector=vector
-        )
-
-        # Store in Qdrant
-        try:
-            operation_info = await asyncio.to_thread(
-                client.upsert,
-                collection_name="chat_feedback",
-                points=[point]
-            )
-            logging.info(f"Feedback submitted successfully: {point_id}")
-            return "Thanks for your feedback! Your response has been recorded."
-        except Exception as db_error:
-            logging.error(f"Failed to upsert point to Qdrant: {db_error}")
-            return "Sorry, there was an error submitting your feedback."
-
-    except Exception as e:
-        logging.error(f"Unexpected error in submit_feedback: {e}")
-        return "Sorry, there was an unexpected error submitting your feedback."
-
-# Initialize services and feedback collection
-try:
-    retriever, llm = initialize_services()
-    initialize_feedback_collection()
-except Exception as initialization_error:
-    logging.critical(f"Initialization failed: {initialization_error}")
-    raise
-
-# Prompt template
-prompt_template = PromptTemplate(
-    template="""You are RRA Assistant, created by Cedric to help users get tax related information in Rwanda. Your task is to answer tax-related questions using the provided context.
-
-Context: {context}
-
-User's Question: {question}
-
-Please follow these steps to answer the question:
-
-Step 1: Analyze the question
-Briefly explain your understanding of the question and any key points to address. If it is hi or hello, skip to step 3 and respond with a greeting.
-
-Step 2: Provide relevant information
-Using the context provided, give detailed information related to the question. Include specific facts, figures, or explanations from the context.
-
-Step 3: Final answer
-Provide a clear, concise answer to the original question. Start directly with the relevant information, avoiding phrases like "In summary" or "To conclude".
-
-Remember:
-- If you don't know the answer or can't find relevant information in the context, say so honestly.
-- Do not make up information.
-- Use the provided context to support your answer.
-- Include "For more information, call 3004" at the end of every answer.
-
-Your response:
-""",
-    input_variables=['context', 'question']
-)
-
-async def process_query(message: str, language: str, chat_history: list) -> str:
-    try:
-        # Handle translation based on selected language
-        if language == "Kinyarwanda":
-            query = translator.translate(message, "rw", "en")
-            logging.info(f"Translated query to English: {query}")
-        else:
-            query = message
-        
-        # Create QA chain
-        qa = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=retriever,
-            chain_type_kwargs={"prompt": prompt_template},
-            return_source_documents=True
-        )
-        
-        # Get response
-        response = await asyncio.to_thread(
-            lambda: qa.invoke({"query": query})
-        )
-        logging.info("QA chain invoked successfully.")
-        
-        # Extract final answer
-        result_text = response.get('result', '')
-        final_answer_start = result_text.find("Step 3: Final answer")
-        if final_answer_start != -1:
-            answer = result_text[final_answer_start + len("Step 3: Final answer"):].strip()
-        else:
-            answer = result_text
-        
-        # Clean up the answer
-        answer = re.sub(r'\*\*', '', answer).strip()
-        answer = re.sub(r'Step \d+:', '', answer).strip()
-        
-        # Translate response if needed
-        if language == "Kinyarwanda":
-            answer = translator.translate(answer, "en", "rw")
-            logging.info(f"Translated answer to Kinyarwanda: {answer}")
-        
-        return answer
-    except Exception as e:
-        logging.error(f"Query processing error: {str(e)}")
-        return f"An error occurred: {str(e)}"
-
-# Define separate feedback submission functions to pass feedback type correctly
-async def submit_positive_feedback(chat_history, language_choice):
-    return await submit_feedback("positive", chat_history, language_choice)
-
-async def submit_negative_feedback(chat_history, language_choice):
-    return await submit_feedback("negative", chat_history, language_choice)
-
-# Create Gradio interface
-with gr.Blocks(title="RRA FAQ Chatbot") as demo:
-    gr.Markdown(
-        """
-        # RRA FAQ Chatbot
-        Ask tax-related questions in English or Kinyarwanda
-        > 🔒 Your questions and interactions remain private unless you choose to submit feedback, which helps improve our service.
-        """
-    )
-    
-    # Add language selector
-    language = gr.Radio(
-        choices=["English", "Kinyarwanda"],
-        value="English",
-        label="Select Language / Hitamo Ururimi"
-    )
-    
-    chatbot = gr.Chatbot(
-        value=[],
-        show_label=False,
-        height=400,
-        type='messages'
-    )
-    
-    with gr.Row():
-        msg = gr.Textbox(
-            label="Ask your question",
-            placeholder="Type your tax-related question here...",
-            show_label=False
-        )
-        submit = gr.Button("Send")
-    
-    # Add feedback section
-    with gr.Row():
-        with gr.Column(scale=2):
-            feedback_label = gr.Markdown("Was this response helpful?")
