Update gradio_app.py

gradio_app.py

@@ -1,27 +1,22 @@
 import gradio as gr
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
-from llama_index.llms.huggingface import HuggingFaceInferenceAPI
-from dotenv import load_dotenv
+from llama_index.core import (
+    StorageContext,
+    load_index_from_storage,
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    ChatPromptTemplate,
+)
+from llama_index.llms.huggingface import HuggingFaceInferencePipeline
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.core import Settings
 import os
 import tempfile
+from pathlib import Path
+import logging
 
-# Load environment variables
-load_dotenv()
-
-# Configure the Llama index settings
-Settings.llm = HuggingFaceInferenceAPI(
-    model_name="google/gemma-1.1-7b-it",
-    tokenizer_name="google/gemma-1.1-7b-it",
-    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"
-)
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # Define the directory for persistent storage and data
 PERSIST_DIR = "./db"
@@ -31,57 +26,79 @@ DATA_DIR = "data"
 os.makedirs(DATA_DIR, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
+# Configure the Llama index settings
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="BAAI/bge-small-en-v1.5"
+)
+
 def data_ingestion():
-    documents = SimpleDirectoryReader(DATA_DIR).load_data()
-    storage_context = StorageContext.from_defaults()
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
+    try:
+        documents = SimpleDirectoryReader(DATA_DIR).load_data()
+        if not documents:
+            logger.warning("No documents loaded from the data directory.")
+            return False
+        storage_context = StorageContext.from_defaults()
+        index = VectorStoreIndex.from_documents(documents)
+        index.storage_context.persist(persist_dir=PERSIST_DIR)
+        return True
+    except Exception as e:
+        logger.error(f"Error during data ingestion: {str(e)}")
+        return False
 
 def handle_query(query):
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-    chat_text_qa_msgs = [
-    (
-        "user",
-        """You are a Q&A assistant named EazyPeazy, For all other inquiries, your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
-        Context:
-        {context_str}
-        Question:
-        {query_str}
-        """
-    )
-    ]
-    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
-    
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
-    answer = query_engine.query(query)
-    
-    if hasattr(answer, 'response'):
-        return answer.response
-    elif isinstance(answer, dict) and 'response' in answer:
-        return answer['response']
-    else:
-        return "Sorry, I couldn't find an answer."
+    try:
+        storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+        index = load_index_from_storage(storage_context)
+        chat_text_qa_msgs = [
+            (
+                "user",
+                """You are a Q&A assistant named EazyPeazy. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
+                Context:
+                {context_str}
+                Question:
+                {query_str}
+                """
+            )
+        ]
+        text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+        
+        query_engine = index.as_query_engine(text_qa_template=text_qa_template)
+        answer = query_engine.query(query)
+        
+        if hasattr(answer, 'response'):
+            return answer.response
+        elif isinstance(answer, dict) and 'response' in answer:
+            return answer['response']
+        else:
+            return "Sorry, I couldn't find an answer."
+    except Exception as e:
+        logger.error(f"Error handling query: {str(e)}")
+        return "An error occurred while processing your query. Please try again."
 
 def process_file(file):
     if file is None:
         return "Please upload a PDF file."
     
-    temp_dir = tempfile.mkdtemp()
-    temp_path = os.path.join(temp_dir, "uploaded.pdf")
-    
-    with open(temp_path, "wb") as f:
-        f.write(file.read())
-    
-    # Copy the file to the DATA_DIR
-    os.makedirs(DATA_DIR, exist_ok=True)
-    dest_path = os.path.join(DATA_DIR, "saved_pdf.pdf")
-    os.replace(temp_path, dest_path)
-    
-    # Process the uploaded PDF
-    data_ingestion()
-    
-    return "PDF processed successfully. You can now ask questions about its content."
+    try:
+        temp_dir = tempfile.mkdtemp()
+        temp_path = Path(temp_dir) / "uploaded.pdf"
+        
+        with open(temp_path, "wb") as f:
+            f.write(file.read())
+        
+        # Copy the file to the DATA_DIR
+        dest_path = Path(DATA_DIR) / "saved_pdf.pdf"
+        dest_path.parent.mkdir(parents=True, exist_ok=True)
+        temp_path.replace(dest_path)
+        
+        # Process the uploaded PDF
+        if data_ingestion():
+            return "PDF processed successfully. You can now ask questions about its content."
+        else:
+            return "Failed to process the PDF. Please try uploading again."
+    except Exception as e:
+        logger.error(f"Error processing file: {str(e)}")
+        return f"An error occurred while processing the file: {str(e)}"
 
 def chat_function(message, history):
     response = handle_query(message)