initial push

main.py

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+import streamlit as st 
+from sentence_transformers import SentenceTransformer
+import faiss
+import pandas as pd
+import numpy as np
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+df = pd.read_csv('course_data.csv',index_col=0)
+courses = df.to_dict('records')
+descriptions = [course['Content'] for course in courses]
+embeddings = model.encode(descriptions)
+index = faiss.IndexFlatL2(embeddings.shape[1])  # L2 distance index
+index.add(np.array(embeddings))
+# Function to simulate chatbot response (replace with your AI model logic)
+def generate_response(query,k=5):
+    # print(query)
+    # Placeholder response logic (you can replace this with your model/API call)
+    query_embedding = model.encode([query[-1]])  # Encode the user query
+    # Search in FAISS index for the closest matches
+    D, I = index.search(np.array(query_embedding), k=k)  # k is the number of top results
+    # Retrieve course titles based on the search results
+    results = []
+    desc = []
+    for idx in I[0]:
+        course_title = courses[idx]['Course_Name']  # Get the course title
+        desc.append(courses[idx]['Content'])
+        results.append(course_title)
+    # output=''
+    # for i,j in enumerate(list(set(results))):
+    #     output+=str(i+1)+j+'\n'
+    return list(set(results))
+
+
+# Define session state variables
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+if 'mess' not in st.session_state:
+    st.session_state.mess=[]
+
+
+if st.sidebar.button("RESET"):
+    st.session_state.messages=[]
+    st.session_state.mess=[]
+    
+# User input
+st.title('Analytics Vidhya Course Finder')
+user_input = st.chat_input('Write your message here...')
+
+if user_input:
+    # Append user input to messages
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    st.session_state.mess+=[user_input]
+    # Generate chatbot response
+    bot_response = generate_response(st.session_state.mess)
+    st.session_state.messages.append({"role": "bot", "content": bot_response})
+
+# Display chat messages in correct order
+for message in st.session_state.messages:
+    if message["role"] == "user":
+        with st.chat_message("human"):
+            st.write(message['content'])
+    else:
+        with st.chat_message("ai"):
+            for i in message['content']:
+                st.write('* '+i)

requirements.txt

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+faiss-cpu
+streamlit
+sentence_transformers