Delete app.py
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app.py
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import streamlit as st
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import requests
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import os
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# Initialize chat history in session state
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = []
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st.title("DeepSeek-V3 Chatbot")
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# Display chat messages from history
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for message in st.session_state.chat_history:
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st.write(f"**{message['role'].capitalize()}:** {message['content']}")
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# Input for user message
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user_input = st.text_input("You:", key="user_input")
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if st.button("Send"):
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if user_input:
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# Append user message to chat history
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st.session_state.chat_history.append({"role": "user", "content": user_input})
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# Prepare payload for Hugging Face Inference API
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payload = {
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"inputs": {
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"past_user_inputs": [msg['content'] for msg in st.session_state.chat_history if msg['role'] == 'user'],
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"generated_responses": [msg['content'] for msg in st.session_state.chat_history if msg['role'] == 'assistant'],
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"text": user_input
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}
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}
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# Fetch API token from environment variables
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API_TOKEN = os.getenv("API_TOKEN")
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if not API_TOKEN:
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st.error("API_TOKEN not found. Please set it as an environment variable.")
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else:
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api_url = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-V3"
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headers = {"Authorization": f"Bearer {API_TOKEN}"}
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# Send request to Hugging Face Inference API
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response = requests.post(api_url, headers=headers, json=payload)
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if response.status_code == 200:
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assistant_reply = response.json().get('generated_text', '')
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# Append assistant's reply to chat history
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st.session_state.chat_history.append({"role": "assistant", "content": assistant_reply})
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st.write(f"**Assistant:** {assistant_reply}")
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else:
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st.error("Error: Unable to get response from DeepSeek-V3 model.")
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else:
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st.warning("Please enter a message.")
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if st.button("Clear Chat"):
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st.session_state.chat_history = []
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