revert

app.py

@@ -8,12 +8,6 @@ from langchain.llms import OpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
-from langchain.document_loaders import WebBaseLoader
-from langchain.chains.summarize import load_summarize_chain
-from langchain.chains.llm import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 
 os.environ['OPENAI_API_KEY'] = os.getenv("Your_API_Key")
 
@@ -23,62 +17,24 @@ last_interaction_time = 0
 def loading_pdf():
     return "Working on the upload. Also, pondering the usefulness of sporks..."
 
-# Inside Chroma mod
-def summary(self):
-    num_documents = len(self.documents)
-    avg_doc_length = sum(len(doc) for doc in self.documents) / num_documents
-    return f"Number of documents: {num_documents}, Average document length: {avg_doc_length}"
-
-# PDF summary and query using stuffing
 def pdf_changes(pdf_doc):
-    try:
-        # Initialize loader and load documents
-        loader = OnlinePDFLoader(pdf_doc.name)
-        documents = loader.load()
-
-        # Define the prompt for summarization
-        prompt_template = """Write a concise summary of the following:
-        "{text}"
-        CONCISE SUMMARY:"""
-        prompt = PromptTemplate.from_template(prompt_template)
-
-        # Define the LLM chain with the specified prompt
-        llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")
-        llm_chain = LLMChain(llm=llm, prompt=prompt)
-
-        # Initialize StuffDocumentsChain
-        stuff_chain = StuffDocumentsChain(
-            llm_chain=llm_chain, document_variable_name="text"
-        )
-
-        # Generate summary using StuffDocumentsChain
-        global full_summary
-        full_summary = stuff_chain.run(documents)
-
-        # Other existing logic for Chroma, embeddings, and retrieval
-        embeddings = OpenAIEmbeddings()
-        global db
-        db = Chroma.from_documents(documents, embeddings)
-
-        retriever = db.as_retriever()
-        global qa
-        qa = ConversationalRetrievalChain.from_llm(
-            llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo-16k", max_tokens=-1, n=2),
-            retriever=retriever,
-            return_source_documents=False
-        )
-
-        return f"Ready. Full Summary loaded."
-
-    except Exception as e:
-        return f"Error processing PDF: {str(e)}"
-
-
+    loader = OnlinePDFLoader(pdf_doc.name)
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    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=0.2, model_name="gpt-3.5-turbo"),  
+        retriever=retriever, 
+        return_source_documents=False)
+    return "Ready"
 
 def clear_data():
-    global qa, db
+    global qa
     qa = None
-    db = None
     return "Data cleared"
 
 def add_text(history, text):
@@ -88,43 +44,31 @@ def add_text(history, text):
     return history, ""
 
 def bot(history):
-    global full_summary  
-    if 'summary' in history[-1][0].lower():  # Check if the last question asks for a summary
-        response = full_summary
-        return full_summary
-    else:
-        response = infer(history[-1][0], history)
-
-    sentences = '  \n'.join(response.split('. '))
-    formatted_response = f"**Bot:**\n\n{sentences}"
+    response = infer(history[-1][0], history)
+    formatted_response = "**Bot:**  \n" + '  \n'.join(response.split('. '))
     history[-1][1] = formatted_response
     return history
 
-
 def infer(question, history):
-    try:
-        res = []
-        for human, ai in history[:-1]:
-            pair = (human, ai)
-            res.append(pair)
+    res = []
+    for human, ai in history[:-1]:
+        pair = (human, ai)
+        res.append(pair)
     
-        chat_history = res
-        query = question
-        result = qa({"question": query, "chat_history": chat_history, "system": "This is a world-class summarizing AI, be helpful."})
-        return result["answer"]
-    except Exception as e:
-        return f"Error querying chatbot: {str(e)}"
+    chat_history = res
+    query = question
+    result = qa({"question": query, "chat_history": chat_history, "system:":"This is a world-class summarizing AI, be helpful."})
+    return result["answer"]
 
 def auto_clear_data():
-    global qa, da, last_interaction_time
-    if time.time() - last_interaction_time > 1000:
+    global qa, last_interaction_time
+    if time.time() - last_interaction_time > 600:
         qa = None
-        db = None
 
 def periodic_clear():
     while True:
         auto_clear_data()
-        time.sleep(1000)
+        time.sleep(60)
 
 threading.Thread(target=periodic_clear).start()
 
@@ -136,17 +80,10 @@ title = """
 <div style="text-align: center;max-width: 700px;">
     <h1>CauseWriter Chat with PDF • OpenAI</h1>
     <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
-    when everything is ready, you can start asking questions about the pdf. Limit ~11k words. <br />
-    This version is set to erase chat history automatically after page timeout and uses OpenAI.</p>
+    when everything is ready, you can start asking questions about the pdf. <br />
+    This version is set to store chat history and uses OpenAI as LLM.</p>
 </div>
 """
-# Global variable for tracking last interaction time
-last_interaction_time = 0
-full_summary = ""  # Added global full_summary
-
-def update_summary_box():
-    global full_summary
-    return {"summary_box": full_summary}
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
@@ -158,19 +95,13 @@ with gr.Blocks(css=css) as demo:
                 langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
                 load_pdf = gr.Button("Convert PDF to Magic AI language")
                 clear_btn = gr.Button("Clear Data")
-            
-            # New Textbox to display summary
-            summary_box = gr.Textbox(label="Document Summary", placeholder="Summary will appear here.", 
-                                     interactive=False, rows=5, elem_id="summary_box")
         
         chatbot = gr.Chatbot([], elem_id="chatbot").style(height=450)
         question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter")
         submit_btn = gr.Button("Send Message")
 
     load_pdf.click(loading_pdf, None, langchain_status, queue=False)
-    load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False).then(
-        update_summary_box, state={"summary_box": summary_box}
-    )  # Then update the summary_box
+    load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False)
     clear_btn.click(clear_data, outputs=[langchain_status], queue=False)
     question.submit(add_text, [chatbot, question], [chatbot, question]).then(
         bot, chatbot, chatbot
@@ -179,4 +110,4 @@ with gr.Blocks(css=css) as demo:
         bot, chatbot, chatbot
     )
 
-demo.launch()
+demo.launch()