Update app.py
Browse files
app.py
CHANGED
@@ -1,61 +1,36 @@
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import streamlit as st
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from
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for user_msg, assistant_msg in history:
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if user_msg:
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messages.append({"role": "user", "content": user_msg})
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if assistant_msg:
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messages.append({"role": "assistant", "content": assistant_msg})
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messages.append({"role": "user", "content": message})
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response = ""
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try:
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for message in client.chat_completion(
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model="mistralai/Codestral-22B-v0.1",
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messages=messages,
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max_tokens=max_tokens,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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except Exception as e:
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yield f"Error: {e}"
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# Streamlit interface
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st.title("
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system_message = st.text_input("System message", value="You are an expert python coder with in-depth knowledge of langchain.")
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max_tokens = st.slider("Max new tokens", min_value=1, max_value=2048, value=2048, step=1)
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temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.6, step=0.1)
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top_p = st.slider("Top-p (nucleus sampling)", min_value=0.1, max_value=1.0, value=0.95, step=0.05)
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# Initialize history in session state
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if "history" not in st.session_state:
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st.session_state.history = []
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response_generator = respond(user_input, st.session_state.history, system_message, max_tokens, temperature, top_p)
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response = ""
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for r in response_generator:
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response = r
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st.session_state.history[-1] = (user_input, response)
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st.session_state.user_input = "" # Clear input after sending
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# Display chat history
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st.text_area("Chat History", value="\n".join([f"User: {h[0]}\nAssistant: {h[1]}" for h in st.session_state.history]), height=300, key="chat_history")
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# User input
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st.
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# Import necessary libraries
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import streamlit as st
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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# Load the model and tokenizer
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model_name = "mistralai/Codestral-22B-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Initialize the pipeline
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text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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# Streamlit interface
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st.title("Codestral Text Generation")
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st.write("""
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This is a text generation application using the Codestral model from Mistral AI.
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Enter your prompt below and generate text.
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""")
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# User input
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user_input = st.text_area("Enter your prompt here:", "")
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if st.button("Generate"):
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if user_input:
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with st.spinner("Generating text..."):
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# Generate text using the model
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generated_text = text_generator(user_input, max_length=100, num_return_sequences=1)
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st.write("### Generated Text")
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st.write(generated_text[0]['generated_text'])
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else:
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st.warning("Please enter a prompt to generate text.")
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# Run the Streamlit app
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if __name__ == '__main__':
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st.run()
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