Update app.py

app.py

@@ -1,107 +1,95 @@
 import streamlit as st
 import requests
 
-# -----------------------------------
-# 1. Hugging Face API Configuration
-# -----------------------------------
+# Hugging Face API URL
 API_URL = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
 
+# Function to query the Hugging Face API
 def query(payload):
-    """
-    Query the Hugging Face Inference API with the given payload.
-    Keeps the original approach: payload = {"inputs": user_input}.
-    """
     headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}
     response = requests.post(API_URL, headers=headers, json=payload)
     return response.json()
 
-
-# -----------------------------------
-# 2. Streamlit Page Settings
-# -----------------------------------
+# Page configuration
 st.set_page_config(
     page_title="DeepSeek Chatbot - ruslanmv.com",
     page_icon="🤖",
     layout="centered"
 )
 
-# -----------------------------------
-# 3. Session State Initialization
-# -----------------------------------
-# We'll keep a chat history in st.session_state
+# Initialize session state for chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
-# -----------------------------------
-# 4. Sidebar Configuration
-# -----------------------------------
+# Sidebar configuration
 with st.sidebar:
-    st.header("Configuration")
-    st.markdown("[Get your HuggingFace Token](https://huggingface.co/settings/tokens)")
-
-    # Although these parameters are shown on the sidebar, we won't actually
-    # pass them to the payload in `query()`, to strictly preserve the "original" approach.
-    st.write("**NOTE:** These sliders do not affect the inference in this demo.")
+    st.header("Model Configuration")
+    st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")
+    
     system_message = st.text_area(
-        "System Message (display only)",
+        "System Message",
         value="You are a friendly Chatbot created by ruslanmv.com",
         height=100
     )
-    max_tokens = st.slider("Max Tokens (not used here)", 1, 4000, 512)
-    temperature = st.slider("Temperature (not used here)", 0.1, 4.0, 0.7)
-    top_p = st.slider("Top-p (not used here)", 0.1, 1.0, 0.9)
+    
+    max_tokens = st.slider(
+        "Max Tokens",
+        1, 4000, 512
+    )
+    
+    temperature = st.slider(
+        "Temperature",
+        0.1, 4.0, 0.7
+    )
+    
+    top_p = st.slider(
+        "Top-p",
+        0.1, 1.0, 0.9
+    )
 
-# -----------------------------------
-# 5. Main Chat Interface
-# -----------------------------------
+# Chat interface
 st.title("🤖 DeepSeek Chatbot")
-st.caption("Powered by Hugging Face Inference API - Original Inference Approach")
+st.caption("Powered by Hugging Face Inference API - Configure in sidebar")
 
-# Display the chat history, message by message
+# Display chat history
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-# -----------------------------------
-# 6. Capture User Input
-# -----------------------------------
-if user_input := st.chat_input("Type your message..."):
-    # 6.1 Append user message to chat history
-    st.session_state.messages.append({"role": "user", "content": user_input})
+# Handle input
+if prompt := st.chat_input("Type your message..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
     
-    # Display user's message
     with st.chat_message("user"):
-        st.markdown(user_input)
+        st.markdown(prompt)
 
-    # -----------------------------------
-    # 7. Query the Model
-    # -----------------------------------
     try:
         with st.spinner("Generating response..."):
-            # Prepare payload with the original approach
-            payload = {"inputs": user_input}
+            # Prepare the payload for the API
+            payload = {
+                "inputs": prompt,
+                "parameters": {
+                    "max_new_tokens": max_tokens,
+                    "temperature": temperature,
+                    "top_p": top_p,
+                    "return_full_text": False
+                }
+            }
+            
+            # Query the Hugging Face API
             output = query(payload)
-
-            # Check if the output is valid
-            if (
-                isinstance(output, list) 
-                and len(output) > 0 
-                and "generated_text" in output[0]
-            ):
-                assistant_response = output[0]["generated_text"]
+            
+            # Handle API response
+            if isinstance(output, list) and len(output) > 0 and 'generated_text' in output[0]:
+                assistant_response = output[0]['generated_text']
             else:
-                assistant_response = (
-                    "Error: Unable to generate a response. Please try again."
-                )
+                st.error("Error: Unable to generate a response. Please try again.")
+                return
 
-        # Display the assistant's response
         with st.chat_message("assistant"):
             st.markdown(assistant_response)
-
-        # Store assistant's response in chat history
-        st.session_state.messages.append(
-            {"role": "assistant", "content": assistant_response}
-        )
+        
+        st.session_state.messages.append({"role": "assistant", "content": assistant_response})
 
     except Exception as e:
-        st.error(f"Application Error: {str(e)}")
+        st.error(f"Application Error: {str(e)}")