Create app.py

app.py

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+# Import necessary libraries
+import gradio as gr
+from langchain.document_loaders import OnlinePDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import HuggingFaceHub
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+
+# Define a function to display "Loading..." when loading a PDF
+def loading_pdf():
+    return "Loading..."
+
+# Define a function to process PDF changes
+def pdf_changes(pdf_doc, repo_id):
+    # Initialize the OnlinePDFLoader to load the PDF document
+    loader = OnlinePDFLoader(pdf_doc.name)
+    documents = loader.load()
+
+    # Split the loaded documents into chunks using CharacterTextSplitter
+    text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=50)
+    texts = text_splitter.split_documents(documents)
+
+    # Initialize HuggingFaceHubEmbeddings for embeddings
+    embeddings = HuggingFaceHubEmbeddings()
+
+    # Create a Chroma vector store from the text chunks and embeddings
+    db = Chroma.from_documents(texts, embeddings)
+
+    # Convert the vector store to a retriever
+    retriever = db.as_retriever()
+
+    # Initialize an HuggingFaceHub language model (LLM)
+    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.25, "max_new_tokens": 1000})
+
+    # Create a RetrievalQA chain with the LLM, retriever, and return_source_documents option
+    global qa
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
+
+    return "Ready"
+
+# Define a function to add text to a history
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ""
+
+# Define a bot function to generate responses
+def bot(history):
+    response = infer(history[-1][0])
+    history[-1][1] = response['result']
+    return history
+
+# Define an inference function to query the LLM
+def infer(query):
+    result = qa({"query": query})
+    return result
+
+# Define custom CSS styles
+css = """
+#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+"""
+
+# Define a title HTML for the interface
+title = """
+<div style="text-align: center;max-width: 700px;">
+    <h1>Chat with PDF</h1>
+    <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
+    when everything is ready, you can start asking questions about the PDF ;)</p>
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(title)
+
+        with gr.Column():
+            # Create a file input for loading PDF
+            pdf_doc = gr.File(label="Load a PDF", file_types=['.pdf'], type="file", value="AhmedS_Resume.pdf")
+
+            # Create a dropdown for selecting the LLM
+            repo_id = gr.Dropdown(label="LLM", choices=["HuggingFaceH4/zephyr-7b-alpha", "CausalLM/14B", "meta-llama/Llama-2-7b-chat-hf"], value="HuggingFaceH4/zephyr-7b-alpha")
+
+            with gr.Row():
+                langchain_status = gr.Textbox(label="Status", placeholder="Waiting...", interactive=False)
+                load_pdf = gr.Button("Load PDF to LangChain")
+
+        chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
+        query = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
+        submit_btn = gr.Button("Send message")
+
+    # Set up actions for UI elements
+    repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
+    load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
+    question.submit(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
+    submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
+
+# Launch the Gradio interface
+demo.launch()