Update app.py

app.py

@@ -1,83 +1,99 @@
 import streamlit as st
 from huggingface_hub import InferenceClient
 import os
+import sys
+import pickle
 
-st.title("CODEFUSSION ☄")  
+st.title("CODEFUSSION ☄")
 
 base_url = "https://api-inference.huggingface.co/models/"
 API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
 
 model_links = {
-    "LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",  
-    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",  
-    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct" 
+    "LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",
+    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct"
 }
 
 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""",
+        'description': """The LegacyLift model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \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""",
+        'description': """The ModernMigrate model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \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""",
+        'description': """The RetroRecode model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \n\nThis model is best suited for critical development, practical knowledge, and serverless inference.\n""",
         'logo': './3.jpg'
     },
 }
 
-def format_prompt(message, conversation_history, custom_instructions=None):
+def format_promt(message, conversation_history, custom_instructions=None):
     prompt = ""
     if custom_instructions:
-        prompt += f"[INST] {custom_instructions} [/INST]"
+        prompt += f"\[INST\] {custom_instructions} \[/INST\]"
     
-    prompt += "[CONV_HISTORY]\n"
+    # Add conversation history to the prompt
+    prompt += "\[CONV_HISTORY\]\n"
     for role, content in conversation_history:
         prompt += f"{role.upper()}: {content}\n"
-    prompt += "[/CONV_HISTORY]"
+    prompt += "\[/CONV_HISTORY\]"
+    
+    # Add the current message
+    prompt += f"\[INST\] {message} \[/INST\]"
     
-    prompt += f"[INST] {message} [/INST]"
     return prompt
 
 def reset_conversation():
     '''
     Resets Conversation
     '''
+    st.session_state.conversation = []
     st.session_state.messages = []
-    st.session_state.conversation_history = []
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-    st.session_state.conversation_history = []
+    return None
+
+def load_conversation_history():
+    history_file = "conversation_history.pickle"
+    if os.path.exists(history_file):
+        with open(history_file, "rb") as f:
+            conversation_history = pickle.load(f)
+    else:
+        conversation_history = []
+    return conversation_history
+
+def save_conversation_history(conversation_history):
+    history_file = "conversation_history.pickle"
+    with open(history_file, "wb") as f:
+        pickle.dump(conversation_history, f)
 
 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)
+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
 
 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 *")
+
+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.session_state.conversation_history = []
     st.session_state.prev_option = selected_model
 
-repo_id = model_links[selected_model]
+reset_conversation()
 
+repo_id = model_links[selected_model]
 st.subheader(f'{selected_model}')
 
+# Load the conversation history from the file
+st.session_state.messages = load_conversation_history()
+
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
@@ -88,15 +104,21 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, How can I help you today?"
         st.markdown(prompt)
     
     st.session_state.messages.append({"role": "user", "content": prompt})
-    st.session_state.conversation_history.append(("user", prompt))
-
-    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(formatted_text, temperature=temp_values, max_new_tokens=3000, stream=True)
-        response = "".join([chunk for chunk in output])
-        st.markdown(response)
+    conversation_history = [(message["role"], message["content"]) for message in st.session_state.messages]
+    
+    formated_text = format_promt(prompt, conversation_history, custom_instruction)
     
-    st.session_state.messages.append({"role": "assistant", "content": response})
-    st.session_state.conversation_history.append(("assistant", response))
+    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})
+        
+        # Save the updated conversation history to the file
+        save_conversation_history(st.session_state.messages)