First commit

app.py

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+import streamlit as st
+
+from transformers import pipeline
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=200))
+
+
+# Create a banner using Markdown
+st.markdown(
+    """
+    <div style="min-width: 1000px;">
+        <div style="background-color:#f63366;padding:10px;border-radius:10px;">
+            <h1 style="color:white;text-align:center;">Red Octopus</h1>
+        </div>
+        <div style="color:black;text-align:center;">
+            <h1>Welcome to the Proposition Management Tool</h1>
+        </div>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+selectedCity = st.selectbox("Please select the City and the Bank Product for Your Proposition.", ["CharlesTown", "Limburg"])
+selectedProduct = st.selectbox("Please select the Product", ["Current", "Mortage", "Credit Card", "Crypto"])
+userProposal = st.text_input("Enter your Proposition for Select City and Product")
+
+submit_button = st.button("Submit")
+
+if submit_button:
+    st.write("You clicked the Submit button!")
+    st.write("Entered text:", userProposal)
+    result = pipe(f"<s>[INST] {userProposal} [/INST]")
+    st.write(result)
+

dump.txt

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+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        assistant_response = random.choice(
+            [
+                "Hello there! How can I assist you today?",
+                "Hi, human! Is there anything I can help you with?",
+                "Do you need help?",
+            ]
+        )
+        # Simulate stream of response with milliseconds delay
+        for chunk in assistant_response.split():
+            full_response += chunk + " "
+            time.sleep(0.05)
+            # Add a blinking cursor to simulate typing
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})

htmlTemplates.py

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+css="""<style>
+    body {
+        font-family: 'Arial', sans-serif;
+        background-color: #f0f0f0;
+        margin: 0;
+        padding: 0;
+    }
+
+    #banner {
+        background-color: #ff4500;
+        color: white;
+        padding: 10px;
+        text-align: center;
+        font-size: 24px;
+    }
+
+    #form-container {
+        margin: 50px auto;
+        padding: 20px;
+        background-color: #ffffff;
+        border-radius: 8px;
+        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    }
+
+    input[type="text"] {
+        width: 100%;
+        padding: 10px;
+        margin-bottom: 10px;
+        border: 1px solid #ccc;
+        border-radius: 4px;
+        box-sizing: border-box;
+    }
+
+    button {
+        background-color: #4caf50;
+        color: white;
+        padding: 10px 15px;
+        border: none;
+        border-radius: 4px;
+        cursor: pointer;
+        font-size: 16px;
+    }
+
+    #output {
+        margin-top: 20px;
+        padding: 10px;
+        background-color: #f8f8f8;
+        border: 1px solid #ddd;
+        border-radius: 4px;
+    }
+</style>
+"""
+bot_template = """
+<div id="banner">
+    Red Octopus
+</div>
+
+<div id="form-container">
+    <form id="input_form">
+        <label for="input_text">Enter your text:</label>
+        <input type="text" id="input_text" name="input_text" required>
+        <br>
+        <button type="button" onclick="submitForm()">Submit</button>
+    </form>
+</div>
+
+<div id="output"></div>
+
+<script>
+    function submitForm() {
+        var inputText = document.getElementById('input_text').value;
+        fetch('/predict', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({ text: inputText }),
+        })
+        .then(response => response.json())
+        .then(data => {
+            document.getElementById('output').innerText = 'Model Prediction: ' + data.prediction;
+        });
+    }
+</script>
+"""

requirements.txt

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+streamlit
+altair==4.0