Update app.py

app.py

@@ -1,51 +1,69 @@
 import gradio as gr
+import os
+import time
 
 from langchain.document_loaders import OnlinePDFLoader
 
 from langchain.text_splitter import CharacterTextSplitter
 
-from langchain.llms import HuggingFaceHub
 
-from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.llms import OpenAI
 
-from langchain.vectorstores import Chroma
-
-from langchain.chains import RetrievalQA
+from langchain.embeddings import OpenAIEmbeddings
 
+from langchain.vectorstores import Chroma
 
+from langchain.chains import ConversationalRetrievalChain
 
 def loading_pdf():
     return "Loading..."
 
-def pdf_changes(pdf_doc, repo_id):
-    
-    loader = OnlinePDFLoader(pdf_doc.name)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0)
-    texts = text_splitter.split_documents(documents)
-    embeddings = HuggingFaceHubEmbeddings()
-    db = Chroma.from_documents(texts, embeddings)
-    retriever = db.as_retriever()
-    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":250})
-    global qa 
-    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
-    return "Ready"
+def pdf_changes(pdf_doc, open_ai_key):
+    if openai_key is not None:
+        os.environ['OPENAI_API_KEY'] = open_ai_key
+        loader = OnlinePDFLoader(pdf_doc.name)
+        documents = loader.load()
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        texts = text_splitter.split_documents(documents)
+        embeddings = OpenAIEmbeddings()
+        db = Chroma.from_documents(texts, embeddings)
+        retriever = db.as_retriever()
+        global qa 
+        qa = ConversationalRetrievalChain.from_llm(
+            llm=OpenAI(temperature=0.5), 
+            retriever=retriever, 
+            return_source_documents=False)
+        return "Ready"
+    else:
+        return "You forgot OpenAI API key"
 
 def add_text(history, text):
     history = history + [(text, None)]
     return history, ""
 
 def bot(history):
-    response = infer(history[-1][0])
-    history[-1][1] = response['result']
-    return history
+    response = infer(history[-1][0], history)
+    history[-1][1] = ""
+    
+    for character in response:     
+        history[-1][1] += character
+        time.sleep(0.05)
+        yield history
+    
 
-def infer(question):
+def infer(question, history):
     
+    res = []
+    for human, ai in history[:-1]:
+        pair = (human, ai)
+        res.append(pair)
+    
+    chat_history = res
+    #print(chat_history)
     query = question
-    result = qa({"query": query})
-
-    return result
+    result = qa({"question": query, "chat_history": chat_history})
+    #print(result)
+    return result["answer"]
 
 css="""
 #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
@@ -60,27 +78,26 @@ title = """
 """
 
 
-with gr.Blocks(css=css,theme=gr.themes.Soft()) as demo:
+
+with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.HTML(title)
-        
         with gr.Column():
+            openai_key = gr.Textbox(label="You OpenAI API key", type="password")
             pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
-            repo_id = gr.Dropdown(label="LLM", choices=["google/flan-ul2", "OpenAssistant/oasst-sft-1-pythia-12b", "bigscience/bloomz"], value="google/flan-ul2")
             with gr.Row():
                 langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
-                load_pdf = gr.Button("Load to langchain")
+                load_pdf = gr.Button("Load pdf to langchain")
         
         chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
         question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
-        submit_btn = gr.Button("Send message")
-    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)
+        submit_btn = gr.Button("Send Message")
+    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
+    load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], 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
-    )
+        bot, chatbot, chatbot)
 
 demo.launch()