-        with gr.Column(scale=1):
-            feedback_positive = gr.Button("👍 Helpful")
-        with gr.Column(scale=1):
-            feedback_negative = gr.Button("👎 Not Helpful")
-    
-    # Add feedback status message
-    feedback_status = gr.Markdown("")
-    
-    # Connect feedback buttons to their respective functions
-    feedback_positive.click(
-        fn=submit_positive_feedback,
-        inputs=[chatbot, language],
-        outputs=feedback_status
-    )
-    
-    feedback_negative.click(
-        fn=submit_negative_feedback,
-        inputs=[chatbot, language],
-        outputs=feedback_status
-    )
-    
-    # Create two sets of examples
-    with gr.Row() as english_examples_row:
-        gr.Examples(
-            examples=[
-                "What is VAT in Rwanda?",
-                "How do I register for taxes?",
-                "What are the tax payment deadlines?",
-                "How can I get a TIN number?",
-                "How do I get purchase code?"
-            ],
-            inputs=msg,
-            label="English Examples"
-        )
-    
-    with gr.Row(visible=False) as kinyarwanda_examples_row:
-        gr.Examples(
-            examples=[
-                "Ese VAT ni iki mu Rwanda?",
-                "Nabona TIN number nte?",
-                "Ni ryari tugomba kwishyura imisoro?",
-                "Ese nandikwa nte ku musoro?",
-                "Ni gute nabone kode yo kugura?"
-            ],
-            inputs=msg,
-            label="Kinyarwanda Examples"
-        )
-    
-    async def respond(message, lang, chat_history):
-        bot_message = await process_query(message, lang, chat_history)
-        chat_history.append({"role": "user", "content": message})
-        chat_history.append({"role": "assistant", "content": bot_message})
-        return "", chat_history
-    
-    def toggle_language_interface(language_choice):
-        if language_choice == "English":
-            placeholder_text = "Type your tax-related question here..."
-            return {
-                msg: gr.update(placeholder=placeholder_text),
-                english_examples_row: gr.update(visible=True),
-                kinyarwanda_examples_row: gr.update(visible=False)
-            }
-        else:
-            placeholder_text = "Andika ibibazo bijyanye n'umusoro hano"
-            return {
-                msg: gr.update(placeholder=placeholder_text),
-                english_examples_row: gr.update(visible=False),
-                kinyarwanda_examples_row: gr.update(visible=True)
-            }
-    
-    msg.submit(respond, [msg, language, chatbot], [msg, chatbot])
-    submit.click(respond, [msg, language, chatbot], [msg, chatbot])
-    
-    # Update both examples visibility and placeholder when language changes
-    language.change(
-        fn=toggle_language_interface,
-        inputs=language,
-        outputs=[msg, english_examples_row, kinyarwanda_examples_row]
-    )
-    
-    gr.Markdown(
-        """
-        ### About
-        - Created by: [Cedric](mailto:[email protected])
-        - Data source: [RRA Website FAQ](https://www.rra.gov.rw/en/domestic-tax-services/faqs)
-        
-        **Disclaimer:** This chatbot provides general tax information. For official guidance, 
-        consult RRA or call 3004.
-        🔒 **Privacy:** Your interactions remain private unless you choose to submit feedback.
-        """
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    try:
-        demo.launch(share=False)
-        logging.info("Gradio app launched successfully.")
-    except Exception as launch_error:
-        logging.critical(f"Failed to launch Gradio app: {launch_error}")
-        raise
+import os
+import gradio as gr
+import logging
+import asyncio
+from dotenv import load_dotenv
+from langchain.prompts import PromptTemplate
+from langchain_qdrant import QdrantVectorStore
+from langchain.chains import RetrievalQA
+from langchain_groq import ChatGroq
+from qdrant_client.models import PointStruct, VectorParams, Distance
+import uuid
+from qdrant_client.http import models
+from datetime import datetime
+from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
+from qdrant_client import QdrantClient
+import cohere
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain_cohere import CohereRerank
+import re
+from translation_service import TranslationService
+
+# Load environment variables
+load_dotenv()
+
+# Initialize logging with INFO level and detailed format
+logging.basicConfig(
+    filename='app.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+# Initialize services
+translator = TranslationService()
+
+def initialize_database_client():
+    """Initialize Qdrant client"""
+    try:
+        client = QdrantClient(
+            url=os.getenv("QDURL"),
+            api_key=os.getenv("API_KEY1"),
+            verify=True # Set to True if using SSL
+        )
+        logging.info("Qdrant client initialized successfully.")
