Update app.py

app.py

@@ -19,11 +19,10 @@ class AdvancedPdfChatbot:
         os.environ["OPENAI_API_KEY"] = openai_api_key
         self.embeddings = OpenAIEmbeddings()
         self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        self.llm =  ChatOpenAI(temperature=0.5,model_name='gpt-4o',max_tokens=3000)
+        self.llm =  ChatOpenAI(temperature=0.5,model_name='gpt-4o',max_tokens=3000')
         
         self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
         self.qa_chain = None
-        self.pdf_path = None
         self.template = """
 You are a file-based knowledge assistant that interacts with users like ChatGPT. Your primary source of knowledge comes from user-uploaded files, such as PDFs. You do not rely on general knowledge or the internet. Instead, you extract, analyze, and synthesize information directly from the content of the provided file(s).  
 **1. Personality and Tone**  
@@ -71,7 +70,7 @@ NOTE : DESCRIBE/SUMMARY should always return the overall summary of the document
         Question: {question}
         Answer:
         """
-        
+
         self.prompt = PromptTemplate(template=self.template, input_variables=["context", "question"])
 
     def load_and_process_pdf(self, pdf_path):
@@ -79,7 +78,6 @@ NOTE : DESCRIBE/SUMMARY should always return the overall summary of the document
         documents = loader.load()
         texts = self.text_splitter.split_documents(documents)
         self.db = FAISS.from_documents(texts, self.embeddings)
-        self.pdf_path = pdf_path
         self.setup_conversation_chain()
 
     def setup_conversation_chain(self):
@@ -96,13 +94,6 @@ NOTE : DESCRIBE/SUMMARY should always return the overall summary of the document
         result = self.qa_chain({"question": query})
         return result['answer']
 
-    def get_pdf_path(self):
-        # Return the stored PDF path
-        if self.pdf_path:
-            return self.pdf_path
-        else:
-            return "No PDF uploaded yet."
-
 # Initialize the chatbot
 pdf_chatbot = AdvancedPdfChatbot(openai_api_key)
 
@@ -111,7 +102,7 @@ def upload_pdf(pdf_file):
         return "Please upload a PDF file."
     file_path = pdf_file.name
     pdf_chatbot.load_and_process_pdf(file_path)
-    return file_path
+    return "PDF uploaded and processed successfully. You can now start chatting!"
 
 def respond(message, history):
     bot_message = pdf_chatbot.chat(message)
@@ -122,10 +113,6 @@ def clear_chatbot():
     pdf_chatbot.memory.clear()
     return []
 
-def get_pdf_path():
-    # Call the method to return the current PDF path
-    return pdf_chatbot.get_pdf_path()
-
 # Create the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# PDF Chatbot")
@@ -136,15 +123,18 @@ with gr.Blocks() as demo:
 
     upload_status = gr.Textbox(label="Upload Status")
     upload_button.click(upload_pdf, inputs=[pdf_upload], outputs=[upload_status])
-    path_button = gr.Button("Get PDF Path")
-    pdf_path_display = gr.Textbox(label="Current PDF Path")
+
     chatbot_interface = gr.Chatbot()
     msg = gr.Textbox()
     clear = gr.Button("Clear")
 
     msg.submit(respond, inputs=[msg, chatbot_interface], outputs=[msg, chatbot_interface])
     clear.click(clear_chatbot, outputs=[chatbot_interface])
-    path_button.click(get_pdf_path, outputs=[pdf_path_display])
 
 if __name__ == "__main__":
     demo.launch()
+
+
+
+
+