used gradio

app.py

@@ -1,7 +1,7 @@
-import streamlit as st
+import gradio as gr
 from llama_cpp import Llama
-import os
 import json
+import os
 import time
 
 # Function to convert message history to prompt
@@ -9,7 +9,7 @@ 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|>"
+        prompt += f"{message['content']}<|eot_id|}"
     prompt = prompt[:-10]
     return prompt
 
@@ -21,64 +21,54 @@ llm = Llama.from_pretrained(
     verbose=False
 )
 
-# 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']}")
+# Initialize chat history
+messages = [
+    {
+        'role': 'system',
+        'content': 'You are a professional physics master. Answer physics questions directly without using any external resources.'
+    }
+]
 
-# 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")
+# Function to handle user input and generate a response
+def chat_with_physics_master(user_input, chat_history):
+    global messages  # Ensure we can modify the global messages variable
 
-if submit_button and user_input:
     # Append user message
     user_message = {'role': 'user', 'content': user_input}
-    st.session_state.messages.append(user_message)
+    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=st.session_state.messages,
+        messages=messages,
         stream=True
     )
 
     for chunk in response:
         delta = chunk['choices'][0]['delta']
         if 'role' in delta:
-            st.session_state.messages.append({'role': delta['role'], 'content': ''})
+            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
-    st.session_state.messages[-1]['content'] = full_response
+    messages[-1]['content'] = full_response
+
+    # Return the entire chat history for display
+    return [(msg['role'], msg['content']) for msg in messages]
 
-    # Display the full response as a paragraph
-    st.write(f"**Physics Master:** {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.",
+)
 
-# 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)
+# Launch the Gradio app
+iface.launch()