for enabling the context window by enabling chat history access .

app.py

@@ -3,46 +3,46 @@ from huggingface_hub import InferenceClient
 import os
 import sys
 
-st.title("CODEFUSSION ☄")  
+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",  
-    "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"
 }
 
-# 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""",
+        '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_promt(message, 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] {message} [/INST]"
+        prompt += f"\[INST\] {custom_instructions} \[/INST\]"
+    
+    # Add conversation history to the prompt
+    prompt += "\[CONV_HISTORY\]\n"
+    for role, content in conversation_history:
+        prompt += f"{role.upper()}: {content}\n"
+    prompt += "\[/CONV_HISTORY\]"
+    
+    # Add the current message
+    prompt += f"\[INST\] {message} \[/INST\]"
+    
     return prompt
 
 def reset_conversation():
@@ -54,32 +54,27 @@ def reset_conversation():
     return None
 
 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
 
 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.write(f"Changed to {selected_model}")
     st.session_state.prev_option = selected_model
-    reset_conversation()
 
-repo_id = model_links[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 = []
@@ -90,24 +85,23 @@ 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)
-
+    
+    # Append the current message to the conversation history
     st.session_state.messages.append({"role": "user", "content": prompt})
-
-    formated_text = format_promt(prompt, custom_instruction)
-
+    conversation_history = [(message["role"], message["content"]) for message in st.session_state.messages]
+    
+    formated_text = format_promt(prompt, 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})
+        st.session_state.messages.append({"role": "assistant", "content": response})