Update app.py

app.py

@@ -1,7 +1,7 @@
-import gradio as gr
+import streamlit as st
 from llama_cpp import Llama
-import json
 import os
+import json
 import time
 
 # Function to convert message history to prompt
@@ -9,8 +9,8 @@ def prompt_from_messages(messages):
     prompt = ''
     for message in messages:
         prompt += f"<|start_header_id|>{message['role']}<|end_header_id|>\n\n"
-        prompt += f"{message['content']}<|eot_id|>{{}}"  # Corrected here
-    prompt = prompt[:-10]  # Adjust the slicing accordingly
+        prompt += f"{message['content']}<|eot_id|>"
+    prompt = prompt[:-10]
     return prompt
 
 # Initialize the Llama model
@@ -21,54 +21,64 @@ llm = Llama.from_pretrained(
     verbose=False
 )
 
-# Initialize chat history
-messages = [
-    {
-        'role': 'system',
-        'content': 'You are a professional physics master. Answer physics questions directly without using any external resources.'
-    }
-]
+# Set up Streamlit App Layout
+st.title("Physics Master Chatbot")
+st.markdown("Ask **Physics Master** any physics-related question.")
+
+# Initialize chat history in session state
+if 'messages' not in st.session_state:
+    st.session_state.messages = [
+        {
+            'role': 'system',
+            'content': 'You are a professional physics master. Answer physics questions directly without using any external resources.'
+        }
+    ]
+    st.session_state.chat_time = time.time()
+
+# Display chat history
+for message in st.session_state.messages:
+    if message['role'] == 'user':
+        st.write(f"**You:** {message['content']}")
+    else:
+        st.write(f"**Physics Master:** {message['content']}")
 
-# Function to handle user input and generate a response
-def chat_with_physics_master(user_input):
-    global messages  # Ensure we can modify the global messages variable
+# Use a form to manage user input and submission
+with st.form(key="input_form", clear_on_submit=True):
+    user_input = st.text_input("Ask a question", key="user_input")
+    submit_button = st.form_submit_button(label="Send")
 
+if submit_button and user_input:
     # Append user message
     user_message = {'role': 'user', 'content': user_input}
-    messages.append(user_message)
+    st.session_state.messages.append(user_message)
 
     # Prepare to get the response from Physics Master
+    st.write('Physics Master is thinking...')
+
+    # Initialize an empty string to accumulate the response
     full_response = ""
 
     # Fetch response tokens and accumulate them
     response = llm.create_chat_completion(
-        messages=messages,
+        messages=st.session_state.messages,
         stream=True
     )
 
     for chunk in response:
         delta = chunk['choices'][0]['delta']
         if 'role' in delta:
-            messages.append({'role': delta['role'], 'content': ''})
+            st.session_state.messages.append({'role': delta['role'], 'content': ''})
         elif 'content' in delta:
             token = delta['content']
             # Accumulate tokens into the full response
             full_response += token
 
     # Once the full response is received, append it to the chat history
-    messages[-1]['content'] = full_response
-
-    # Return the entire chat history for display
-    return [(msg['role'], msg['content']) for msg in messages]
+    st.session_state.messages[-1]['content'] = full_response
 
-# Gradio interface
-iface = gr.Interface(
-    fn=chat_with_physics_master,
-    inputs=gr.inputs.Textbox(label="Ask a question"),
-    outputs=gr.outputs.Chatbox(label="Chat History"),
-    title="Physics Master Chatbot",
-    description="Ask **Physics Master** any physics-related question.",
-)
+    # Display the full response as a paragraph
+    st.write(f"**Physics Master:** {full_response}")
 
-# Launch the Gradio app
-iface.launch()
+# Save the chat history to a JSON file
+with open('chat_history.json', 'w', encoding='utf8') as file:
+    json.dump(st.session_state.messages, file, indent=4)