Update app.py

app.py

@@ -1,108 +1,139 @@
-import gradio as gr
-import os
-from aimakerspace.text_utils import PDFLoader, CharacterTextSplitter
-from aimakerspace.vectordatabase import VectorDatabase
-from aimakerspace.openai_utils.prompts import SystemRolePrompt, UserRolePrompt
-from aimakerspace.openai_utils.chatmodel import ChatOpenAI
-from aimakerspace.openai_utils.embedding import EmbeddingModel
-import asyncio
-
-# Initialize the RAG pipeline
-def initialize_rag():
-    # Load the PDF
-    pdf_loader = PDFLoader("data/How-to-Build-a-Career-in-AI.pdf")
-    documents = pdf_loader.load_documents()
-    
-    # Split the documents
-    text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=300)
-    split_documents = text_splitter.split_texts(documents)
-    
-    # Create vector database
-    embedding_model = EmbeddingModel()
-    vector_db = VectorDatabase(embedding_model=embedding_model)
-    vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))
-    
-    # Set up prompts
-    RAG_PROMPT_TEMPLATE = """ \
-    Use the provided context to answer the user's query.
-
-    You may not answer the user's query unless there is specific context in the following text.
-
-    If you do not know the answer, or cannot answer, please respond with "I don't know".
-    """
-    rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
-    
-    USER_PROMPT_TEMPLATE = """ \
-    Context:
-    {context}
-
-    User Query:
-    {user_query}
-    """
-    user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
-    
-    # Create ChatOpenAI instance
-    chat_openai = ChatOpenAI()
-    
-    # Create and return pipeline
-    return RetrievalAugmentedQAPipeline(vector_db_retriever=vector_db, llm=chat_openai)
-
-class RetrievalAugmentedQAPipeline:
-    def __init__(self, llm: ChatOpenAI, vector_db_retriever: VectorDatabase) -> None:
-        self.llm = llm
-        self.vector_db_retriever = vector_db_retriever
-
-    def run_pipeline(self, user_query: str) -> str:
-        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
-        context_prompt = ""
-        for context in context_list:
-            context_prompt += context[0] + "\n"
-        
-        formatted_system_prompt = SystemRolePrompt(""" \
-        Use the provided context to answer the user's query.
-        You may not answer the user's query unless there is specific context in the following text.
-        If you do not know the answer, or cannot answer, please respond with "I don't know".
-        """).create_message()
-        
-        formatted_user_prompt = UserRolePrompt(""" \
-        Context:
-        {context}
-
-        User Query:
-        {user_query}
-        """).create_message(user_query=user_query, context=context_prompt)
-        
-        response = self.llm.run([formatted_system_prompt, formatted_user_prompt])
-        return response
-
-# Create Gradio interface
-def create_interface():
-    # Initialize RAG pipeline
-    rag_pipeline = initialize_rag()
-    
-    def query_rag(question):
-        return rag_pipeline.run_pipeline(question)
-    
-    with gr.Blocks(title="RAG Implementation") as demo:
-        gr.Markdown("# RAG Implementation Demo")
-        gr.Markdown("Ask questions about the 'How to Build a Career in AI' document")
-        
-        with gr.Row():
-            with gr.Column():
-                question = gr.Textbox(label="Your Question", placeholder="Type your question here...")
-                submit_btn = gr.Button("Submit")
-            
-            with gr.Column():
-                answer = gr.Textbox(label="Answer", lines=5)
-        
-        submit_btn.click(
-            fn=query_rag,
-            inputs=question,
-            outputs=answer
-        )
-    
-    return demo
-
-if __name__ == "__main__":
-    demo = create_interface()
+import gradio as gr
+import os
+from aimakerspace.text_utils import PDFLoader, CharacterTextSplitter
+from aimakerspace.vectordatabase import VectorDatabase
+from aimakerspace.openai_utils.prompts import SystemRolePrompt, UserRolePrompt
+from aimakerspace.openai_utils.chatmodel import ChatOpenAI
+from aimakerspace.openai_utils.embedding import EmbeddingModel
+import asyncio
+
+def load_notebook():
+    notebook_path = "Pythonic_RAG_Assignment.ipynb"
+    if os.path.exists(notebook_path):
+        with open(notebook_path, "r", encoding="utf-8") as f:
+            return f.read()
+    return "Notebook not found"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# RAG Implementation Notebook")
+    gr.Markdown("This space contains a Jupyter notebook demonstrating a Retrieval Augmented Generation (RAG) implementation.")
