Update app.py

app.py

@@ -3,39 +3,43 @@ import sympy as sp
 import chromadb
 from transformers import pipeline
 from langchain.chains import RetrievalQA
-from langchain_community.vectorstores import FAISS  # ✅ FIXED IMPORT
+from langchain_community.vectorstores import FAISS
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.llms import OpenAI
 import os
+
 # Initialize ChromaDB for Retrieval-Augmented Generation (RAG)
 chroma_client = chromadb.PersistentClient(path="./chroma_db")
 embedding_model = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-# Load the RAG-based Retrieval System
-
-os.listdir("faiss_index")
+# Check if FAISS index exists
+faiss_path = "faiss_index"
 
-vectorstore = FAISS.load_local("faiss_index", embedding_model)
-retriever = vectorstore.as_retriever()
-qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=retriever)
+if not os.path.exists(faiss_path):
+    st.error("⚠️ FAISS index not found. Please upload or generate the FAISS index first.")
+else:
+    # Load the RAG-based Retrieval System
+    vectorstore = FAISS.load_local(faiss_path, embedding_model, allow_dangerous_deserialization=True)
+    retriever = vectorstore.as_retriever()
+    qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=retriever)
 
-# Load NLP Model
-model = pipeline("text2text-generation", model="google/flan-t5-small")
+    # Load NLP Model
+    model = pipeline("text2text-generation", model="google/flan-t5-small")
 
-st.title("🤖 AI-Driven Mathematical Model Generator")
-st.write("Enter a problem statement in natural language to get a mathematical model.")
+    st.title("🤖 AI-Driven Mathematical Model Generator")
+    st.write("Enter a problem statement in natural language to get a mathematical model.")
 
-user_input = st.text_area("✍️ Enter your problem:")
+    user_input = st.text_area("✍️ Enter your problem:")
 
-if st.button("🚀 Generate Model"):
-    retrieved_context = qa_chain.run(user_input)  # RAG retrieval
-    response = model(f"Generate a mathematical model for: {user_input}\nContext: {retrieved_context}", max_length=200)
-    
-    try:
-        equation = sp.sympify(response[0]['generated_text'])
-    except:
-        equation = response[0]['generated_text']  # If parsing fails, return text
-    
-    st.subheader("📌 Mathematical Model:")
-    st.latex(sp.latex(equation))
-    st.code(str(equation), language='python')
+    if st.button("🚀 Generate Model"):
+        retrieved_context = qa_chain.run(user_input)  # RAG retrieval
+        response = model(f"Generate a mathematical model for: {user_input}\nContext: {retrieved_context}", max_length=200)
+        
+        try:
+            equation = sp.sympify(response[0]['generated_text'])
+        except:
+            equation = response[0]['generated_text']  # If parsing fails, return text
+        
+        st.subheader("📌 Mathematical Model:")
+        st.latex(sp.latex(equation))
+        st.code(str(equation), language='python')