Update app.py

app.py

@@ -12,46 +12,41 @@ import torch
 import textwrap 
 
 def main():
-    st.set_page_config(page_title="👨‍💻 Talk with BORROWER data")
-    st.title("👨‍💻 Talk with BORROWER data")
+    st.set_page_config(page_title="Talk with BORROWER data")
+    st.title("Talk with BORROWER data")
     uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")
 
     query = st.text_input("Send a Message")
     if st.button("Submit Query", type="primary"):
         DB_FAISS_PATH = "vectorstore/db_faiss"
 
-        if uploaded_file :
-        #use tempfile because CSVLoader only accepts a file_path
-            with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-                tmp_file.write(uploaded_file.getvalue())
-                tmp_file_path = tmp_file.name
 
-            loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={
-                        'delimiter': ','})
-            data = loader.load()
-            st.write(data)
+        loader = CSVLoader(file_path="./borrower_data.csv", encoding="utf-8", csv_args={
+                    'delimiter': ','})
+        data = loader.load()
+        st.write(data)
 
-            model = "daryl149/llama-2-7b-chat-hf" 
-            tokenizer = AutoTokenizer.from_pretrained(model) 
-            pipeline = transformers.pipeline("text-generation", #task 
-                                              model=model, 
-                                              tokenizer=tokenizer, 
-                                              torch_dtype=torch.bfloat16, 
-                                              trust_remote_code=True, 
-                                              device_map="auto",  
-                                              do_sample=True, 
-                                              top_k=5, 
-                                              num_return_sequences=1, 
-                                              eos_token_id=tokenizer.eos_token_id 
-            ) 
+        model = "daryl149/llama-2-7b-chat-hf" 
+        tokenizer = AutoTokenizer.from_pretrained(model) 
+        pipeline = transformers.pipeline("text-generation", #task 
+                                          model=model, 
+                                          tokenizer=tokenizer, 
+                                          torch_dtype=torch.bfloat16, 
+                                          trust_remote_code=True, 
+                                          device_map="auto",  
+                                          do_sample=True, 
+                                          top_k=5, 
+                                          num_return_sequences=1, 
+                                          eos_token_id=tokenizer.eos_token_id 
+        ) 
 
-            llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0}) 
-            embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') 
-            vectorstore = FAISS.from_documents(data, embeddings)
-            vectorstore.save_local(DB_FAISS_PATH)
-            chain=retrievalQA.from_chain_type(llm=llm, chain_type = "stuff",return_source_documents=True, retriever=vectorstore.as_retriever()) 
-            result=chain(query)
-            st.write(result['result'])
+        llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0}) 
+        embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') 
+        vectorstore = FAISS.from_documents(data, embeddings)
+        vectorstore.save_local(DB_FAISS_PATH)
+        chain=retrievalQA.from_chain_type(llm=llm, chain_type = "stuff",return_source_documents=True, retriever=vectorstore.as_retriever()) 
+        result=chain(query)
+        st.write(result['result'])
 
 if __name__ == '__main__':
     main()