add examples

app.py

@@ -90,7 +90,7 @@ dataset = Dataset.from_dict(data, features=features)
 
 dataset.add_faiss_index(column='embeddings')
 
-def retrieve(query, top_k=3):
+def retrieve(query, top_k=1):
     query_embedding = ST.encode([query])
     scores, retrieved_examples = dataset.get_nearest_examples('embeddings', query_embedding, k=top_k)
 
@@ -100,69 +100,6 @@ def retrieve(query, top_k=3):
 
 client = InferenceClient(BASE_MODEL)
 
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     max_tokens=512,
-#     temperature=0.5,
-#     top_p=0.9,
-# ):
-#     # Retrieve top 3 relevant documents based on the user's query
-#     score, retrieved_docs = retrieve(message, top_k=TOP_K)
-
-#     # print(f"Score: {score}")
-#     if score <= 11:
-#         # Prepare the retrieved context
-#         context = "\n".join([f"{doc}" for i, doc in enumerate(retrieved_docs)])
-#     else:
-#         context = ""
-    
-#     print(f"Feed:\n{context}")
-
-#     messages = [{"role": "system", "content": SYS_MSG}]
-
-#     for val in history:
-#         if val[0]:
-#             messages.append({"role": "user", "content": val[0]})
-#         if val[1]:
-#             messages.append({"role": "assistant", "content": val[1]})
-
-#     # messages.append({"role": "user", "content": message})
-    
-#     # Append the current user message along with the retrieved context
-#     user_context = f"{message}\nKonteks:\n{context}"
-#     messages.append({"role": "user", "content": user_context})
-
-#     response = ""
-
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# demo = gr.ChatInterface(
-#     respond,
-#     textbox=gr.Textbox(placeholder="Enter message here", container=False, scale = 7),
-#     chatbot=gr.Chatbot(value=[[None, "Halo namaku Bestie, apa yang perlu kamu ceritakan hari ini?"]]),
-#     examples=[
-#         ["Hello there! How are you doing?"],
-#         ["Can you explain briefly to me what is the Python programming language?"],
-#         ["Explain the plot of Cinderella in a sentence."],
-#         ["How many hours does it take a man to eat a Helicopter?"],
-#         ["Write a 100-word article on 'Benefits of Open-Source in AI research'"]
-#     ],
-# )
-
 examples = [
     ["Saya mengalami pelecehan seksual."],
     ["Bantu saya melaporkan pelecehan seksual."],
@@ -170,7 +107,6 @@ examples = [
     ["Bagaiamana melaporkan pelecahan seksual yang saya lihat?"],
 ]
 
-# Fungsi respond yang mempertahankan penggunaan RAG dan score boundary
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -181,14 +117,15 @@ def respond(
     # Retrieve top 3 relevant documents based on the user's query
     score, retrieved_docs = retrieve(message, top_k=TOP_K)
 
-    # Check the score boundary for relevance
-    if score[0] <= 11:
+    # print(f"Score: {score}")
+    if score <= 11:
         # Prepare the retrieved context
         context = "\n".join([f"{doc}" for i, doc in enumerate(retrieved_docs)])
     else:
         context = ""
+    
+    print(f"Feed:\n{context}")
 
-    # Construct the conversation history with context
     messages = [{"role": "system", "content": SYS_MSG}]
 
     for val in history:
@@ -197,12 +134,14 @@ def respond(
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
+    # messages.append({"role": "user", "content": message})
+    
     # Append the current user message along with the retrieved context
     user_context = f"{message}\nKonteks:\n{context}"
     messages.append({"role": "user", "content": user_context})
 
-    # Generate the response from the language model
     response = ""
+
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -211,36 +150,19 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-        response += token
-
-    # Add the response to the history
-    history.append((message, response))
-    return history, history
-
-# Fungsi untuk mengisi contoh ke dalam kotak teks dan menjalankan respond
-def example_click(example):
-    return example[0]
-
-# Membuat interface Gradio dengan examples yang dapat diklik
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(value=[[None, "Halo namaku Bestie, apa yang perlu kamu ceritakan hari ini?"]])
 
-    with gr.Row():
-        textbox = gr.Textbox(placeholder="Enter message here", container=False, scale=7)
-        submit_btn = gr.Button("Send")
-
-    # Menampilkan contoh di dalam chatbot
-    examples_box = gr.Examples(
-        examples=examples,
-        inputs=textbox,
-        label="Contoh yang bisa dipilih",
-    )
-
-    # Event tombol submit untuk mengirim pesan
-    submit_btn.click(respond, inputs=[textbox, chatbot], outputs=[chatbot, chatbot])
+        response += token
+        yield response
 
-    # Event klik pada examples untuk mengisi textbox dengan contoh yang diklik
-    examples_box.select(fn=example_click, inputs=examples_box, outputs=textbox)
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    textbox=gr.Textbox(placeholder="Enter message here", container=False, scale = 7),
+    chatbot=gr.Chatbot(value=[[None, "Halo namaku Bestie, apa yang perlu kamu ceritakan hari ini?"]]),
+    examples=examples,
+)
 
 if __name__ == "__main__":
     demo.launch(share=True)