refactored for readability

app.py

@@ -33,7 +33,10 @@ dotenv.load_dotenv()
 
 
 
-text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)
+text_splitter = CharacterTextSplitter(
+    chunk_size=350, 
+    chunk_overlap=0
+    )
 
 # flan_ul2 = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":300})
 # flan_ul2 = OpenAI()
@@ -48,7 +51,7 @@ flan_ul2 = chat = ChatOpenAI(
 
 global qa
 
-# embeddings = HuggingFaceHubEmbeddings()
+
 COHERE_API_KEY = os.getenv("COHERE_API_KEY")
 embeddings = CohereEmbeddings(
     model="embed-english-v3.0",
@@ -57,17 +60,14 @@ embeddings = CohereEmbeddings(
            
 
 
-
-
 def loading_pdf():
     return "Loading..."
 def pdf_changes(pdf_doc):
-    # embeddings = OpenAIEmbeddings()
-    # embeddings = HuggingFaceHubEmbeddings()
+
 
     embeddings = CohereEmbeddings(
     model="embed-english-light-v3.0",
-    # cohere_api_key=COHERE_API_KEY
+
 )
     
     loader = PyPDFLoader(pdf_doc.name)
@@ -75,44 +75,52 @@ def pdf_changes(pdf_doc):
     texts = text_splitter.split_documents(documents)
     db = Chroma.from_documents(texts, embeddings)
     retriever = db.as_retriever()
-    # memory = VectorStoreRetrieverMemory(retriever=retriever)
-    memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
-    
-    # prompt_template = """You have been given a pdf or pdfs. You must search these pdfs. 
-    # If you don't know the answer, just say that you don't know, don't try to make up an answer.
-    # Only answer the question.
-    
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history", 
+        input_key="human_input"
+        )
     
-    # Question: {query}
-    # Answer:"""
-    # PROMPT = PromptTemplate(
-    #     template=prompt_template, input_variables=["context", "question"]
-    # )
 
-    prompt = PromptTemplate(input_variables=["chat_history", "human_input", "context"], template=template)
+
+    prompt = PromptTemplate(
+        input_variables=[
+            "chat_history",
+            "human_input", 
+            "context"
+            ], 
+            template=template
+            )
+
     chain_type_kwargs = {"prompt": prompt}
+
     global qa 
-    # qa = RetrievalQA.from_chain_type(
-    #     llm=flan_ul2, 
-    #     memory=memory,
-    #     chain_type="stuff", 
-    #     retriever=retriever, 
-    #     return_source_documents=True,
-    #     chain_type_kwargs=chain_type_kwargs,
-    # )
+
 
     prompt = PromptTemplate(
-    input_variables=["history", "context", "question"],
+    input_variables=[
+        "history", 
+        "context", 
+        "question"
+        ],
     template=template,
 )
-    memory = ConversationBufferMemory(memory_key="history", input_key="question")
-
-    qa = RetrievalQAWithSourcesChain.from_chain_type(llm=flan_ul2, retriever=retriever, return_source_documents=True, verbose=True, chain_type_kwargs={
-        "verbose": True,
-        "memory": memory,
-        "prompt": prompt,
-        "document_variable_name": "context"
-    }
+    memory = ConversationBufferMemory(
+        memory_key="history", 
+        input_key="question"
+        )
+
+    qa = RetrievalQAWithSourcesChain.from_chain_type(
+        llm=flan_ul2, 
+        retriever=retriever, 
+        return_source_documents=True, 
+        verbose=True, 
+        chain_type_kwargs={
+            "verbose": True,
+            "memory": memory,
+            "prompt": prompt,
+            "document_variable_name": "context"
+            }
         )
 
     return "Ready"
@@ -126,21 +134,14 @@ def bot(history):
     history[-1][1] = response['answer']
     return history
 
-# def bot(history):
-#     response = infer(history[-1][0], history)
-#     sources = [doc.metadata.get("source") for doc in response['source_documents']]
-#     src_list = '\n'.join(sources)
-#     print_this = response['answer'] + "\n\n\n Sources: \n\n\n" + src_list
-#     return print_this
+
 
 def infer(question, history) -> dict:
     
     query = question
-    # result = qa({"query": query, "context":""})
-    # result = qa({"query": query, })
+
     result = qa({"query": query, "history": history, "question": question})
 
-    # result = result['answer']
     return result
 
 css="""
@@ -159,22 +160,57 @@ title = """
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.HTML(title)
-        # with gr.Blocks() as demo:
+
         
         with gr.Column():
             pdf_doc = gr.File()
-            # pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="filepath") #try filepath for type if binary does not work
+
             with gr.Row():
-                langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
+                langchain_status = gr.Textbox(
+                    label="Status", 
+                    placeholder="", 
+                    interactive=False
+                    )
                 load_pdf = gr.Button("Load pdf to langchain")
         
-        chatbot = gr.Chatbot([], elem_id="chatbot") #.style(height=350)
+        chatbot = gr.Chatbot(
+            [], 
+            elem_id="chatbot"
+            ) #.style(height=350)
+        
         with gr.Row():
-            question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
-    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
-    load_pdf.click(pdf_changes, pdf_doc, langchain_status, queue=False)
-    question.submit(add_text, [chatbot, question], [chatbot, question]).then(
-        bot, chatbot, chatbot
+            question = gr.Textbox(
+                label="Question", 
+                placeholder="Type your question and hit Enter "
+                )
+
+    load_pdf.click(
+        loading_pdf, 
+        None, 
+        langchain_status, 
+        queue=False
+        )
+    
+    load_pdf.click(
+        pdf_changes, 
+        pdf_doc, 
+        langchain_status, 
+        queue=False
+        )
+    
+    question.submit
+    (add_text, 
+     [
+        chatbot, 
+        question
+      ], 
+      [
+          chatbot, 
+          question]
+          ).then(
+        bot, 
+        chatbot, 
+        chatbot
     )
 
 demo.launch()