Create app.py

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import os
+import tempfile
+from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.chains import RetrievalQA
+from langchain.schema import AIMessage, HumanMessage
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain import HuggingFaceHub
+import time
+
+# Set your API keys
+API_KEY = os.environ["HUGGINGFACEHUB_API_TOKEN"] 
+
+# Create a temporary upload directory
+upload_dir = tempfile.mkdtemp()
+
+# Define global variables for loaders and index
+index = None
+
+def load_file(pdf_file, progress=gr.Progress()):
+    global index
+    uploaded_pdf_path = os.path.join(upload_dir, pdf_file.name)
+    pdf_loader = UnstructuredPDFLoader(uploaded_pdf_path)
+    index = VectorstoreIndexCreator(
+    embedding=HuggingFaceEmbeddings(),
+    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    ).from_loaders([pdf_loader])
+
+def chat(message,history):
+    global index
+    history_langchain_format = []
+    for human, ai in history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
+    history_langchain_format.append(HumanMessage(content=message))
+    history_langchain_format.append(HumanMessage(content=message))
+    # Create the index (update index)
+    llm2 = HuggingFaceHub(repo_id="declare-lab/flan-alpaca-large", model_kwargs={"temperature": 0, "max_length": 512},API_KEY )
+    chain = RetrievalQA.from_chain_type(llm=llm2,
+                                        chain_type="stuff",
+                                        retriever=index.vectorstore.as_retriever(),
+                                        input_key="question")
+    # Perform question-answering on the uploaded PDF with the user's question
+    gpt_response = chain.run(message)
+    return gpt_response
+
+
+# Create a Gradio interface for chat
+chat_interface = gr.ChatInterface(
+    chat,
+    theme=gr.themes.Soft()
+)
+
+
+upload_interface = gr.Interface(
+    fn=load_file,
+    inputs=[
+        gr.File(label="Upload a PDF",file_types=["pdf"]),
+    ],
+    outputs="text",
+    title="PDF Question Answering",
+    description="Upload a PDF, enter a question, and get an answer from the model.",
+    theme=gr.themes.Soft()
+)
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Row():
+                upload_file = gr.File(label="Upload a PDF",file_types=["pdf"])
+            with gr.Row():
+                upload_button = gr.Button(label="Upload a PDF")
+            with gr.Row():
+                text = gr.Textbox(label="Status")
+            def load_file(pdf_file):
+                global index
+                uploaded_pdf_path = os.path.join(upload_dir, pdf_file.name)
+                pdf_loader = UnstructuredPDFLoader(uploaded_pdf_path)
+                index = VectorstoreIndexCreator(
+                embedding=HuggingFaceEmbeddings(),
+                text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                ).from_loaders([pdf_loader])
+                return "DONE ✅"
+            upload_button.click(load_file, [upload_file], text)
+        with gr.Column(scale=2):
+            chat_interface = gr.ChatInterface(
+                            chat,
+                            theme=gr.themes.Soft()
+                        )
+
+demo.queue().launch(inline=False)