Create app.py

app.py

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+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from qdrant_client import models, QdrantClientS
+import pandas as pd
+from datasets import load_dataset
+
+#************************************************************* LOAD DATA
+data = load_dataset("ManuelAlv/academic_conuseling")
+
+# Main dataset
+bert_dataset = data['dataset'].to_pandas()
+
+# Dataset used to test the chatbot
+test_dataset = data['test'].to_pandas()
+test_dataset.columns = ["test_question", "original_question"]
+
+#************************************************************* Create Functions
+# function to add values
+def add_value(collection, key, value, id):
+  encoder = SentenceTransformer(collection)
+
+  qdrant.upsert(
+      collection_name = collection,
+      wait=True,
+      points = [
+          models.PointStruct(
+              id = id,
+              vector = encoder.encode(key).tolist(),
+              payload = {
+                  'text': value,
+                  'question': key
+              }
+          )
+      ]
+  )
+
+# Function to search for a value
+def search(collection, query):
+  search = qdrant.search(
+      collection_name = collection,
+      query_vector = encoder.encode(query).tolist(),
+      limit = 1
+  )
+  return search
+
+#************************************************************* Create VD
+# Create a local Vector Database
+qdrant = QdrantClient(":memory:")
+
+# Load the model
+model = "all-MiniLM-L6-v2"
+encoder = SentenceTransformer(model)
+
+# Create a collection for the model with its embeddings
+qdrant.recreate_collection(
+    collection_name = model,
+    vectors_config = models.VectorParams(
+        size = encoder.get_sentence_embedding_dimension(),
+        distance = models.Distance.COSINE
+    )
+)
+
+# Add the data to model
+for index, row in bert_dataset.iterrows():
+  key = row['question']
+  value = row['answer']
+  id = index + 1
+
+  add_value(model, key, value, id)
+
+# ************************************************************* QUERY
+# Enter a question
+question = "I'm feeling sad and lonely"
+
+result = search(model, question)
+result = result[0].payload['text']
+
+
+def get_response(input):
+    result = search(model, input)
+    result = result[0].payload['text']
+    return result
+
+st.set_page_config(page_title="RAG", page_icon="🧊", layout="wide")
+st.title("UniSA Academic Support")
+
+# with st.sidebar:
+#     st.header("Settings")
+#     st.text_input("Enter a website URL")
+
+if 'conversation_ended' not in st.session_state:
+    st.session_state['conversation_ended'] = False
+
+if not st.session_state['conversation_ended']:
+  with st.chat_message("AI"):
+    st.write("Hi! I'm BrainHug AI, your supportive AI friend.")
+    st.write("Feel free to chat with me at any time, just enter your question. If I can't answer, try rephrasing it again.")
+    st.write("If you want to finish the conversation, just say BYE")
+
+  user_q = st.chat_input("Start typing here")
+
+  if user_q:
+    if user_q.upper() == "BYE":
+      st.session_state['conversation_ended'] = True
+      with st.chat_message("AI"):
+        st.write("Goodbye! Feel free to come back anytime.")
+      st.stop()
+
+    elif user_q is not None or user_q is "":
+      response = get_response(user_q)
+      with st.chat_message("Human"):
+          st.write(user_q)
+      with st.chat_message("AI"):
+          st.write(response)
+else:
+    st.write("The conversation has ended. Refresh the page to start over.")