Create app.py

app.py

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+import gradio as gr
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+
+from langchain.document_loaders import PyPDFLoader
+import os
+
+import fitz
+from PIL import Image
+
+# Global variables
+COUNT, N = 0, 0
+chat_history = []
+chain = ''
+enable_box = gr.Textbox.update(value=None, 
+                          placeholder='Upload your OpenAI API key', interactive=True)
+disable_box = gr.Textbox.update(value='OpenAI API key is Set', interactive=False)
+
+# Function to set the OpenAI API key
+def set_apikey(api_key):
+    os.environ['OPENAI_API_KEY'] = api_key
+    return disable_box
+
+# Function to enable the API key input box
+def enable_api_box():
+    return enable_box
+
+# Function to add text to the chat history
+def add_text(history, text):
+    if not text:
+        raise gr.Error('Enter text')
+    history = history + [(text, '')]
+    return history
+
+# Function to process the PDF file and create a conversation chain
+def process_file(file):
+    if 'OPENAI_API_KEY' not in os.environ:
+        raise gr.Error('Upload your OpenAI API key')
+
+    loader = PyPDFLoader(file.name)
+    documents = loader.load()
+
+    embeddings = OpenAIEmbeddings()
+    
+    pdfsearch = Chroma.from_documents(documents, embeddings)
+
+    chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0.3), 
+                                   retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
+                                   return_source_documents=True)
+    return chain
+
+# Function to generate a response based on the chat history and query
+def generate_response(history, query, btn):
+    global COUNT, N, chat_history, chain
+    
+    if not btn:
+        raise gr.Error(message='Upload a PDF')
+    if COUNT == 0:
+        chain = process_file(btn)
+        COUNT += 1
+    
+    result = chain({"question": query, 'chat_history': chat_history}, return_only_outputs=True)
+    chat_history += [(query, result["answer"])]
+    N = list(result['source_documents'][0])[1][1]['page']
+
+    for char in result['answer']:
+        history[-1][-1] += char
+        yield history, ''
+
+# Function to render a specific page of a PDF file as an image
+def render_file(file):
+    global N
+    doc = fitz.open(file.name)
+    page = doc[N]
+    # Render the page as a PNG image with a resolution of 300 DPI
+    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
+    image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
+    return image
+
+# Gradio application setup
+with gr.Blocks() as demo:
+    # Create a Gradio block
+
+    with gr.Column():
+        with gr.Row():
+            with gr.Column(scale=0.8):
+                api_key = gr.Textbox(
+                    placeholder='Enter OpenAI API key',
+                    show_label=False,
+                    interactive=True
+                ).style(container=False)
+            with gr.Column(scale=0.2):
+                change_api_key = gr.Button('Change Key')
+
+        with gr.Row():
+            chatbot = gr.Chatbot(value=[], elem_id='chatbot').style(height=650)
+            show_img = gr.Image(label='Upload PDF', tool='select').style(height=680)
+
+    with gr.Row():
+        with gr.Column(scale=0.70):
+            txt = gr.Textbox(
+                show_label=False,
+                placeholder="Enter text and press enter"
+            ).style(container=False)
+
+        with gr.Column(scale=0.15):
+            submit_btn = gr.Button('Submit')
+
+        with gr.Column(scale=0.15):
+            btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"]).style()
+
+    # Set up event handlers
+
+    # Event handler for submitting the OpenAI API key
+    api_key.submit(fn=set_apikey, inputs=[api_key], outputs=[api_key])
+
+    # Event handler for changing the API key
+    change_api_key.click(fn=enable_api_box, outputs=[api_key])
+
+    # Event handler for uploading a PDF
+    btn.upload(fn=render_first, inputs=[btn], outputs=[show_img])
+
+    # Event handler for submitting text and generating response
+    submit_btn.click(
+        fn=add_text,
+        inputs=[chatbot, txt],
+        outputs=[chatbot],
+        queue=False
+    ).success(
+        fn=generate_response,
+        inputs=[chatbot, txt, btn],
+        outputs=[chatbot, txt]
+    ).success(
+        fn=render_file,
+        inputs=[btn],
+        outputs=[show_img]
+    )
+demo.queue()
+if __name__ == "__main__":
+    demo.launch()