Merge branch 'project-RAG-GUI' of https://github.com/arjbingly/Capsto…

…ne_5 into project-RAG-GUI

projects/RAG-GUI/app.py

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+import streamlit as st
+
+import os
+
+# App title
+st.set_page_config(page_title="RAG")
+with st.sidebar:
+    st.title('RAG ')
+    st.subheader('Models and parameters')
+    selected_model = st.sidebar.selectbox('Choose a model', ['Llama2-7B', 'Llama2-13B', 'Mixtral 8x7B','Gemma 7B'], key='selected_model')
+    temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=5.0, value=0.1, step=0.01)
+    top_k = st.sidebar.slider('top_k', min_value=1.0, max_value=5.0, value=1.0, step=1.0)
+
+
+st.title("Welcome to the RAG App")
+
+st.write(f"You have selected the {selected_model} model with the following parameters:")
+st.write(f"Temperature: {temperature}")
+st.write(f"Top-k: {top_k}")
+# st.write(f"Max Length: {max_length}")
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+
+
+# def generate_response(user_input, model, temperature, top_k, max_length):
+#     # inputs = tokenizer(user_input, return_tensors="pt")
+#     outputs = model.generate(user_input, max_length=max_length, do_sample=True, top_k=top_k, temperature=temperature)
+#     # response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+#     return outputs
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# User input
+user_input = st.text_area("Enter your query:", height=200)
+
+# Process user input
+if user_input:
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    # Here you can add the code to process the user input and generate a response using the selected model and parameters
+    # For example:
+    # response = generate_response(user_input, selected_model, temperature, top_k, max_length)
+    # st.session_state.messages.append({"role": "assistant", "content": response})
+