fix

app.py

@@ -1,136 +1,27 @@
 import streamlit as st
-from llama_cpp_cuda_tensorcores import Llama
-from huggingface_hub import hf_hub_download
-import spaces
-
-# Constants
-REPO_ID = "MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF"
-MODEL_NAME = "Meta-Llama-3-70B-Instruct.Q3_K_L.gguf"
-MAX_CONTEXT_LENGTH = 8192
-CUDA = True
-SYSTEM_PROMPT = "You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability."
-TOKEN_STOP = ["<|eot_id|>"]
-SYS_MSG = "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nSYSTEM_PROMPT<|eot_id|>\n"
-USER_PROMPT = (
-    "<|start_header_id|>user<|end_header_id|>\n\nUSER_PROMPT<|eot_id|>\n"
-)
-ASSIS_PROMPT = "<|start_header_id|>assistant<|end_header_id|>\n\n"
-END_ASSIS_PREVIOUS_RESPONSE = "<|eot_id|>\n"
-
-TASK_PROMPT = {
-    "Assistant": SYSTEM_PROMPT,
+import requests
+
+# API URL and headers
+API_URL = "https://pllfc7e5i0rujahy.us-east-1.aws.endpoints.huggingface.cloud"
+headers = {
+    "Accept": "application/json",
+    "Authorization": "Bearer hf_XXXXX",  # Replace with your actual token
+    "Content-Type": "application/json"
 }
 
-# ChatLLM class for handling the chat
-class ChatLLM:
-    def __init__(self, config_model):
-        self.llm = None
-        self.config_model = config_model
-
-    def load_cpp_model(self):
-        self.llm = Llama(**self.config_model)
-
-    def apply_chat_template(self, history, system_message):
-        history = history or []
-        messages = SYS_MSG.replace("SYSTEM_PROMPT", system_message.strip())
-        for msg in history:
-            messages += (
-                USER_PROMPT.replace("USER_PROMPT", msg[0]) + ASSIS_PROMPT + msg[1]
-            )
-            messages += END_ASSIS_PREVIOUS_RESPONSE if msg[1] else ""
-
-        return messages
-
-    @spaces.GPU(duration=120)
-    def response(
-        self,
-        history,
-        system_message,
-        max_tokens,
-        temperature,
-        top_p,
-        top_k,
-        repeat_penalty,
-    ):
-        messages = self.apply_chat_template(history, system_message)
-
-        history[-1][1] = ""
-
-        if not self.llm:
-            print("Loading model")
-            self.load_cpp_model()
-
-        for output in self.llm(
-            messages,
-            echo=False,
-            stream=True,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-            repeat_penalty=repeat_penalty,
-            stop=TOKEN_STOP,
-        ):
-            answer = output["choices"][0]["text"]
-            history[-1][1] += answer
-
-        return history
-
-# Download model from Hugging Face
-model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_NAME)
-
-# Model configuration
-config_model = {
-    "model_path": model_path,
-    "n_ctx": MAX_CONTEXT_LENGTH,
-    "n_gpu_layers": -1 if CUDA else 0,
-}
-
-# Instantiate the chat model
-llm_chat = ChatLLM(config_model)
-
-# Streamlit UI
-st.title("AI Chat Assistant")
-
-# Initialize session state to store the chat history
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
-if "input_text" not in st.session_state:
-    st.session_state.input_text = ""
-
-# Define response area
-def chat_response():
-    if st.session_state.input_text.strip():
-        # User message
-        history = st.session_state.chat_history
-        history.append([st.session_state.input_text, ""])
-
-        # Model response
-        history = llm_chat.response(
-            history=history,
-            system_message=SYSTEM_PROMPT,
-            max_tokens=100,  # Adjust token length as needed
-            temperature=0.7,
-            top_p=0.9,
-            top_k=50,
-            repeat_penalty=1.0,
-        )
-
-        st.session_state.chat_history = history
-        st.session_state.input_text = ""
-
-# Textbox for user input
-st.text_input("You: ", key="input_text", on_change=chat_response)
+# Function to query the API
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
-# Display chat history
-if st.session_state.chat_history:
-    for user_msg, bot_resp in st.session_state.chat_history:
-        st.markdown(f"**You:** {user_msg}")
-        st.markdown(f"**Assistant:** {bot_resp}")
+# Streamlit app layout
+st.title("API Query App")
+st.write("This app allows you to query the API and retrieve responses.")
 
-# Clear chat button
-def clear_chat():
-    st.session_state.chat_history = []
+user_input = st.text_input("Enter your input:", "Can you please let us know more details about your ")
 
-st.button("Clear History", on_click=clear_chat)
+if st.button("Submit"):
+    with st.spinner("Fetching response..."):
+        output = query({"inputs": user_input, "parameters": {}})
+        st.success("Response received!")
+        st.json(output)  # Display the response in a nice format

requirements.txt

@@ -0,0 +1 @@
+requests