Create app.py

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+import os
+import sys
+
+st.title("CODEFUSSION ☄")  # Changed from "strangerzone.world🗞️"
+
+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",  # Changed from "Dorado🥤"
+    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",  # Changed from "Hercules⭐"
+    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct"  # Changed from "Lepus🚀"
+}
+
+# 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': './dorado.png'
+    },
+
+    "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': './hercules.png'
+    },
+
+    "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""",
+        'logo': './lepus.png'
+    },
+}
+
+def format_promt(message, custom_instructions=None):
+    prompt = ""
+    if custom_instructions:
+        prompt += f"[INST] {custom_instructions} [/INST]"
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def reset_conversation():
+    '''
+    Resets Conversation
+    '''
+    st.session_state.conversation = []
+    st.session_state.messages = []
+    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("*Generated content may be inaccurate or false.*")
+st.sidebar.markdown("\nYou can support me by sponsoring to buy me a coffee🥤.[here](https://buymeacoffee.com/prithivsakthi).")
+
+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]
+
+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"]):
+        st.markdown(message["content"])
+
+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})
+
+    formated_text = format_promt(prompt, 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})