aa

app.py

@@ -1,8 +1,4 @@
 import streamlit as st
-from llama_index import download_loader, SimpleDirectoryReader, StorageContext, load_index_from_storage
-from llama_index import SimpleDirectoryReader, ServiceContext, StorageContext, VectorStoreIndex, download_loader
-from llama_index.llms import HuggingFaceLLM
-from llama_index.embeddings import HuggingFaceEmbedding
 
 import torch
 torch.set_default_device('cuda')

pages/Tesla_Cases.py

@@ -66,7 +66,7 @@ def get_vector_index(nodes, vector_store):
 
 def generate_insight(engine, search_string):
 
-    with open("prompts/report.prompt", "r") as f:
+    with open("prompts/main.prompt", "r") as f:
         template = f.read()
 
     prompt_template = PromptTemplate(
@@ -88,7 +88,7 @@ def get_query_engine(engine):
         QueryEngineTool(
             query_engine=engine,
             metadata=ToolMetadata(
-                name="Case Report",
+                name="Alert Report",
                 description=f"Provides information about the cases from its case report.",
             ),
         ),
@@ -111,20 +111,20 @@ os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 
 
 if OPENAI_API_KEY:
-    pdfs = st.sidebar.file_uploader("Upload the case report in PDF format", type="pdf")
+    pptx_files = st.sidebar.file_uploader("Upload the case report in PDF format", type="pptx")
     st.sidebar.info("""
     Example pdf reports you can upload here:
     """)
 
     if st.sidebar.button("Process Document"):
         with st.spinner("Processing Document..."):
-            nodes = process_pdf(pdfs)
-            #st.session_state.index = get_vector_index(nodes, vector_store="faiss")
-            st.session_state.index = get_vector_index(nodes, vector_store="simple")
+            nodes = process_pptx(pptx_files)
+            st.session_state.index = get_vector_index(nodes, vector_store="faiss")
+            #st.session_state.index = get_vector_index(nodes, vector_store="simple")
             st.session_state.process_doc = True
         st.toast("Document Processsed!")
 
-    #st.session_state.process_doc = True
+    st.session_state.process_doc = True
 
     if st.session_state.process_doc:
         search_text = st.text_input("Enter your question")
@@ -132,12 +132,11 @@ if OPENAI_API_KEY:
             engine = get_query_engine(st.session_state.index.as_query_engine(similarity_top_k=3))
             start_time = time.time()
 
-            with st.status("**Analyzing Report...**"):
-                st.write("Case search result...")
-                response = generate_insight(engine, search_text)
-                st.session_state["end_time"] = "{:.2f}".format((time.time() - start_time))
+            st.write("Alert search result...")
+            response = generate_insight(engine, search_text)
+            st.session_state["end_time"] = "{:.2f}".format((time.time() - start_time))
 
-                st.toast("Report Analysis Complete!")
+            st.toast("Report Analysis Complete!")
 
         if st.session_state.end_time:
             st.write("Report Analysis Time: ", st.session_state.end_time, "s")

prompts/main.prompt

@@ -1,3 +1,3 @@
 You are a tesla alert analyzer.
-Your job is the is to provide a detailed analysis of the following:
+Your job is the is to provide a detailed analysis of the alert and follow-up