Update app.py

app.py

@@ -1,126 +1,122 @@
-import os
-from dotenv import load_dotenv
-import gradio as gr
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
-from llama_index.llms.huggingface import HuggingFaceInferenceAPI
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from sentence_transformers import SentenceTransformer
-
-load_dotenv()
-
-# Configure the Llama index settings
-Settings.llm = HuggingFaceInferenceAPI(
-    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    context_window=3000,
-    token=os.getenv("HF_TOKEN"),
-    max_new_tokens=512,
-    generate_kwargs={"temperature": 0.1},
-)
-Settings.embed_model = HuggingFaceEmbedding(
-    model_name="BAAI/bge-small-en-v1.5"
-)
-
-# Define the directory for persistent storage and data
-PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
-
-# Ensure directories exist
-os.makedirs(PDF_DIRECTORY, exist_ok=True)
-os.makedirs(PERSIST_DIR, exist_ok=True)
-
-# Variable to store current chat conversation
-current_chat_history = []
-
-def data_ingestion_from_directory():
-    # Use SimpleDirectoryReader on the directory containing the PDF files
-    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
-    storage_context = StorageContext.from_defaults()
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
-
-def handle_query(query):
-    chat_text_qa_msgs = [
-        (
-            "user",
-            """
-            You are the JackNJill Solutions chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. 
-    
-            Context:
-            {context_str}
-    
-            Question:
-            {query_str}
-            """
-        )
-    ]
-
-    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
-
-    # Load index from storage
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-
-    # Use chat history to enhance response
-    context_str = ""
-    for past_query, response in reversed(current_chat_history):
-        if past_query.strip():
-            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
-
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
-    answer = query_engine.query(query)
-
-    if hasattr(answer, 'response'):
-        response = answer.response
-    elif isinstance(answer, dict) and 'response' in answer:
-        response = answer['response']
-    else:
-          response = "Sorry, as per my current knowledge I am unable to answer this question. Is there anything else I can help you with?"
-
-    # Remove sensitive information and unwanted sections from the response
-    sensitive_keywords = [PERSIST_DIR, PDF_DIRECTORY, "/", "\\", ".pdf", ".doc", ".txt"]
-    for keyword in sensitive_keywords:
-        response = response.replace(keyword, "")
-
-    # Remove sections starting with specific keywords
-    unwanted_sections = ["Page Label","Page Label:","page_label","page_label:","file_path:","file_path",]
-    for section in unwanted_sections:
-        if section in response:
-            response = response.split(section)[0]
-
-    # Additional cleanup for any remaining artifacts from replacements
-    response = ' '.join(response.split())
-
-    # Update current chat history
-    current_chat_history.append((query, response))
-
-    return response
-
-# Example usage: Process PDF ingestion from directory
-print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
-data_ingestion_from_directory()
-
-# Define the input and output components for the Gradio interface
-input_component = gr.Textbox(
-    show_label=False,
-    placeholder="Ask me anything about JackNJill Solutions..."
-)
-
-output_component = gr.Textbox()
-
-# Function to handle queries
-def chatbot_handler(query):
-    response = handle_query(query)
-    return response
-
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=chatbot_handler,
-    inputs=input_component,
-    outputs=output_component,
-    title="Welcome to JackNJill Solutions",
-    description="I am here to assist you with any questions you have about JackNJill Solutions. How can I help you today?"
-)
-
-# Launch the Gradio interface
-interface.launch()
+import os  
+from dotenv import load_dotenv  
+import gradio as gr  
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings  
+from llama_index.llms.huggingface import HuggingFaceInferenceAPI  
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding  
+
+load_dotenv()  
+
+# Configure the Llama index settings  
+Settings.llm = HuggingFaceInferenceAPI(  
+    model_name="meta-llama/Meta-Llama-3-8B-Instruct",  
+    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",  
+    context_window=3000,  
+    token=os.getenv("HF_TOKEN"),  
+    max_new_tokens=512,  
+    generate_kwargs={"temperature": 0.1},  
+)  
+
+Settings.embed_model = HuggingFaceEmbedding(  
+    model_name="BAAI/bge-small-en-v1.5"  
+)  
+
+# Define the directory for persistent storage and data  
+PERSIST_DIR = "db"  
+PDF_DIRECTORY = 'data'  # Directory containing PDFs  
+
+# Ensure directories exist  
+os.makedirs(PDF_DIRECTORY, exist_ok=True)  
+os.makedirs(PERSIST_DIR, exist_ok=True)  
+
+# Variable to store current chat conversation  
+current_chat_history = []  
+
+def data_ingestion_from_directory():  
+    try:  
+        # Use SimpleDirectoryReader on the directory containing the PDF files  
+        documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()  
+        storage_context = StorageContext.from_defaults()  
+        index = VectorStoreIndex.from_documents(documents)  
+        index.storage_context.persist(persist_dir=PERSIST_DIR)  
+    except Exception as e:  
+        print(f"Error during data ingestion: {e}")  
+
+def handle_query(query):  
+    chat_text_qa_msgs = [  
+        (  
+            "user",  
+            """  
+            You are the JackNJill Solutions chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise.   
+
+            Context:  
+            {context_str}  
+
+            Question:  
+            {query_str}  
+            """  
+        )  
+    ]  
+
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)  
+
+    # Load index from storage  
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)  
+    index = load_index_from_storage(storage_context)  
+
+    # Use chat history to enhance response  
+    context_str = "\n".join([f"User asked: '{past_query}'\nBot answered: '{response}'"  
+                             for past_query, response in reversed(current_chat_history) if past_query.strip()])  
+
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)  
+    answer = query_engine.query(query)  
+
+    response = answer.response if hasattr(answer, 'response') else answer.get('response', "I'm sorry, I can't answer that.")  
+
+    # Remove sensitive information and unwanted sections from the response  
+    sensitive_keywords = [PERSIST_DIR, PDF_DIRECTORY, "/", "\\", ".pdf", ".doc", ".txt"]  
+    for keyword in sensitive_keywords:  
+        response = response.replace(keyword, "")  
+
+    # Remove sections starting with specific keywords  
+    unwanted_sections = ["Page Label", "Page Label:", "page_label", "page_label:", "file_path:", "file_path"]  
+    for section in unwanted_sections:  
+        if section in response:  
+            response = response.split(section)[0]  
+
+    # Additional cleanup for any remaining artifacts from replacements  
+    response = ' '.join(response.split())  
+
+    # Update current chat history  
+    current_chat_history.append((query, response))  
+
+    return response  
+
+# Example usage: Process PDF ingestion from directory  
+print("Processing PDF ingestion from directory:", PDF_DIRECTORY)  
+data_ingestion_from_directory()  
+
+# Define the input and output components for the Gradio interface  
+input_component = gr.Textbox(  
+    show_label=False,  
+    placeholder="Ask me anything about JackNJill Solutions..."  
+)  
+
+output_component = gr.Textbox()  
+
+# Function to handle queries  
+def chatbot_handler(query):  
+    response = handle_query(query)  
+    return response  
+
+# Create the Gradio interface  
+interface = gr.Interface(  
+    fn=chatbot_handler,  
+    inputs=input_component,  
+    outputs=output_component,  
+    title="Welcome to JackNJill Solutions",  
+    description="I am here to assist you with any questions you have about JackNJill Solutions. How can I help you today?"  
+)  
+
+# Launch the Gradio interface  
+interface.launch()