Init Repo

Home.py

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+import streamlit as st
+
+st.set_page_config(
+    page_title="Hello",
+    page_icon="👋",
+)
+
+st.write("# Welcome to SPACE! 👋")
+
+st.sidebar.success("Select a demo above.")
+
+st.markdown(
+    """
+    Hello, a simple demo from SPACE.
+"""
+)

pages/1_FitInOne.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+
+st.title("Fit Your Data")
+
+default_data = {
+    "X": [1, 2, 3, 4, 5],
+    "Y": [2.2, 4.4, 6.5, 8.0, 10.1],
+    "Select": [True, True, True, True, True]
+}
+data = pd.DataFrame(default_data)
+
+with st.sidebar:
+    st.subheader("Enter Your Data")
+    user_data = st.data_editor(data, num_rows="dynamic", key="data_editor")
+    fit_type = st.radio(
+        "Choose the Type of Fit",
+        options=["Logarithmic", "Linear", "Linearithmic", "Quadratic", "Cubic", "Exponential"],
+        index=0
+    )
+
+try:
+    selected_data = user_data[user_data["Select"]]
+    x = np.array(selected_data["X"], dtype=float)
+    y = np.array(selected_data["Y"], dtype=float)
+
+    if len(x) < 2 and len(y) < 2:
+        st.warning("Please enter at least 2 data points.")
+        st.stop()
+except ValueError:
+    st.error("Invalid data entered. Please ensure all values are numeric.")
+    st.stop()
+
+
+if fit_type == "Logarithmic":
+    try:
+        log_x = np.log(x)
+        coefficients = np.polyfit(log_x, y , 1)
+        y_fit = coefficients[0] * log_x + coefficients[1]
+        r2 = r2_score(y, y_fit)
+        equation = f"y = {coefficients[0]:.2f}*log(x) + {coefficients[1]:.2f}"
+    except ValueError:
+        st.error("Logarithmic fit failed. Ensure all X values are positive.")
+        st.stop()
+
+elif fit_type == "Linear":
+    degree = 1
+    coefficients = np.polyfit(x, y, degree)
+    y_fit = np.polyval(coefficients, x)
+    r2 = r2_score(y, y_fit)
+    equation = f"y = {coefficients[0]:.2f}*x + {coefficients[1]:.2f}"
+
+elif fit_type == "Linearithmic":
+    try:
+        x_log_x = x * np.log(x)
+        A = np.column_stack((x_log_x, x, np.ones_like(x)))
+        coefficients, _, _, _ = np.linalg.lstsq(A, y, rcond=None)
+        a, b, c = coefficients
+        y_fit = a * x_log_x + b * x + c
+        r2 = r2_score(y, y_fit)
+        equation = f"y = {a:.2f}*x*log(x) + {b:.2f}*x + {c:.2f}"
+    except ValueError:
+        st.error("Linearithmic fir failed. Ensure all X values are positive.")
+        st.stop()
+
+elif fit_type == "Quadratic":
+    degree = 2
+    coefficients = np.polyfit(x, y, degree)
+    y_fit = np.polyval(coefficients, x)
+    r2 = r2_score(y, y_fit)
+    equation = f"y = {coefficients[0]:.2f}*x² + {coefficients[1]:.2f}*x + {coefficients[2]:.2f}"
+
+elif fit_type == "Cubic":
+    degree = 3
+    coefficients = np.polyfit(x, y, degree)
+    y_fit = np.polyval(coefficients, x)
+    r2 = r2_score(y, y_fit)
+    equation = f"y = {coefficients[0]:.2f}*x³ + {coefficients[1]:.2f}*x² + {coefficients[2]:.2f}*x + {coefficients[3]:.2f}"
+
+elif fit_type == "Exponential":
+    try:
+        log_y = np.log(y)
+        coefficients = np.polyfit(x, log_y, 1)
+        a = np.exp(coefficients[1])
+        b = coefficients[0]
+        y_fit = a * np.exp(b * x)
+        r2 = r2_score(y, y_fit)
+        equation = f"y = {a:.2f}*exp({b:.2f}*x)"
+    except ValueError:
+        st.error("Exponential fit failed. Ensure all Y values are positive.")
+        st.stop()
+
+x_smooth = np.linspace(min(x), max(x), 500)
+if fit_type == "Logarithmic":
+    y_smooth = coefficients[0] * np.log(x_smooth) + coefficients[1]
+elif fit_type == "Linearithmic":
+    y_smooth = a * x_smooth * np.log(x_smooth) + b * x_smooth + c
+elif fit_type == "Exponential":
+    y_smooth = a * np.exp(b * x_smooth)
+else:
+    y_smooth = np.polyval(coefficients, x_smooth)
+
+
+fig, ax = plt.subplots()
+ax.scatter(x, y, color="red", label="Original Data")
+ax.plot(x_smooth, y_smooth, color="blue", label=f"{fit_type} Fit (R²={r2:.2f})")
+ax.set_xlabel("X-axis")
+ax.set_ylabel("Y-axis")
+ax.legend()
+ax.set_title("Fit")
+
+st.pyplot(fig)
+
+st.write(f"**Fitted Equation**: {equation}")
+st.write(f"**R² Value**: {r2:.4f}")

pages/2_Chatbot.py

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+from openai import OpenAI
+import streamlit as st
+
+with st.sidebar:
+    IFC_API_KEY = st.text_input("HF access tokens", key="chat_bot_api_key", type="password")
+    model = st.radio(
+        "Choose a model to chat with",
+        ["Qwen/Qwen2.5-72B-Instruct", "meta-llama/Llama-3.3-70B-Instruct", "Qwen/QwQ-32B-Preview"],
+        captions=[
+            "By Qwen",
+            "By Meta",
+            "By Qwen",
+        ],
+    )
+    temperature = st.slider("Temperature", 0.01, 0.99, 0.5)
+    top_p = st.slider("Top_p", 0.01, 0.99, 0.7)
+    max_tokens = st.slider("Max Tokens", 128, 4096, 2048)
+
+st.title("💬 Chatbot")
+st.caption(" A HF chatbot powered by HF")
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+if prompt := st.chat_input():
+    if not IFC_API_KEY:
+        st.info("Please add your access token to continue.")
+        st.stop()
+
+    client = OpenAI(api_key=IFC_API_KEY)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+    response = client.chat.completions.create(
+        model=model,
+        messages = st.session_state.messages,
+        temperature = temperature,
+        max_tokens = max_tokens,
+        top_p = top_p,
+    )
+    msg = response.choices[0].message.content
+    st.session_state.messages.append({"role": "assistant", "content": msg})
+    st.chat_message("assistant").write(msg)

requirements.txt

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+streamlit
+matplotlib
+numpy
+scikitlearn
+openai