Update app.py

app.py

@@ -14,8 +14,7 @@ from neo4j import GraphDatabase
 import networkx as nx
 import pinecone
 import os
-
-os.system("pip install sentence-transformers")
+from datetime import datetime
 
 # RAG Setup
 text_path = r"./text_chunks.txt"
@@ -34,8 +33,6 @@ class CustomTextLoader(TextLoader):
             text = f.read()
         return [Document(page_content=text)]
 
-from langchain.embeddings import HuggingFaceEmbeddings
-
 embeddings = HuggingFaceEmbeddings()
 
 from langchain.llms import HuggingFaceHub
@@ -69,21 +66,15 @@ rag_llm = ChatGroq(
     groq_api_key='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'
 )
 
-from langchain import PromptTemplate
-
-from langchain import PromptTemplate
-
 template = """
 You are a Thai rice assistants. These Human will ask you a questions about Thai Rice.
-Answer the question onlt in Thai langauges.
+Answer the question only in Thai languages.
 Use following piece of context to answer the question. 
 If you don't know the answer, just say you don't know. 
 Keep the answer within 2 sentences and concise.
-
 Context: {context}
 Question: {question}
 Answer: 
-
 """
 
 prompt = PromptTemplate(
@@ -102,8 +93,6 @@ class ChatBot():
   loader = CustomTextLoader(r'./text_chunks.txt', encoding='utf-8')
   documents = loader.load()
 
-  # The rest of the code here
-
   rag_chain = (
     {"context": docsearch.as_retriever(),  "question": RunnablePassthrough()} 
     | prompt 
@@ -175,23 +164,31 @@ def compare_models(question):
     graphrag_response = get_graphrag_response(question)
     return rag_response, graphrag_response
 
-def store_feedback(feedback, question, rag_response, graphrag_response):
-    print("Storing feedback...")
-    print(f"Question: {question}")
-    print(f"RAG Response: {rag_response}")
-    print(f"GraphRAG Response: {graphrag_response}")
-    print(f"User Feedback: {feedback}")
-    
-    with open("feedback.txt", "a", encoding='utf-8') as f:
-        f.write(f"Question: {question}\n")
-        f.write(f"RAG Response: {rag_response}\n")
-        f.write(f"GraphRAG Response: {graphrag_response}\n")
-        f.write(f"User Feedback: {feedback}\n\n")
-
 def handle_feedback(feedback, question, rag_response, graphrag_response):
     store_feedback(feedback, question, rag_response, graphrag_response)
     return "Feedback stored successfully!"
 
+def store_feedback(feedback, question, rag_response, graphrag_response):
+    # Ensure the feedback directory exists
+    os.makedirs('feedback', exist_ok=True)
+    
+    # Generate a unique filename based on the current timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f'feedback/feedback_{timestamp}.txt'
+    
+    # Prepare the content to be written
+    content = f"Timestamp: {datetime.now()}\n"
+    content += f"Question: {question}\n"
+    content += f"RAG Response: {rag_response}\n"
+    content += f"GraphRAG Response: {graphrag_response}\n"
+    content += f"User Feedback: {feedback}\n\n"
+
+    # Write the feedback to a new file
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.write(content)
+
+    print(f"Feedback stored in {filename}")
+
 with gr.Blocks() as demo:
     gr.Markdown("## Thai Rice Assistant A/B Testing")
 
@@ -209,6 +206,8 @@ with gr.Blocks() as demo:
             choice = gr.Radio(["A is better", "B is better", "Tie", "Both Bad"], label="Which response is better?")
             send_feedback_btn = gr.Button("Send Feedback")
 
+    feedback_output = gr.Textbox(label="Feedback Status", interactive=False)
+
     def on_submit(question):
         rag_response, graphrag_response = compare_models(question)
         return rag_response, graphrag_response
@@ -220,6 +219,6 @@ with gr.Blocks() as demo:
         return handle_feedback(feedback, question, rag_response, graphrag_response)
 
     submit_btn.click(on_submit, inputs=[question_input], outputs=[rag_output, graphrag_output])
-    send_feedback_btn.click(on_feedback, inputs=[choice], outputs=[])
+    send_feedback_btn.click(on_feedback, inputs=[choice], outputs=[feedback_output])
 
-demo.launch(share=True)
+demo.launch(share=True)