Update app.py

app.py

@@ -34,6 +34,14 @@ def store_feedback_in_sheet(feedback, question, rag_response, graphrag_response)
     row = [timestamp, question, rag_response, graphrag_response, feedback]
     sheet.append_row(row)
 
+def load_data():
+    data = sheet.get_all_records()
+    return data[-10:], len(data)
+
+def add_review(question, rag_response, graphrag_response, feedback):
+    store_feedback_in_sheet(feedback, question, rag_response, graphrag_response)
+    return load_data()
+
 # RAG Setup
 text_path = r"./text_chunks.txt"
 loader = TextLoader(text_path, encoding='utf-8')
@@ -41,16 +49,6 @@ documents = loader.load()
 text_splitter = CharacterTextSplitter(chunk_size=3000, chunk_overlap=4)
 docs = text_splitter.split_documents(documents)
 
-class CustomTextLoader(TextLoader):
-    def __init__(self, file_path: str, encoding: str = 'utf-8'):
-        super().__init__(file_path)
-        self.encoding = encoding
-
-    def load(self):
-        with open(self.file_path, encoding=self.encoding) as f:
-            text = f.read()
-        return [Document(page_content=text)]
-
 embeddings = HuggingFaceEmbeddings()
 
 from langchain.llms import HuggingFaceHub
@@ -107,17 +105,6 @@ rag_chain = (
     | StrOutputParser()
 )
 
-class ChatBot():
-    loader = CustomTextLoader(r"./text_chunks.txt", encoding='utf-8')
-    documents = loader.load()
-
-    rag_chain = (
-        {"context": docsearch.as_retriever(),  "question": RunnablePassthrough()} 
-        | prompt 
-        | llm
-        | StrOutputParser() 
-    )
-
 graphrag_llm = ChatGroq(
     model="Llama3-8b-8192",
     temperature=0,
@@ -182,43 +169,29 @@ def compare_models(question):
     graphrag_response = get_graphrag_response(question)
     return rag_response, graphrag_response
 
-def handle_feedback(feedback, question, rag_response, graphrag_response):
-    try:
-        store_feedback_in_sheet(feedback, question, rag_response, graphrag_response)
-        return "ส่งสำเร็จ!"
-    except Exception as e:
-        return f"Error: {e}"
-
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Thai Rice Assistant A/B Testing")
-
     with gr.Row():
         with gr.Column():
-            question_input = gr.Textbox(label="ถามคำถามเกี่ยวกับข้าว:")
-            submit_btn = gr.Button("ถาม")
-
-        with gr.Column():
+            question_input = gr.Textbox(label="ถามคำถามเกี่ยวกับข้าว:", placeholder="Enter your question about Thai rice")
+            submit_btn = gr.Button(value="ถาม")
             rag_output = gr.Textbox(label="Model A", interactive=False)
             graphrag_output = gr.Textbox(label="Model B", interactive=False)
+            feedback = gr.Radio(label="Which response is better?", choices=["A ดีกว่า", "B ดีกว่า", "เท่ากัน", "แย่ทั้งคู่"])
+            comments = gr.Textbox(label="Comments", lines=10, placeholder="Any additional comments?")
+            submit_feedback = gr.Button(value="Submit Feedback")
 
-    with gr.Row():
         with gr.Column():
-            choice = gr.Radio(["A ดีกว่า", "B ดีกว่า", "เท่ากัน", "แย่ทั้งคู่"], label="คำตอบไหนดีกว่ากัน?")
-            send_feedback_btn = gr.Button("ส่ง")
-
-    feedback_output = gr.Textbox(label="Feedback Status", interactive=False)
+            data = gr.Dataframe(label="Most recently created 10 rows")
+            count = gr.Number(label="Total number of reviews")
 
-    def on_submit(question):
-        rag_response, graphrag_response = compare_models(question)
-        return rag_response, graphrag_response
+    # Function to handle question submission and display responses
+    submit_btn.click(fn=compare_models, inputs=[question_input], outputs=[rag_output, graphrag_output])
 
-    def on_feedback(feedback):
-        question = question_input.value
-        rag_response = rag_output.value
-        graphrag_response = graphrag_output.value
-        return handle_feedback(feedback, question, rag_response, graphrag_response)
+    # Function to handle feedback submission and update data display
+    submit_feedback.click(fn=add_review, inputs=[question_input, rag_output, graphrag_output, feedback], outputs=[data, count])
 
-    submit_btn.click(on_submit, inputs=[question_input], outputs=[rag_output, graphrag_output])
-    send_feedback_btn.click(on_feedback, inputs=[choice], outputs=[feedback_output])
+    # Load initial data display
+    demo.load(fn=load_data, inputs=None, outputs=[data, count])
 
 demo.launch(share=True)