+        return client
+    except Exception as e:
+        logging.error(f"Failed to initialize Qdrant client: {e}")
+        raise
+
+def initialize_llm():
+    """Initialize LLM with fallback"""
+    try:
+        llm = ChatGroq(
+            temperature=0, 
+            model_name="llama3-8b-8192", 
+            api_key=os.getenv("GROQ_API_KEY")
+        )
+        logging.info("ChatGroq initialized with model llama3-8b-8192.")
+        return llm
+    except Exception as e:
+        logging.warning(f"Failed to initialize ChatGroq with llama3: {e}. Falling back to mixtral.")
+        try:
+            llm = ChatGroq(
+                temperature=0, 
+                model_name="mixtral-8x7b-32768", 
+                api_key=os.getenv("GROQ_API_KEY")
+            )
+            logging.info("ChatGroq initialized with fallback model mixtral-8x7b-32768.")
+            return llm
+        except Exception as fallback_e:
+            logging.error(f"Failed to initialize fallback LLM: {fallback_e}")
+            raise
+
+def initialize_services():
+    """Initialize all services"""
+    try:
+        # Initialize Qdrant client
+        client = initialize_database_client()
+        
+        # Initialize embeddings
+        embeddings = FastEmbedEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5-Q")
+        logging.info("FastEmbedEmbeddings initialized successfully.")
+        
+        # Initialize Qdrant DB
+        db = QdrantVectorStore(
+            client=client,
+            embedding=embeddings,
+            collection_name="RR4"
+        )
+        logging.info("QdrantVectorStore initialized with collection 'RR4'.")
+        
+        # Initialize retriever with reranker
+        cohere_client = cohere.Client(api_key=os.getenv("COHERE_API_KEY"))
+        reranker = CohereRerank(
+            client=cohere_client,
+            top_n=6,
+            model="rerank-multilingual-v3.0"
+        )
+        base_retriever = db.as_retriever(search_kwargs={"k": 30})
+        retriever = ContextualCompressionRetriever(
+            base_compressor=reranker, 
+            base_retriever=base_retriever
+        )
+        logging.info("Retriever with reranker initialized successfully.")
+        
+        # Initialize LLM
+        llm = initialize_llm()
+        
+        return retriever, llm
+    except Exception as e:
+        logging.error(f"Service initialization error: {str(e)}")
+        raise
+
+def initialize_feedback_collection():
+    """Initialize and verify feedback collection"""
+    try:
+        client = initialize_database_client()
+        
+        # Check if collection exists
+        collections = client.get_collections().collections
+        collection_exists = any(c.name == "chat_feedback" for c in collections)
+        
+        if not collection_exists:
+            # Create collection with proper configuration
+            client.create_collection(
+                collection_name="chat_feedback",
+                vectors_config=VectorParams(
+                    size=768,  # Ensure this matches the embedding size
+                    distance=Distance.COSINE
+                )
+            )
+            logging.info("Created 'chat_feedback' collection with vector size 768 and Cosine distance.")
+        else:
+            logging.info("'chat_feedback' collection already exists.")
+        
+        # Verify collection exists and has correct configuration
+        collection_info = client.get_collection("chat_feedback")
+        if collection_info.config.params.vectors.size != 768:
+            raise ValueError("Incorrect vector size in 'chat_feedback' collection.")
+        logging.info("'chat_feedback' collection verified successfully with correct vector size.")
+        
+        return True
+    except Exception as e:
+        logging.error(f"Failed to initialize feedback collection: {e}")
+        raise
+
+async def submit_feedback(feedback_type, chat_history, language_choice):
+    """Submit feedback with improved error handling and logging."""
+    try:
+        if not chat_history or len(chat_history) < 2:
+            logging.warning("Attempted to submit feedback with insufficient chat history.")
+            return "No recent interaction to provide feedback for."
+
+        # Get last question and answer
+        last_interaction = chat_history[-4:]
+        question = last_interaction[0].get("content", "").strip()
+        answer = last_interaction[1].get("content", "").strip()
+
+        if not question or not answer:
+            logging.warning("Question or answer content is missing.")
+            return "Incomplete interaction data. Cannot submit feedback."
+
+        logging.info(f"Processing feedback for question: {question[:50]}...")
