Update app.py

app.py

@@ -7,20 +7,20 @@ from llama_parse import LlamaParse
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 
-# LLM ve Parser Başlatma
+# LLM and Parser Initialization
 llm = HuggingFaceInferenceAPI(model_name="tiiuae/falcon-7b-instruct")
 parser = LlamaParse(api_key='llx-zKtsC5UBLs8DOApOsLluXMBdQhC75ea0Vs80SmPSjsmDzuhh', result_type='markdown')
 
-# PDF dosyasını yükleyip indexleme işlemi
+# PDF document extraction and indexing
 file_extractor = {'.pdf': parser}
 documents = SimpleDirectoryReader('data/', file_extractor=file_extractor).load_data()
 
-# Embedding Modeli ve Query Engine Başlatma
+# Embedding Model and Query Engine Initialization
 embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
 vector_index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
 query_engine = vector_index.as_query_engine(llm=llm)
 
-# System Prompt: LLM'nin görevini belirlemek için
+# System Prompt for LLM
 system_prompt = """
 You are an AI assistant designed to answer questions about the Hund Ecosystem based on the uploaded PDF document. 
 Your primary responsibility is to provide detailed, accurate, and clear answers to user queries related to the content of the document. 
@@ -28,12 +28,12 @@ For any question that is not related to the content of the document, kindly ask
 Please ensure to be polite and professional in your responses. If the question cannot be answered based on the document, kindly guide the user accordingly.
 """
 
-# Sorgu işlemi için retry mekanizması
+# Query Retry Logic
 def query_with_retry(query, max_retries=3, wait_time=5):
     for attempt in range(max_retries):
         try:
             start_time = datetime.now()
-            response = query_engine.query(query)  # System prompt ekleniyor
+            response = query_engine.query(query)  # System prompt is added here
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
             print(f"Query completed in {duration:.2f} seconds.\n {response}")
@@ -48,37 +48,42 @@ def query_with_retry(query, max_retries=3, wait_time=5):
             print(f"An error occurred: {e}")
             break
 
-# Kullanıcı mesajlarını ve botun cevabını yönetmek
+# Manage user messages and bot responses
 def respond(message, history):
     try:
-        # Sorguyu query_engine üzerinde çalıştır
+        # Run the query engine with the user message
         bot_message = query_engine.query(message)
+
+        print(f"\n{datetime.now()}:{llm.model_name}:: {message} --> {str(bot_message)}\n")
         
-        # Geçmişi güncelle ve doğru formatta döndür
+        # Add user's message and bot's response to history
         history.append((message, str(bot_message)))
-        return history, history  # Chatbot için doğru formatta döndürülüyor
+        
+        # Clear the input field after sending the message
+        return history, ""  # Clear the input field for the next question
+
     except Exception as e:
         if str(e) == "'NoneType' object has no attribute 'as_query_engine'":
             return "Please upload a file.", history
         return f"An error occurred: {e}", history
 
 # UI Setup
-with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")])) as demo:
+with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
     gr.Markdown("# HundAI Chatbot🤖")
 
     with gr.Row():
         with gr.Column(scale=3):
-            chatbot = gr.Chatbot(height=500)
-            user_message = gr.Textbox(placeholder="Ask me questions anything about the Hund Ecosystem!", container=False)
-            submit_btn = gr.Button("Ask")
+            chatbot = gr.Chatbot(height=500)  # Display chat history here
+            user_message = gr.Textbox(placeholder="Ask me questions about the Hund Ecosystem!", container=False)
+            submit_btn = gr.Button("Send")
             clear_btn = gr.Button("Clear Chat")
             
-            # `respond` fonksiyonu, kullanıcı sorularını cevaplamak için kullanılacak
+            # When submit is clicked, process the message and show response
             submit_btn.click(fn=respond, inputs=[user_message, chatbot], outputs=[chatbot, user_message])
 
-            # `clear` butonu, sohbeti temizleyecek
+            # Clear the chat history when the clear button is clicked
             clear_btn.click(lambda: [None, []], outputs=[chatbot, chatbot])
 
 # Launch the demo
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()