Update app.py

app.py

@@ -40,44 +40,89 @@
 # if __name__ == "__main__":
 #     main()
 import streamlit as st
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-# Load the DialoGPT model and tokenizer
-@st.cache_resource
-def load_model():
-    model_name = "microsoft/DialoGPT-medium"
-    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-    model = GPT2LMHeadModel.from_pretrained(model_name)
-    return model, tokenizer
-
-# Function to generate a response from DialoGPT
-def generate_response(input_text, model, tokenizer):
-    inputs = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=150, pad_token_id=tokenizer.eos_token_id, do_sample=True, top_p=0.9, top_k=50)
-    response = tokenizer.decode(outputs[:, inputs.shape[-1]:][0], skip_special_tokens=True)
-    return response
-
-# Streamlit UI setup
-def main():
-    st.title("DialoGPT Chatbot")
-
-    # Chat history
-    if 'history' not in st.session_state:
-        st.session_state['history'] = []
-
-    user_input = st.text_input("You:", "")
-    
-    # Generate and display response
-    if user_input:
-        model, tokenizer = load_model()
-        response = generate_response(user_input, model, tokenizer)
-        st.session_state['history'].append({"user": user_input, "bot": response})
-    
-    # Display chat history
-    for chat in st.session_state['history']:
-        st.write(f"You: {chat['user']}")
-        st.write(f"Bot: {chat['bot']}")
+from transformers import pipeline
+
+# Configure the Hugging Face API key
+HUGGINGFACE_API_KEY = st.secrets['huggingface_api_key']
+
+# Initialize the Hugging Face conversational model
+# You can replace 'microsoft/DialoGPT-medium' with any other supported conversational model
+chatbot = pipeline("conversational", model="microsoft/DialoGPT-medium")
+
+# Function to get response from the Hugging Face model
+def get_chatbot_response(user_input):
+    try:
+        conversation = chatbot(user_input)
+        return conversation[0]['generated_text']  # Extract the generated response
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Streamlit interface
+st.set_page_config(page_title="Smart ChatBot", layout="centered")
+
+# Custom CSS for chat bubbles with full width and emojis
+st.markdown("""
+    <style>
+    .chat-container {
+        display: flex;
+        flex-direction: column;
+        width: 100%;
+    }
+    .chat-bubble {
+        width: 100%;
+        padding: 15px;
+        margin: 10px 0;
+        border-radius: 10px;
+        font-size: 18px;
+        color: white;
+        display: inline-block;
+        line-height: 1.5;
+    }
+    .user-bubble {
+        background: #6a82fb; /* Soft blue */
+        align-self: flex-end;
+        border-radius: 10px 10px 10px 10px;
+    }
+    .bot-bubble {
+        background: #fc5c7d; /* Soft pink */
+        align-self: flex-start;
+        border-radius: 10px 10px 10px 10px;
+    }
+    .chat-header {
+        # text-align: center;
+        font-size: 35px;
+        font-weight: bold;
+        margin-bottom: 20px;
+        color: #3d3d3d;
+    }
+    .emoji {
+        font-size: 22px;
+        margin-right: 10px;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+st.markdown('<div class="chat-header">Hugging Face Chatbot-Your AI Companion 💻</div>', unsafe_allow_html=True)
+st.write("Powered by Hugging Face for smart, engaging conversations. 🤖")
+
+if "history" not in st.session_state:
+    st.session_state["history"] = []
+
+with st.form(key="chat_form", clear_on_submit=True):
+    user_input = st.text_input("Your message here... ✍️", max_chars=2000, label_visibility="collapsed")
+    submit_button = st.form_submit_button("Send 🚀")
+
+    if submit_button:
+        if user_input:
+            response = get_chatbot_response(user_input)
+            st.session_state.history.append((user_input, response))
+        else:
+            st.warning("Please Enter A Prompt 😅")
 
-if __name__ == "__main__":
-    main()
+if st.session_state["history"]:
+    st.markdown('<div class="chat-container">', unsafe_allow_html=True)
+    for user_input, response in st.session_state["history"]:
+        st.markdown(f'<div class="chat-bubble user-bubble"><span class="emoji">👤</span>You: {user_input}</div>', unsafe_allow_html=True)
+        st.markdown(f'<div class="chat-bubble bot-bubble"><span class="emoji">🤖</span>Bot: {response}</div>', unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)