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Update app.py
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app.py
CHANGED
@@ -1,62 +1,24 @@
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# from transformers import GPT2LMHeadModel, GPT2Tokenizer
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# # Load the GPT-2 model and tokenizer
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# @st.cache_resource
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# def load_model():
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# model_name = "gpt2"
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# tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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# model = GPT2LMHeadModel.from_pretrained(model_name)
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# return model, tokenizer
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# # Function to generate a response from GPT-2
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# def generate_response(input_text, model, tokenizer):
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# inputs = tokenizer.encode(input_text, return_tensors="pt")
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# outputs = model.generate(inputs, max_length=150, do_sample=True, top_p=0.9, top_k=50)
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# response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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# return response
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# # Streamlit UI setup
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# def main():
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# st.title("GPT-2 Chatbot")
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# # Chat history
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# if 'history' not in st.session_state:
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# st.session_state['history'] = []
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# user_input = st.text_input("You:", "")
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# # Generate and display response
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# if user_input:
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# model, tokenizer = load_model()
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# response = generate_response(user_input, model, tokenizer)
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# st.session_state['history'].append({"user": user_input, "bot": response})
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# # Display chat history
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# for chat in st.session_state['history']:
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# st.write(f"You: {chat['user']}")
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# st.write(f"Bot: {chat['bot']}")
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# if __name__ == "__main__":
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# main()
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import streamlit as st
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from transformers import pipeline
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#
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HUGGINGFACE_API_KEY = st.secrets[
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# Initialize the
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chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium"
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# Function to get response from the
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def get_chatbot_response(user_input):
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try:
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except Exception as e:
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return f"Error: {str(e)}"
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# Streamlit interface
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st.set_page_config(page_title="Smart ChatBot", layout="centered")
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# Custom CSS for chat bubbles with full width and emojis
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@@ -88,7 +50,7 @@ st.markdown("""
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border-radius: 10px 10px 10px 10px;
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}
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.chat-header {
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font-size: 35px;
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font-weight: bold;
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margin-bottom: 20px;
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@@ -101,8 +63,8 @@ st.markdown("""
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</style>
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""", unsafe_allow_html=True)
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st.markdown('<div class="chat-header">
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st.write("Powered by Hugging Face for smart, engaging conversations. 🤖")
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if "history" not in st.session_state:
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st.session_state["history"] = []
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import os
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import streamlit as st
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from transformers import pipeline
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# Set the Hugging Face API key in the environment (if required)
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HUGGINGFACE_API_KEY = st.secrets["huggingface_api_key"]
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os.environ["HF_HOME"] = HUGGINGFACE_API_KEY # Set the Hugging Face API key
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# Initialize the text generation pipeline (without passing api_key)
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chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
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# Function to get response from the model
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def get_chatbot_response(user_input):
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try:
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# Generate response from the model
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response = chatbot(user_input)
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return response[0]["generated_text"]
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except Exception as e:
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return f"Error: {str(e)}"
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# Streamlit interface setup
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st.set_page_config(page_title="Smart ChatBot", layout="centered")
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# Custom CSS for chat bubbles with full width and emojis
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border-radius: 10px 10px 10px 10px;
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}
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.chat-header {
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text-align: center;
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font-size: 35px;
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font-weight: bold;
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margin-bottom: 20px;
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</style>
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""", unsafe_allow_html=True)
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st.markdown('<div class="chat-header">Gemini Chatbot-Your AI Companion 💻</div>', unsafe_allow_html=True)
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st.write("Powered by Hugging Face’s DialoGPT model for smart, engaging conversations. 🤖")
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if "history" not in st.session_state:
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st.session_state["history"] = []
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