Update app.py

app.py

@@ -1,64 +1,252 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import streamlit as st
+import boto3
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_aws import BedrockEmbeddings
+# --- CHANGED: Import Qdrant instead of Chroma ---
+from langchain_qdrant import Qdrant 
+# --- Optional: If you need direct Qdrant client interaction or for advanced setups ---
+# from qdrant_client import QdrantClient, models 
+
+from langchain_aws import ChatBedrock
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+import os
+from dotenv import load_dotenv # Import load_dotenv
+
+# --- Load Environment Variables ---
+load_dotenv() # This loads variables from .env file
+
+# --- Streamlit UI Setup (MUST BE THE FIRST STREAMLIT COMMAND) ---
+st.set_page_config(
+    page_title="Math Research Paper RAG Bot",
+    page_icon="📚",
+    layout="wide"
+)
+
+st.title("📚 Math Research Paper RAG Chatbot")
+st.markdown(
+    """
+    Upload a mathematical research paper (PDF) and ask questions about its content. 
+    This bot uses Amazon Bedrock (Claude 3 Sonnet for reasoning, Titan Embeddings for vectors) 
+    and **QdrantDB** for Retrieval-Augmented Generation.
+    
+    **Note:** This application requires AWS credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`) 
+    and region (`AWS_REGION`) to be set up in a `.env` file or environment variables.
+    The Qdrant vector store is **in-memory** and will be reset on app restart.
+    """
 )
 
