Update app.py

app.py

@@ -19,77 +19,72 @@ def load_llm():
         temperature=0.5
     )
     return llm
-# Function for conversational chat
-def conversational_chat(query):
-    result = chain({"question": query, "chat_history": st.session_state['history']})
-    st.session_state['history'].append((query, result["answer"]))
-    return result["answer"]
-def main():
-    # Set the title for the Streamlit app
-    st.title("Llama2 Chat CSV - 🦜🦙")
 
-    # Create a file uploader in the sidebar
-    uploaded_file = st.sidebar.file_uploader("Upload File", type="csv")
+# Set the title for the Streamlit app
+st.title("Llama2 Chat CSV - 🦜🦙")
 
-    # Handle file upload
-    if uploaded_file:
-        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-            tmp_file.write(uploaded_file.getvalue())
-            tmp_file_path = tmp_file.name
+# Create a file uploader in the sidebar
+uploaded_file = st.sidebar.file_uploader("Upload File", type="csv")
 
-        # Load CSV data using CSVLoader
-        loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
-        data = loader.load()
+# Handle file upload
+if uploaded_file:
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
 
-        # Create embeddings using Sentence Transformers
-        embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
+    # Load CSV data using CSVLoader
+    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
+    data = loader.load()
 
-        # Create a FAISS vector store and save embeddings
-        db = FAISS.from_documents(data, embeddings)
-        db.save_local(DB_FAISS_PATH)
+    # Create embeddings using Sentence Transformers
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
 
+    # Create a FAISS vector store and save embeddings
+    db = FAISS.from_documents(data, embeddings)
+    db.save_local(DB_FAISS_PATH)
 
-        # Load the language model
-        llm = load_llm()
 
-        # Create a conversational chain
-        chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
+    # Load the language model
+    llm = load_llm()
 
-        
+    # Create a conversational chain
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
 
-        # Initialize chat history
-        if 'history' not in st.session_state:
-            st.session_state['history'] = []
+    # Function for conversational chat
+    def conversational_chat(query):
+        result = chain({"question": query, "chat_history": st.session_state['history']})
+        st.session_state['history'].append((query, result["answer"]))
+        return result["answer"]
 
-        # Initialize messages
-        if 'generated' not in st.session_state:
-            st.session_state['generated'] = ["Hello ! Ask me(LLAMA2) about " + uploaded_file.name + " 🤗"]
+    # Initialize chat history
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
 
-        if 'past' not in st.session_state:
-            st.session_state['past'] = ["Hey ! 👋"]
+    # Initialize messages
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ["Hello ! Ask me(LLAMA2) about " + uploaded_file.name + " 🤗"]
 
-        # Create containers for chat history and user input
-        response_container = st.container()
-        container = st.container()
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey ! 👋"]
 
-        # User input form
-        with container:
-            with st.form(key='my_form', clear_on_submit=True):
-                user_input = st.text_input("Query:", placeholder="Talk to csv data 👉 (:", key='input')
-                submit_button = st.form_submit_button(label='Send')
+    # Create containers for chat history and user input
+    response_container = st.container()
+    container = st.container()
 
-            if submit_button and user_input:
-                output = conversational_chat(user_input)
-                st.session_state['past'].append(user_input)
-                st.session_state['generated'].append(output)
+    # User input form
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input("Query:", placeholder="Talk to csv data 👉 (:", key='input')
+            submit_button = st.form_submit_button(label='Send')
 
-        # Display chat history
-        if st.session_state['generated']:
-            with response_container:
-                for i in range(len(st.session_state['generated'])):
-                    message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
-                    message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")
+        if submit_button and user_input:
+            output = conversational_chat(user_input)
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
 
-
-if __name__ == "__main__":
-    main()
+    # Display chat history
+    if st.session_state['generated']:
+        with response_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")