Update app.py

app.py

@@ -1,15 +1,11 @@
 import streamlit as st
 from huggingface_hub import InferenceClient
 import os
-import sys
 
 st.title("CODEFUSSION ☄")  
 
 base_url = "https://api-inference.huggingface.co/models/"
-
 API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
-# print(API_KEY)
-# headers = {"Authorization":"Bearer "+API_KEY}
 
 model_links = {
     "LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",  
@@ -17,20 +13,17 @@ model_links = {
     "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct" 
 }
 
-# Pull info about the model to display
 model_info = {
     "LegacyLift🚀": {
         'description': """The LegacyLift model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
             \nThis model is best for minimal problem-solving, content writing, and daily tips.\n""",
         'logo': './11.jpg'
     },
-
     "ModernMigrate⭐": {
         'description': """The ModernMigrate model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
             \nThis model excels in coding, logical reasoning, and high-speed inference. \n""",
         'logo': './2.jpg'
     },
-
     "RetroRecode🔄": {
         'description': """The RetroRecode model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
           \nThis model is best suited for critical development, practical knowledge, and serverless inference.\n""",
@@ -38,10 +31,16 @@ model_info = {
     },
 }
 
-def format_promt(message, custom_instructions=None):
+def format_prompt(message, conversation_history, custom_instructions=None):
     prompt = ""
     if custom_instructions:
         prompt += f"[INST] {custom_instructions} [/INST]"
+    
+    prompt += "[CONV_HISTORY]\n"
+    for role, content in conversation_history:
+        prompt += f"{role.upper()}: {content}\n"
+    prompt += "[/CONV_HISTORY]"
+    
     prompt += f"[INST] {message} [/INST]"
     return prompt
 
@@ -49,40 +48,35 @@ def reset_conversation():
     '''
     Resets Conversation
     '''
-    st.session_state.conversation = []
     st.session_state.messages = []
-    return None
+    st.session_state.conversation_history = []
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+    st.session_state.conversation_history = []
 
 models = [key for key in model_links.keys()]
 
 selected_model = st.sidebar.selectbox("Select Model", models)
-
-temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
-
-st.sidebar.button('Reset Chat', on_click=reset_conversation)  # Reset button
+temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
+st.sidebar.button('Reset Chat', on_click=reset_conversation)
 
 st.sidebar.write(f"You're now chatting with **{selected_model}**")
 st.sidebar.markdown(model_info[selected_model]['description'])
 st.sidebar.image(model_info[selected_model]['logo'])
 st.sidebar.markdown("*Generating the code might go slow if you are using low power resources *")
 
-
 if "prev_option" not in st.session_state:
     st.session_state.prev_option = selected_model
 
 if st.session_state.prev_option != selected_model:
     st.session_state.messages = []
-    # st.write(f"Changed to {selected_model}")
+    st.session_state.conversation_history = []
     st.session_state.prev_option = selected_model
-    reset_conversation()
 
 repo_id = model_links[selected_model]
 
 st.subheader(f'{selected_model}')
-# st.title(f'ChatBot Using {selected_model}')
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
 
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
@@ -90,24 +84,19 @@ for message in st.session_state.messages:
 
 if prompt := st.chat_input(f"Hi I'm {selected_model}, How can I help you today?"):
     custom_instruction = "Act like a Human in conversation"
-
     with st.chat_message("user"):
         st.markdown(prompt)
-
+    
     st.session_state.messages.append({"role": "user", "content": prompt})
+    st.session_state.conversation_history.append(("user", prompt))
 
-    formated_text = format_promt(prompt, custom_instruction)
+    formatted_text = format_prompt(prompt, st.session_state.conversation_history, custom_instruction)
 
     with st.chat_message("assistant"):
-        client = InferenceClient(
-            model=model_links[selected_model], )
-
-        output = client.text_generation(
-            formated_text,
-            temperature=temp_values,  # 0.5
-            max_new_tokens=3000,
-            stream=True
-        )
-
-        response = st.write_stream(output)
-    st.session_state.messages.append({"role": "assistant", "content": response})
+        client = InferenceClient(model=model_links[selected_model])
+        output = client.text_generation(formatted_text, temperature=temp_values, max_new_tokens=3000, stream=True)
+        response = "".join([chunk for chunk in output])
+        st.markdown(response)
+    
+    st.session_state.messages.append({"role": "assistant", "content": response})
+    st.session_state.conversation_history.append(("assistant", response))