Upload 3 files

app.py

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+import streamlit as st
+import uuid
+from typing import List, Dict
+import os
+from dataset_loader import load_dataset
+
+if not os.path.isdir("r_logic_data"):
+    load_dataset()
+
+from r_logic_data.agent import Agent
+
+
+class StreamlitChatbot:
+    def __init__(self):
+        self.agent = Agent()
+
+    def initialize_session_state(self):
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+        if "recipient_id" not in st.session_state:
+            st.session_state.recipient_id = str(uuid.uuid4())
+
+    def display_chat_history(self):
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+    def get_chatbot_response(self, user_input: str) -> str:
+        item = {
+            "content": user_input,
+            "recipient_id": st.session_state.recipient_id,
+            "sender_id": "chatbot",
+            "history_id": f"history_{len(st.session_state.messages)}",
+            "qa_id": f"qa_{len(st.session_state.messages)}",
+        }
+
+        response = self.agent.answer(
+            question=user_input,
+            recipient_id=st.session_state.recipient_id,
+            sender_id="chatbot",
+            item=item,
+            mode="chat",
+            platform=1,  # Assuming 1 is for Streamlit
+            history=st.session_state.messages,
+        )
+
+        return response[0]  # Returning just the answer text
+
+    def run(self):
+        st.title("R-Logic Computer Repair Chatbot")
+        self.initialize_session_state()
+
+        self.display_chat_history()
+
+        if user_input := st.chat_input(
+            "Ask about your repair status or any questions..."
+        ):
+            st.session_state.messages.append({"role": "user", "content": user_input})
+            with st.chat_message("user"):
+                st.markdown(user_input)
+
+            response = self.get_chatbot_response(user_input)
+            st.session_state.messages.append({"role": "assistant", "content": response})
+            with st.chat_message("assistant"):
+                st.markdown(response)
+
+
+if __name__ == "__main__":
+    chatbot = StreamlitChatbot()
+    chatbot.run()

dataset_loader.py

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+from huggingface_hub import Repository
+from huggingface_hub import login
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def load_dataset():
+    login(token=os.getenv("HUB_TOKEN"))
+
+    repo = Repository(
+        local_dir="r_logic_data",
+        repo_type="dataset",
+        clone_from="dahreply/r_logic_dataset",
+        git_user=os.getenv("GIT_USER"),
+        git_email=os.getenv("GIT_EMAIL"),
+        token=True,
+    )
+    repo.git_pull()

requirements.txt

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+
+huggingface_hub
+pandas
+ipykernel
+pandasql
+requests
+python-dotenv
+icecream
+boto3
+pymysql
+streamlit