Update app.py

app.py

@@ -1,22 +1,63 @@
 import streamlit as st
 from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+from sentence_transformers import SentenceTransformer
+import faiss
+import torch
 
-# Load RAG components
+# Title of the Streamlit app
+st.title("Mental Health Chatbot")
+
+# Load a pre-trained sentence transformer model for embedding
+st.write("Loading models... Please wait.")
+sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Load the RAG model, tokenizer, and retriever
 tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
 retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", use_dummy_dataset=True)
 rag_model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
 
-# Streamlit UI
-st.title("RAG-based Q&A")
+# Sample dialogues related to mental health (replace with actual dataset for production)
+sample_dialogues = [
+    "I'm feeling really down lately and don't know what to do.",
+    "I just lost my job, and I'm worried about the future.",
+    "I'm having trouble sleeping and feeling anxious all the time.",
+    "I've been feeling isolated and lonely.",
+    "I don't have the energy to do anything, and it's affecting my work."
+]
+
+# Embed the sample dialogues using the sentence transformer model
+embeddings = sentence_model.encode(sample_dialogues, convert_to_tensor=True)
+
+# Build FAISS index
+index = faiss.IndexFlatL2(embeddings.shape[1])
+index.add(embeddings.cpu().numpy())
+
+# User input
+user_input = st.text_input("How are you feeling today?")
+
+# Define response generation function
+def generate_response(query):
+    # Embed the query using the sentence transformer
+    query_embedding = sentence_model.encode(query, convert_to_tensor=True).cpu().numpy()
+
+    # Search for the closest dialogue in the index
+    D, I = index.search(query_embedding, k=1)
+    
+    # Retrieve the closest dialogue
+    closest_dialogue = sample_dialogues[I[0][0]]
 
-query = st.text_input("Enter your question:")
+    # Generate response using RAG model
+    inputs = tokenizer(closest_dialogue, return_tensors="pt")
+    outputs = rag_model.generate(**inputs)
+    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    
+    return response[0]
 
-if st.button("Generate Answer"):
-    if query:
-        # Process the input query and generate a response
-        inputs = tokenizer(query, return_tensors="pt")
-        outputs = rag_model.generate(**inputs)
-        response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-        st.write(f"Answer: {response[0]}")
+# Generate a response when the user submits input
+if st.button("Talk to the Chatbot"):
+    if user_input:
+        with st.spinner('Generating response...'):
+            response = generate_response(user_input)
+            st.write(f"Chatbot: {response}")
     else:
-        st.write("Please enter a question to get an answer.")
+        st.write("Please enter something for the chatbot to respond to.")