+    
+    with gr.Tabs():
+        with gr.TabItem("Notebook Preview"):
+            notebook_content = gr.Markdown(load_notebook())
+        
+        with gr.TabItem("About"):
+            gr.Markdown("""
+            ## About This Space
+            
+            This space contains a Jupyter notebook that demonstrates:
+            - PDF document processing
+            - Text chunking and embedding
+            - Vector database implementation
+            - RAG pipeline with context-aware responses
+            
+            To run the notebook locally:
+            1. Clone this repository
+            2. Install requirements: `pip install -r requirements.txt`
+            3. Run: `jupyter notebook Pythonic_RAG_Assignment.ipynb`
+            """)
+
+# Initialize the RAG pipeline
+def initialize_rag():
+    # Load the PDF
+    pdf_loader = PDFLoader("data/How-to-Build-a-Career-in-AI.pdf")
+    documents = pdf_loader.load_documents()
+    
+    # Split the documents
+    text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=300)
+    split_documents = text_splitter.split_texts(documents)
+    
+    # Create vector database
+    embedding_model = EmbeddingModel()
+    vector_db = VectorDatabase(embedding_model=embedding_model)
+    vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))
+    
+    # Set up prompts
+    RAG_PROMPT_TEMPLATE = """ \
+    Use the provided context to answer the user's query.
+
+    You may not answer the user's query unless there is specific context in the following text.
+
+    If you do not know the answer, or cannot answer, please respond with "I don't know".
+    """
+    rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
+    
+    USER_PROMPT_TEMPLATE = """ \
+    Context:
+    {context}
+
+    User Query:
+    {user_query}
+    """
+    user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
+    
+    # Create ChatOpenAI instance
+    chat_openai = ChatOpenAI()
+    
+    # Create and return pipeline
+    return RetrievalAugmentedQAPipeline(vector_db_retriever=vector_db, llm=chat_openai)
+
+class RetrievalAugmentedQAPipeline:
+    def __init__(self, llm: ChatOpenAI, vector_db_retriever: VectorDatabase) -> None:
+        self.llm = llm
+        self.vector_db_retriever = vector_db_retriever
+
+    def run_pipeline(self, user_query: str) -> str:
+        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
+        context_prompt = ""
+        for context in context_list:
+            context_prompt += context[0] + "\n"
+        
+        formatted_system_prompt = SystemRolePrompt(""" \
+        Use the provided context to answer the user's query.
+        You may not answer the user's query unless there is specific context in the following text.
+        If you do not know the answer, or cannot answer, please respond with "I don't know".
+        """).create_message()
+        
+        formatted_user_prompt = UserRolePrompt(""" \
+        Context:
+        {context}
+
+        User Query:
+        {user_query}
+        """).create_message(user_query=user_query, context=context_prompt)
+        
+        response = self.llm.run([formatted_system_prompt, formatted_user_prompt])
+        return response
+
+# Create Gradio interface
+def create_interface():
+    # Initialize RAG pipeline
+    rag_pipeline = initialize_rag()
+    
+    def query_rag(question):
+        return rag_pipeline.run_pipeline(question)
+    
+    with gr.Blocks(title="RAG Implementation") as demo:
+        gr.Markdown("# RAG Implementation Demo")
+        gr.Markdown("Ask questions about the 'How to Build a Career in AI' document")
+        
+        with gr.Row():
+            with gr.Column():
+                question = gr.Textbox(label="Your Question", placeholder="Type your question here...")
+                submit_btn = gr.Button("Submit")
+            
+            with gr.Column():
+                answer = gr.Textbox(label="Answer", lines=5)
+        
+        submit_btn.click(
+            fn=query_rag,
+            inputs=question,
+            outputs=answer
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
     demo.launch()