Create app.py

app.py

@@ -0,0 +1,220 @@
+import os
+import streamlit as st
+import tempfile
+import pandas as pd
+import json
+import xml.etree.ElementTree as ET
+import yaml
+from bs4 import BeautifulSoup
+from pptx import Presentation
+from docx import Document
+
+from langchain.document_loaders import PyPDFLoader, TextLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.chains import RetrievalQA
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.foundation_models.extensions.langchain import WatsonxLLM
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+from ibm_watson_machine_learning.foundation_models.utils.enums import DecodingMethods
+
+# Initialize index to None
+index = None
+rag_chain = None  # Initialize rag_chain as None by default
+
+# Custom loader for DOCX files
+class DocxLoader:
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def load(self):
+        document = Document(self.file_path)
+        text_content = [para.text for para in document.paragraphs]
+        return " ".join(text_content)
+
+# Custom loader for PPTX files
+class PptxLoader:
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def load(self):
+        presentation = Presentation(self.file_path)
+        text_content = [shape.text for slide in presentation.slides for shape in slide.shapes if hasattr(shape, "text")]
+        return " ".join(text_content)
+
+# Custom loader for additional file types
+def load_csv(file_path):
+    df = pd.read_csv(file_path)
+    # Adding pagination for large CSV data
+    st.write("Large dataset detected, displaying data in pages.")
+    page_size = 100  # Define the number of rows per page
+    page_number = st.number_input("Page number", min_value=1, max_value=(len(df) // page_size) + 1, step=1, value=1)
+    
+    start_index = (page_number - 1) * page_size
+    end_index = start_index + page_size
+    paginated_data = df.iloc[start_index:end_index]
+    
+    st.dataframe(paginated_data)  # Display paginated data
+    return df.to_string(index=False)
+
+def load_json(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+    return json.dumps(data, indent=2)
+
+def load_xml(file_path):
+    tree = ET.parse(file_path)
+    root = tree.getroot()
+    return ET.tostring(root, encoding="unicode")
+
+def load_yaml(file_path):
+    with open(file_path, 'r') as file:
+        data = yaml.safe_load(file)
+    return yaml.dump(data)
+
+def load_html(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        soup = BeautifulSoup(file, 'html.parser')
+    return soup.get_text()
+
+# Caching function to load various file types
+@st.cache_resource
+def load_file(file_name, file_type):
+    loaders = []
+
+    if file_type == "pdf":
+        loaders = [PyPDFLoader(file_name)]
+    elif file_type == "docx":
+        loader = DocxLoader(file_name)
+        text = loader.load()
+    elif file_type == "pptx":
+        loader = PptxLoader(file_name)
+        text = loader.load()
+    elif file_type == "txt":
+        loaders = [TextLoader(file_name)]
+    elif file_type == "csv":
+        text = load_csv(file_name)
+    elif file_type == "json":
+        text = load_json(file_name)
+    elif file_type == "xml":
+        text = load_xml(file_name)
+    elif file_type == "yaml":
+        text = load_yaml(file_name)
+    elif file_type == "html":
+        text = load_html(file_name)
+    else:
+        st.error("Unsupported file type.")
+        return None
+
+    # Use TextLoader for intermediate text files from custom loaders
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt") as temp_file:
+        temp_file.write(text.encode("utf-8"))
+        temp_file_path = temp_file.name
+    loaders = [TextLoader(temp_file_path)]
+
+    index = VectorstoreIndexCreator(
+        embedding=HuggingFaceEmbeddings(model_name="all-MiniLM-L12-v2"),
+        text_splitter=RecursiveCharacterTextSplitter(chunk_size=450, chunk_overlap=50)
+    ).from_loaders(loaders)
+    return index
+
+# Watsonx API setup
+watsonx_api_key = os.getenv("WATSONX_API_KEY")
+watsonx_project_id = os.getenv("WATSONX_PROJECT_ID")
+
+if not watsonx_api_key or not watsonx_project_id:
+    st.error("API Key or Project ID is not set. Please set them as environment variables.")
+
+prompt_template_br = PromptTemplate(
+    input_variables=["context", "question"],
+    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+I am a helpful assistant.
+
+<|eot_id|>
+{context}
+<|start_header_id|>user<|end_header_id|>
+{question}<|eot_id|>
+"""
+)
+
+with st.sidebar:
+    st.title("Watsonx RAG with Multiple docs")
+    watsonx_model = st.selectbox("Model", ["meta-llama/llama-3-405b-instruct", "codellama/codellama-34b-instruct-hf", "ibm/granite-20b-multilingual"])
+    max_new_tokens = st.slider("Max output tokens", min_value=100, max_value=4000, value=600, step=100)
+    decoding_method = st.radio("Decoding", (DecodingMethods.GREEDY.value, DecodingMethods.SAMPLE.value))
+    parameters = {
+        GenParams.DECODING_METHOD: decoding_method,
+        GenParams.MAX_NEW_TOKENS: max_new_tokens,
+        GenParams.MIN_NEW_TOKENS: 1,
+        GenParams.TEMPERATURE: 0,
+        GenParams.TOP_K: 50,
+        GenParams.TOP_P: 1,
+        GenParams.STOP_SEQUENCES: [],
+        GenParams.REPETITION_PENALTY: 1
+    }
+    st.info("Upload a file to use RAG")
+    uploaded_file = st.file_uploader("Upload file", accept_multiple_files=False, type=["pdf", "docx", "txt", "pptx", "csv", "json", "xml", "yaml", "html"])
+    
+    if uploaded_file is not None:
+        bytes_data = uploaded_file.read()
+        st.write("Filename:", uploaded_file.name)
+        
+        with open(uploaded_file.name, 'wb') as f:
+            f.write(bytes_data)
+
+        file_type = uploaded_file.name.split('.')[-1].lower()
+        index = load_file(uploaded_file.name, file_type)
+
+    model_name = watsonx_model
+
+st.info("Setting up Watsonx...")
+my_credentials = {
+    "url": "https://us-south.ml.cloud.ibm.com",
+    "apikey": watsonx_api_key
+}
+params = parameters
+project_id = watsonx_project_id
+space_id = None
+verify = False
+model = WatsonxLLM(model=Model(model_name, my_credentials, params, project_id, space_id, verify))
+
+if model:
+    st.info(f"Model {model_name} ready.")
+    chain = LLMChain(llm=model, prompt=prompt_template_br, verbose=True)
+
+if chain and index is not None:
+    rag_chain = RetrievalQA.from_chain_type(
+        llm=model,
+        chain_type="stuff",
+        retriever=index.vectorstore.as_retriever(),
+        chain_type_kwargs={"prompt": prompt_template_br},
+        return_source_documents=False,
+        verbose=True
+    )
+    st.info("Document-based retrieval is ready.")
+else:
+    st.warning("No document uploaded or chain setup issue.")
+
+# Chat loop
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    st.chat_message(message["role"]).markdown(message["content"])
+
+prompt = st.chat_input("Ask your question here", disabled=False if chain else True)
+
+if prompt:
+    st.chat_message("user").markdown(prompt)
+    if rag_chain:
+        response_text = rag_chain.run(prompt).strip()
+    else:
+        response_text = chain.run(question=prompt, context="").strip()
+        
+    st.session_state.messages.append({'role': 'User', 'content': prompt})
+    st.chat_message("assistant").markdown(response_text)
+    st.session_state.messages.append({'role': 'Assistant', 'content': response_text})