update app.py

app.py

@@ -51,12 +51,23 @@ def create_conversational_chain(vector_store):
     replicate_api_token = "r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl"  # Replace with your actual token
     os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
 
-    print("Length of text_chunks:", len(text_chunks))
-    print("Content of text_chunks:", text_chunks)
-
-    print("Length of embeddings:", len(embeddings))
-    print("Content of embeddings:", embeddings)
-
+    # Add debugging statements
+    st.write("Text chunks lengths:", [len(chunk) for chunk in text_chunks])
+    st.write("Text chunks content:", text_chunks)
+
+    # Create embeddings
+    st.write("Creating embeddings...")
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                       model_kwargs={'device': 'cpu'})
+    
+    # Add debugging statements
+    st.write("Embeddings lengths:", [len(emb) for emb in embeddings])
+    st.write("Embeddings content:", embeddings)
+
+    vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
+    
+    st.write("Creating conversation chain...")
+    
     llm = Replicate(
         streaming=True,
         model="replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781",
@@ -72,6 +83,7 @@ def create_conversational_chain(vector_store):
     return chain
 
 
+
 def main():
     initialize_session_state()
     st.title("Chat With Your Doc")