Update app.py

app.py

@@ -7,63 +7,87 @@ from llama_parse import LlamaParse
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 
-# Define the LLM model and document processing
+# LLM ve Parser Başlatma
 llm = HuggingFaceInferenceAPI(model_name="tiiuae/falcon-7b-instruct")
 parser = LlamaParse(api_key='llx-zKtsC5UBLs8DOApOsLluXMBdQhC75ea0Vs80SmPSjsmDzuhh', result_type='markdown')
+
+# PDF dosyasını yükleyip indexleme işlemi
 file_extractor = {'.pdf': parser}
 documents = SimpleDirectoryReader('data/', file_extractor=file_extractor).load_data()
 
+# Embedding Modeli ve Query Engine Başlatma
 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)
 
-# Function to process query with retries
+# System Prompt: LLM'nin görevini belirlemek için
+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. 
+For any question that is not related to the content of the document, kindly ask the user to refer to the Hund Ecosystem.
+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
 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)
+            response = query_engine.query(query, system_message=system_prompt)  # System prompt ekleniyor
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
+            print(f"Query completed in {duration:.2f} seconds.\n {response}")
             return response
         except httpx.ReadTimeout:
             if attempt < max_retries - 1:
+                print(f"Timeout occurred. Retrying in {wait_time} seconds...")
                 time.sleep(wait_time)
             else:
                 raise
         except Exception as e:
-            return f"An error occurred: {e}"
+            print(f"An error occurred: {e}")
+            break
+
+# Arayüz fonksiyonları
+def chatbot_interface(input_text, history):
+    if input_text:
+        response = query_with_retry(input_text)
+        history.append(("Hundler", input_text))
+        history.append(("HundAI Chatbot", response))
+    return history, ""  # Chat geçmişini döndür
+
+def clear_all():
+    return [], ""  # Sohbet geçmişini ve metni temizler
 
-# Chatbot interface function
-def chatbot_interface(user_input, history=None):
-    if history is None:
-        history = []  # Initialize history if it is None
-    
-    if not user_input:
-        return history, history
-    
-    bot_response = query_with_retry(user_input)
-    
-    # Append messages to chat history
-    history.append(("User", user_input))
-    history.append(("Bot", bot_response.strip()))
-    
-    return history, history
+# Gradio Arayüzü
+with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css="""
+footer {visibility: hidden;}
+#input-box {background-color: #2e2e2e; color: #e5e5e5;}
+#submit-btn {background-color: #607D8B; color: #fff; border-radius: 8px; padding: 10px 20px; font-weight: bold;}
+#retry-btn {background-color: #8e8e8e; color: #fff; border-radius: 8px; padding: 10px 20px; font-weight: bold;}
+#clear-btn {background-color: #f44336; color: #fff; border-radius: 8px; padding: 10px 20px; font-weight: bold;}
+#undo-btn {background-color: #2196F3; color: #fff; border-radius: 8px; padding: 10px 20px; font-weight: bold;}
+#output-box {background-color: #2e2e2e; color: #fff;}
+#submit-btn:hover, #retry-btn:hover, #clear-btn:hover, #undo-btn:hover {cursor: pointer; opacity: 0.9;}
+#buttons {display: flex; justify-content: space-between; font-size: 0.85em;}
+""") as demo:
+    # Chatbot UI
+    with gr.Column():
+        chatbox = gr.Chatbot(label="Chatbot", elem_id="chatbox")
+        input_text = gr.Textbox(label="Ask a question", placeholder="Type your question here...", lines=2, elem_id="input-box")
+        
+        # Butonlar: Retry, Clear, Undo butonları küçük şekilde düzenlendi
+        with gr.Row(elem_id="buttons"):
+            retry_btn = gr.Button("Retry", elem_id="retry-btn", scale=0.8)
+            clear_btn = gr.Button("Clear", elem_id="clear-btn", scale=0.8)
+            undo_btn = gr.Button("Undo", elem_id="undo-btn", scale=0.8)
+        
+        submit_btn = gr.Button("Ask", elem_id="submit-btn")
 
-# Define the Gradio interface
-iface = gr.Interface(
-    fn=chatbot_interface,
-    inputs=[
-        gr.Textbox(label="Ask a question", placeholder="Enter your query here...", lines=2),
-        gr.State()  # Store chat history
-    ],
-    outputs=[
-        gr.Chatbot(label="Chat"),
-        gr.State()  # Output the updated chat history
-    ],
-    title="Document-based Question Answering Chatbot",
-    description="Ask questions based on the documents you uploaded. This model answers queries using your uploaded PDFs.",
-    live=False  # live=True can cause issues with chat interfaces
-)
+    # Button actions
+    submit_btn.click(chatbot_interface, inputs=[input_text, chatbox], outputs=[chatbox, input_text])
+    retry_btn.click(chatbot_interface, inputs=[input_text, chatbox], outputs=[chatbox, input_text])  # Retry işlemi
+    clear_btn.click(clear_all, outputs=[chatbox, input_text])
+    undo_btn.click(clear_all, outputs=[chatbox, input_text])  # Undo işlevi Clear olarak kullanıldı
 
-iface.launch()
+demo.launch()