Update app.py

app.py

@@ -1,86 +1,108 @@
 import streamlit as st
 import requests
 import logging
+import time
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Page configuration
+# Set page configuration
 st.set_page_config(
     page_title="DeepSeek Chatbot - NextGenWebAI",
     page_icon="🤖",
-    layout="centered"
+    layout="wide"
 )
 
+# Custom CSS for UI Enhancements
+st.markdown("""
+    <style>
+    body {
+        font-family: 'Arial', sans-serif;
+    }
+    .stChatMessage {
+        border-radius: 10px;
+        padding: 10px;
+        margin: 5px 0;
+    }
+    .user {
+        background-color: #007BFF;
+        color: white;
+        text-align: right;
+        border-radius: 12px 12px 0px 12px;
+    }
+    .assistant {
+        background-color: #F1F1F1;
+        color: black;
+        text-align: left;
+        border-radius: 12px 12px 12px 0px;
+    }
+    .sidebar .sidebar-content {
+        background-color: #222;
+        color: white;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
 # Initialize session state for chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
-# Sidebar configuration
+# Sidebar Configuration
 with st.sidebar:
-    st.header("Model Configuration")
-    st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")
+    st.image("https://huggingface.co/front/thumbnails/hf-logo.png", width=150)
+    st.header("⚙️ Model Configuration")
+    st.markdown("[🔑 Get HuggingFace Token](https://huggingface.co/settings/tokens)")
 
-    # Dropdown to select model
+    # Model selection
     model_options = [
         "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
     ]
-    selected_model = st.selectbox("Select Model", model_options, index=0)
+    selected_model = st.selectbox("🔍 Select AI Model", model_options, index=0)
 
+    # System prompt
     system_message = st.text_area(
-        "System Message",
-        value="You are a friendly chatbot created by https://cursor-ai-demo.vercel.app. Provide clear, accurate, and brief answers. Keep responses polite, engaging, and to the point. If unsure, politely suggest alternatives.",
-        height=100
-    )
-
-    max_tokens = st.slider(
-        "Max Tokens",
-        10, 4000, 100
+        "📜 System Instructions",
+        value="You are a friendly chatbot. Provide clear and engaging responses.",
+        height=80
     )
 
-    temperature = st.slider(
-        "Temperature",
-        0.1, 4.0, 0.3
-    )
-
-    top_p = st.slider(
-        "Top-p",
-        0.1, 1.0, 0.6
-    )
+    # Chat settings
+    max_tokens = st.slider("🔢 Max Tokens", 10, 4000, 300)
+    temperature = st.slider("🔥 Temperature", 0.1, 2.0, 0.7)
+    top_p = st.slider("🎯 Top-p", 0.1, 1.0, 0.9)
 
 # Function to query the Hugging Face API
 def query(payload, api_url):
     headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}
-    logger.info(f"Sending request to {api_url} with payload: {payload}")
-    response = requests.post(api_url, headers=headers, json=payload)
-    logger.info(f"Received response: {response.status_code}, {response.text}")
     try:
+        response = requests.post(api_url, headers=headers, json=payload)
+        response.raise_for_status()  # Raise HTTP errors if any
         return response.json()
-    except requests.exceptions.JSONDecodeError:
-        logger.error(f"Failed to decode JSON response: {response.text}")
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Request Error: {e}")
         return None
 
-# Chat interface
+# Main Chat Interface
 st.title("🤖 DeepSeek Chatbot")
-st.caption("Powered by Hugging Face Inference API - Configure in sidebar")
+st.caption("🚀 AI-powered chatbot using Hugging Face API")
 
-# Display chat history
+# Display chat history with enhanced UI
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
-        st.markdown(message["content"])
+        class_name = "user" if message["role"] == "user" else "assistant"
+        st.markdown(f"<div class='{class_name}'>{message['content']}</div>", unsafe_allow_html=True)
 
-# Handle input
-if prompt := st.chat_input("Type your message..."):
+# Handle user input
+if prompt := st.chat_input("💬 Type your message..."):
     st.session_state.messages.append({"role": "user", "content": prompt})
 
     with st.chat_message("user"):
-        st.markdown(prompt)
+        st.markdown(f"<div class='user'>{prompt}</div>", unsafe_allow_html=True)
 
     try:
-        with st.spinner("Generating response..."):
-            # Prepare the payload for the API
-            # Combine system message and user input into a single prompt
+        with st.spinner("🤖 Thinking..."):
+            time.sleep(1)  # Simulate processing time
             full_prompt = f"{system_message}\n\nUser: {prompt}\nAssistant:"
             payload = {
                 "inputs": full_prompt,
@@ -92,37 +114,16 @@ if prompt := st.chat_input("Type your message..."):
                 }
             }
 
-            # Dynamically construct the API URL based on the selected model
             api_url = f"https://api-inference.huggingface.co/models/{selected_model}"
-            logger.info(f"Selected model: {selected_model}, API URL: {api_url}")
-
-            # Query the Hugging Face API using the selected model
             output = query(payload, api_url)
 
-            # Handle API response
-            if output is not None and isinstance(output, list) and len(output) > 0:
-                if 'generated_text' in output[0]:
-                    # Extract the assistant's response
-                    assistant_response = output[0]['generated_text'].strip()
-
-                    # Check for and remove duplicate responses
-                    responses = assistant_response.split("\n</think>\n")
-                    unique_response = responses[0].strip()
-
-                    logger.info(f"Generated response: {unique_response}")
-
-                    # Append response to chat only once
-                    with st.chat_message("assistant"):
-                        st.markdown(unique_response)
-
-                    st.session_state.messages.append({"role": "assistant", "content": unique_response})
-                else:
-                    logger.error(f"Unexpected API response structure: {output}")
-                    st.error("Error: Unexpected response from the model. Please try again.")
+            if output and isinstance(output, list) and 'generated_text' in output[0]:
+                assistant_response = output[0]['generated_text'].strip()
+                with st.chat_message("assistant"):
+                    st.markdown(f"<div class='assistant'>{assistant_response}</div>", unsafe_allow_html=True)
+                st.session_state.messages.append({"role": "assistant", "content": assistant_response})
             else:
-                logger.error(f"Empty or invalid API response: {output}")
-                st.error("Error: Unable to generate a response. Please check the model and try again.")
-
+                st.error("⚠️ Unable to generate a response. Please try again.")
     except Exception as e:
         logger.error(f"Application Error: {str(e)}", exc_info=True)
-        st.error(f"Application Error: {str(e)}")
+        st.error(f"⚠️ Error: {str(e)}")