+# --- Configuration ---
+# Set AWS region (adjust if needed, loaded from .env or env var)
+AWS_REGION = os.getenv("AWS_REGION") 
+if not AWS_REGION:
+    st.error("AWS_REGION not found in environment variables or .env file. Please set it.")
+    st.stop()
+
+# Bedrock model IDs
+EMBEDDING_MODEL_ID = "amazon.titan-embed-text-v1"
+LLM_MODEL_ID = "anthropic.claude-3-sonnet-20240229-v1:0" 
+
+# --- Qdrant Specific Configuration ---
+QDRANT_COLLECTION_NAME = "math_research_papers_collection"
+EMBEDDING_DIMENSION = 1536 # Titan Text Embeddings output 1536-dimensional vectors
+
+# --- Initialize Bedrock Client (once) ---
+@st.cache_resource
+def get_bedrock_client():
+    """Initializes and returns a boto3 Bedrock client.
+    Returns: Tuple (boto3_client, success_bool, error_message_str or None)
+    """
+    try:
+        client = boto3.client(
+            service_name="bedrock-runtime",
+            region_name=AWS_REGION
+        )
+        return client, True, None # Success: client, True, no error message
+    except Exception as e:
+        return None, False, str(e) # Failure: None, False, error message
+
+# Get the client and check its status
+bedrock_client, bedrock_success, bedrock_error_msg = get_bedrock_client()
+
+if not bedrock_success:
+    st.error(f"Error connecting to AWS Bedrock. Please check your AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) and region (AWS_REGION) in your .env file or environment variables. Error: {bedrock_error_msg}")
+    st.stop() # Stop execution if Bedrock client cannot be initialized
+else:
+    st.success(f"Successfully connected to AWS Bedrock in {AWS_REGION}!")
+
+
+# --- LangChain Components ---
+@st.cache_resource
+def get_embeddings_model(_client): # Prepend underscore to tell Streamlit not to hash
+    """Returns the BedrockEmbeddings model."""
+    return BedrockEmbeddings(client=_client, model_id=EMBEDDING_MODEL_ID)
+
+@st.cache_resource
+def get_llm_model(_client): # Prepend underscore to tell Streamlit not to hash
+    """Returns the Bedrock LLM model for Claude 3 Sonnet."""
+    return ChatBedrock(
+        client=_client,
+        model_id=LLM_MODEL_ID,
+        streaming=False, 
+        temperature=0.1, 
+        model_kwargs={"max_tokens": 4000}
+    )
+
+# --- PDF Processing and Vector Store Creation ---
+def create_vector_store(pdf_file_path):
+    """
+    Loads PDF, chunks it contextually for mathematical papers,
+    creates embeddings, and stores them in QdrantDB (in-memory).
+    """
+    with st.spinner("Loading PDF and creating vector store..."):
+        # 1. Load PDF
+        loader = PyPDFLoader(pdf_file_path)
+        pages = loader.load_and_split()
+        st.info(f"Loaded {len(pages)} pages from the PDF.")
+
+        # 2. Contextual Chunking for Mathematical Papers
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1500,  # Increased chunk size for math papers
+            chunk_overlap=150, # Generous overlap to maintain context
+            separators=[
+                "\n\n",  # Prefer splitting by paragraphs
+                "\n",    # Then by newlines (might break equations but less likely than fixed char)
+                " ",     # Then by spaces
+                "",      # Fallback
+            ],
+            length_function=len,
+            is_separator_regex=False,
+        )
+        chunks = text_splitter.split_documents(pages)
+        st.info(f"Split PDF into {len(chunks)} chunks.")
+
+        # 3. Create Embeddings and QdrantDB
+        embeddings = get_embeddings_model(bedrock_client)
+        
+        # --- CHANGED: Qdrant vector store creation ---
+        vector_store = Qdrant.from_documents(
+            documents=chunks,
+            embedding=embeddings,
+            location=":memory:",  # Use in-memory Qdrant instance
+            collection_name=QDRANT_COLLECTION_NAME,
+            # For persistent Qdrant (requires a running Qdrant server):
+            # url="http://localhost:6333", # Or your Qdrant Cloud URL
+            # api_key="YOUR_QDRANT_CLOUD_API_KEY", # Only for Qdrant Cloud
+            # prefer_grpc=True # Set to True if using gRPC for Qdrant Cloud
+            # force_recreate=True # Use with caution: deletes existing collection
+        )
+        # Note: LangChain's Qdrant integration will automatically create the collection
+        # if it doesn't exist, inferring vector_size from embeddings.
+        
+        st.success("Vector store created and ready!")
+        return vector_store
+
+# --- RAG Chain Construction ---
+def get_rag_chain(vector_store):
+    """Constructs the RAG chain using LCEL."""
+    retriever = vector_store.as_retriever(search_kwargs={"k": 5}) # Retrieve top 5 relevant chunks
+    llm = get_llm_model(bedrock_client) 
+
+    # Prompt Template optimized for mathematical research papers
+    prompt_template = ChatPromptTemplate.from_messages(
+        [
+            ("system", 
+             "You are an expert AI assistant specialized in analyzing and explaining mathematical research papers. "
+             "Your goal is to provide precise, accurate, and concise answers based *only* on the provided context from the research paper. "
+             "When answering, focus on definitions, theorems, proofs, key mathematical concepts, and experimental results. "
+             "If the user asks about a mathematical notation, try to explain its meaning from the context. "
+             "If the answer is not found in the context, explicitly state that you cannot find the information within the provided document. "
+             "Do not invent information or make assumptions outside the given text.\n\n"
+             "Context:\n{context}"),
+            ("user", "{question}"),
+        ]
+    )
+
+    rag_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt_template
+        | llm
+        | StrOutputParser()
+    )
+    return rag_chain
+
+# --- Streamlit UI Main Logic ---
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Initialize vector store and RAG chain
+if "vector_store" not in st.session_state:
+    st.session_state.vector_store = None
+if "rag_chain" not in st.session_state:
+    st.session_state.rag_chain = None
+if "pdf_uploaded" not in st.session_state:
+    st.session_state.pdf_uploaded = False
+
+
+# Sidebar for PDF Upload
+with st.sidebar:
+    st.header("Upload PDF")
+    uploaded_file = st.file_uploader(
+        "Choose a PDF file",
+        type="pdf",
+        accept_multiple_files=False,
+        key="pdf_uploader"
+    )
+
+    if uploaded_file and not st.session_state.pdf_uploaded:
+        # Save the uploaded file temporarily
+        with open("temp_doc.pdf", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        
+        st.session_state.vector_store = create_vector_store("temp_doc.pdf")
+        st.session_state.rag_chain = get_rag_chain(st.session_state.vector_store)
+        st.session_state.pdf_uploaded = True
+        st.success("PDF processed successfully! You can now ask questions.")
+        # Clean up temporary file
+        os.remove("temp_doc.pdf")
+    elif st.session_state.pdf_uploaded:
+        st.info("PDF already processed. Ready for questions!")
+
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("Ask a question about the paper..."):
+    if not st.session_state.pdf_uploaded:
+        st.warning("Please upload a PDF first to start asking questions.")
+    else:
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Get response from RAG chain
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                try:
+                    full_response = st.session_state.rag_chain.invoke(prompt) 
+                    st.markdown(full_response, unsafe_allow_html=True) 
+
+                    # Add assistant response to chat history
+                    st.session_state.messages.append({"role": "assistant", "content": full_response})
+                except Exception as e:
+                    st.error(f"An error occurred during response generation: {e}")
+                    st.warning("Please try again or check your AWS Bedrock access permissions.")
 
-if __name__ == "__main__":
-    demo.launch()
+# Optional: Clear chat and uploaded PDF
+if st.session_state.pdf_uploaded:
+    if st.sidebar.button("Clear Chat and Upload New PDF"):
+        st.session_state.messages = []
+        st.session_state.vector_store = None
+        st.session_state.rag_chain = None
+        st.session_state.pdf_uploaded = False
+        st.cache_resource.clear() # Clear streamlit caches for a clean slate
+        st.rerun()