Update

app.py

@@ -8,6 +8,7 @@ from langchain_core.prompts import ChatPromptTemplate
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain.chains import create_retrieval_chain
 
+
 def initialise_chatbot(pdf, llm, progress=gr.Progress()):
     progress(0, desc="Reading PDF")
 
@@ -18,10 +19,7 @@ def initialise_chatbot(pdf, llm, progress=gr.Progress()):
 
     progress(0.25, desc="Initialising Vectorstore")
 
-    vectorstore = Chroma.from_documents(
-        splits,
-        embedding=HuggingFaceEmbeddings()
-    )
+    vectorstore = Chroma.from_documents(splits, embedding=HuggingFaceEmbeddings())
 
     progress(0.85, desc="Initialising LLM")
 
@@ -30,13 +28,10 @@ def initialise_chatbot(pdf, llm, progress=gr.Progress()):
         task="text-generation",
         max_new_tokens=512,
         top_k=4,
-        temperature=0.05
+        temperature=0.05,
     )
 
-    chat = ChatHuggingFace(
-        llm=llm, 
-        verbose=True
-    )
+    chat = ChatHuggingFace(llm=llm, verbose=True)
 
     retriever = vectorstore.as_retriever(search_kwargs={"k": 8})
 
@@ -62,35 +57,55 @@ def initialise_chatbot(pdf, llm, progress=gr.Progress()):
 
     return rag_chain, "Complete!"
 
+
 def send(message, rag_chain, chat_history):
     response = rag_chain.invoke({"input": message})
     chat_history.append((message, response["answer"]))
 
     return "", chat_history
 
-with gr.Blocks() as demo:
 
+with gr.Blocks() as demo:
     vectorstore = gr.State()
     rag_chain = gr.State()
-    
+
     gr.Markdown("<H1>Talk to Documents</H1>")
     gr.Markdown("<H3>Upload and ask questions about your PDF files</H3>")
-    gr.Markdown("<H6>Note: This project uses LangChain to perform RAG (Retrieval Augmented Generation) on PDF files, allowing users to ask any questions related to their contents. When a PDF file is uploaded, it is embedded and stored in an in-memory Chroma vectorstore, which the chatbot uses as a source of knowledge when aswering user questions.</H6>")
+    gr.Markdown(
+        "<H6>Note: This project uses LangChain to perform RAG (Retrieval Augmented Generation) on PDF files, allowing users to ask any questions related to their contents. When a PDF file is uploaded, it is embedded and stored in an in-memory Chroma vectorstore, which the chatbot uses as a source of knowledge when aswering user questions.</H6>"
+    )
 
     with gr.Row():
         with gr.Column(scale=1):
             input_pdf = gr.File(label="1. Upload PDF")
-            language_model = gr.Radio(label="2. Choose LLM", choices=["microsoft/Phi-3-mini-4k-instruct", "mistralai/Mistral-7B-Instruct-v0.2", "HuggingFaceH4/zephyr-7b-beta", "mistralai/Mixtral-8x7B-Instruct-v0.1"])
-            initialise_chatbot_btn = gr.Button(value="3. Initialise Chatbot", variant='primary')
-            chatbot_initialisation_progress = gr.Textbox(value="Not Started", label="Initialization Progress")
+            language_model = gr.Radio(
+                label="2. Choose LLM",
+                choices=[
+                    "microsoft/Phi-3-mini-4k-instruct",
+                    "mistralai/Mistral-7B-Instruct-v0.2",
+                    "HuggingFaceH4/zephyr-7b-beta",
+                    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+                ],
+            )
+            initialise_chatbot_btn = gr.Button(
+                value="3. Initialise Chatbot", variant="primary"
+            )
+            chatbot_initialisation_progress = gr.Textbox(
+                value="Not Started", label="Initialization Progress"
+            )
 
         with gr.Column(scale=4):
             chatbot = gr.Chatbot(scale=1)
             message = gr.Textbox(label="4. Ask questions about your PDF")
 
     initialise_chatbot_btn.click(
-        fn=initialise_chatbot, inputs=[input_pdf, language_model], outputs=[rag_chain, chatbot_initialisation_progress]
+        fn=initialise_chatbot,
+        inputs=[input_pdf, language_model],
+        outputs=[rag_chain, chatbot_initialisation_progress],
     )
-    message.submit(fn=send, inputs=[message, rag_chain, chatbot], outputs=[message, chatbot])
+    message.submit(
+        fn=send, inputs=[message, rag_chain, chatbot], outputs=[message, chatbot]
+    )
+
+demo.launch()
 
-demo.launch()