+
+        # Initialize client
+        client = initialize_database_client()
+
+        # Create point ID
+        point_id = str(uuid.uuid4())
+
+        # Create payload
+        payload = {
+            "question": question,
+            "answer": answer,
+            "language": language_choice,
+            "timestamp": datetime.utcnow().isoformat(),
+            "feedback": feedback_type
+        }
+
+        # Initialize embeddings
+        embeddings = FastEmbedEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5-Q")
+
+        # Create embeddings for the Q&A pair
+        try:
+            embedding_text = f"{question} {answer}"
+            vector = await asyncio.to_thread(embeddings.embed_query, embedding_text)
+            logging.info(f"Generated embedding vector of length {len(vector)}.")
+        except Exception as embed_error:
+            logging.error(f"Embedding generation failed: {embed_error}")
+            return "Failed to generate embeddings for your feedback."
+
+        if not isinstance(vector, list) or not vector:
+            logging.error("Invalid vector generated from embeddings.")
+            return "Failed to generate valid embeddings for your feedback."
+
+        # Create point
+        point = PointStruct(
+            id=point_id,
+            payload=payload,
+            vector=vector
+        )
+
+        # Store in Qdrant
+        try:
+            operation_info = await asyncio.to_thread(
+                client.upsert,
+                collection_name="chat_feedback",
+                points=[point]
+            )
+            logging.info(f"Feedback submitted successfully: {point_id}")
+            return "Thanks for your feedback! Your response has been recorded."
+        except Exception as db_error:
+            logging.error(f"Failed to upsert point to Qdrant: {db_error}")
+            return "Sorry, there was an error submitting your feedback."
+
+    except Exception as e:
+        logging.error(f"Unexpected error in submit_feedback: {e}")
+        return "Sorry, there was an unexpected error submitting your feedback."
+
+# Initialize services and feedback collection
+try:
+    retriever, llm = initialize_services()
+    initialize_feedback_collection()
+except Exception as initialization_error:
+    logging.critical(f"Initialization failed: {initialization_error}")
+    raise
+
+# Prompt template
+prompt_template = PromptTemplate(
+    template="""You are RRA Assistant, created by Cedric to help users get tax related information in Rwanda. Your task is to answer tax-related questions using the provided context.
+
+Context: {context}
+
+User's Question: {question}
+
+Please follow these steps to answer the question:
+
+Step 1: Analyze the question
+Briefly explain your understanding of the question and any key points to address. If it is hi or hello, skip to step 3 and respond with a greeting.
+
+Step 2: Provide relevant information
+Using the context provided, give detailed information related to the question. Include specific facts, figures, or explanations from the context.
+
+Step 3: Final answer
+Provide a clear, concise answer to the original question. Start directly with the relevant information, avoiding phrases like "In summary" or "To conclude".
+
+Remember:
+- If you don't know the answer or can't find relevant information in the context, say so honestly.
+- Do not make up information.
+- Use the provided context to support your answer.
+- Include "For more information, call 3004" at the end of every answer.
+
+Your response:
+""",
+    input_variables=['context', 'question']
+)
+
+async def process_query(message: str, language: str, chat_history: list) -> str:
+    try:
+        # Handle translation based on selected language
+        if language == "Kinyarwanda":
+            query = translator.translate(message, "rw", "en")
+            logging.info(f"Translated query to English: {query}")
+        else:
+            query = message
+        
+        # Create QA chain
+        qa = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=retriever,
+            chain_type_kwargs={"prompt": prompt_template},
+            return_source_documents=True
+        )
+        
+        # Get response
+        response = await asyncio.to_thread(
+            lambda: qa.invoke({"query": query})
+        )
+        logging.info("QA chain invoked successfully.")
+        
+        # Extract final answer
+        result_text = response.get('result', '')
+        final_answer_start = result_text.find("Step 3: Final answer")
+        if final_answer_start != -1:
+            answer = result_text[final_answer_start + len("Step 3: Final answer"):].strip()
+        else:
+            answer = result_text
+        
+        # Clean up the answer
+        answer = re.sub(r'\*\*', '', answer).strip()
+        answer = re.sub(r'Step \d+:', '', answer).strip()
+        
+        # Translate response if needed
+        if language == "Kinyarwanda":
+            answer = translator.translate(answer, "en", "rw")
+            logging.info(f"Translated answer to Kinyarwanda: {answer}")
+        
+        return answer
+    except Exception as e:
+        logging.error(f"Query processing error: {str(e)}")
+        return f"An error occurred: {str(e)}"
+
+# Define separate feedback submission functions to pass feedback type correctly
+async def submit_positive_feedback(chat_history, language_choice):
+    return await submit_feedback("positive", chat_history, language_choice)
+
+async def submit_negative_feedback(chat_history, language_choice):
+    return await submit_feedback("negative", chat_history, language_choice)
+
+# Create Gradio interface
+with gr.Blocks(title="RRA FAQ Chatbot") as demo:
+    gr.Markdown(
+        """
+        # RRA FAQ Chatbot
+        Ask tax-related questions in English or Kinyarwanda
+        > 🔒 Your questions and interactions remain private unless you choose to submit feedback, which helps improve our service.
+        """
+    )
+    
+    # Add language selector
+    language = gr.Radio(
+        choices=["English", "Kinyarwanda"],
+        value="English",
+        label="Select Language / Hitamo Ururimi"
+    )
+    
+    chatbot = gr.Chatbot(
+        value=[],
+        show_label=False,
+        height=400,
+        type='messages'
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="Ask your question",
+            placeholder="Type your tax-related question here...",
+            show_label=False
+        )
+        submit = gr.Button("Send")
+    
+    # Add feedback section
+    with gr.Row():
+        with gr.Column(scale=2):
+            feedback_label = gr.Markdown("Was this response helpful?")
+        with gr.Column(scale=1):
+            feedback_positive = gr.Button("👍 Helpful")
+        with gr.Column(scale=1):
+            feedback_negative = gr.Button("👎 Not Helpful")
+    
+    # Add feedback status message
+    feedback_status = gr.Markdown("")
+    
+    # Connect feedback buttons to their respective functions
+    feedback_positive.click(
+        fn=submit_positive_feedback,
+        inputs=[chatbot, language],
+        outputs=feedback_status
+    )
+    
+    feedback_negative.click(
+        fn=submit_negative_feedback,
+        inputs=[chatbot, language],
+        outputs=feedback_status
+    )
+    
+    # Create two sets of examples
+    with gr.Row() as english_examples_row:
+        gr.Examples(
+            examples=[
+                "What is VAT in Rwanda?",
+                "How do I register for taxes?",
+                "What are the tax payment deadlines?",
+                "How can I get a TIN number?",
+                "How do I get purchase code?"
+            ],
+            inputs=msg,
+            label="English Examples"
+        )
+    
+    with gr.Row(visible=False) as kinyarwanda_examples_row:
+        gr.Examples(
+            examples=[
+                "Ese VAT ni iki mu Rwanda?",
+                "Nabona TIN number nte?",
+                "Ni ryari tugomba kwishyura imisoro?",
+                "Ese nandikwa nte ku musoro?",
+                "Ni gute nabone kode yo kugura?"
+            ],
+            inputs=msg,
+            label="Kinyarwanda Examples"
+        )
+    
+    async def respond(message, lang, chat_history):
+        bot_message = await process_query(message, lang, chat_history)
+        chat_history.append({"role": "user", "content": message})
+        chat_history.append({"role": "assistant", "content": bot_message})
+        return "", chat_history
+    
+    def toggle_language_interface(language_choice):
+        if language_choice == "English":
+            placeholder_text = "Type your tax-related question here..."
+            return {
+                msg: gr.update(placeholder=placeholder_text),
+                english_examples_row: gr.update(visible=True),
+                kinyarwanda_examples_row: gr.update(visible=False)
+            }
+        else:
+            placeholder_text = "Andika ibibazo bijyanye n'umusoro hano"
+            return {
+                msg: gr.update(placeholder=placeholder_text),
+                english_examples_row: gr.update(visible=False),
+                kinyarwanda_examples_row: gr.update(visible=True)
+            }
+    
+    msg.submit(respond, [msg, language, chatbot], [msg, chatbot])
+    submit.click(respond, [msg, language, chatbot], [msg, chatbot])
+    
+    # Update both examples visibility and placeholder when language changes
+    language.change(
+        fn=toggle_language_interface,
+        inputs=language,
+        outputs=[msg, english_examples_row, kinyarwanda_examples_row]
+    )
+    
+    gr.Markdown(
+        """
+        ### About
+        - Created by: [Cedric](mailto:[email protected])
+        - Data source: [RRA Website FAQ](https://www.rra.gov.rw/en/domestic-tax-services/faqs)
+        
+        **Disclaimer:** This chatbot provides general tax information. For official guidance, 
+        consult RRA or call 3004.
+        🔒 **Privacy:** Your interactions remain private unless you choose to submit feedback.
+        """
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    try:
+        demo.launch(share=False)
+        logging.info("Gradio app launched successfully.")
+    except Exception as launch_error:
+        logging.critical(f"Failed to launch Gradio app: {launch_error}")
+        raise