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import os
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from io import StringIO
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
# Set API token
os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("hf")
os.environ['HF_TOKEN'] = os.getenv("hf")
st.title("📊 DataCraft CSV")
st.subheader("– Crafting insights from structured data")
# Session state for chat history
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# Upload CSV
uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
if uploaded_file:
df = pd.read_csv(uploaded_file)
st.success("✅ File loaded successfully!")
st.subheader("🔍 Quick Summary")
st.write("**Shape:**", df.shape)
st.write("**Columns:**", df.columns.tolist())
st.write("**Missing Values:**")
st.dataframe(df.isnull().sum())
st.write("**Data Types:**")
st.dataframe(df.dtypes)
st.subheader("💬 Ask a question about the dataset")
user_input = st.text_input("E.g. 'What are the average values?', 'Plot sales over time'")
# Hugging Face Model Setup
deepseek = HuggingFaceEndpoint(
repo_id="deepseek-ai/DeepSeek-R1",
provider="nebius",
temperature=0.5,
max_new_tokens=150,
task="conversational"
)
model = ChatHuggingFace(
llm=deepseek,
repo_id=deepseek.repo_id,
provider=deepseek.provider,
temperature=0.5,
max_new_tokens=150,
task="conversational"
)
if user_input:
df_sample = df.head(50).to_csv(index=False)
prompt = f"""
You are a helpful data analyst. Here's a preview of the dataset and a user question. Provide an answer in plain English. If the question mentions plotting, include the code as well.
Dataset:
{df_sample}
User question: {user_input}
"""
with st.spinner("Thinking..."):
try:
response = model.invoke([{"role": "user", "content": prompt}])
result = response.content if hasattr(response, "content") else response
st.session_state.chat_history.append((user_input, result))
st.markdown("### 🧠 Answer")
st.write(result)
# Optional: Execute simple plot command if mentioned
if "plot" in user_input.lower():
with st.expander("📈 Try plotting automatically"):
try:
# Try simple detection for column plots
cols = df.select_dtypes(include='number').columns.tolist()
if len(cols) >= 2:
fig, ax = plt.subplots()
sns.lineplot(data=df, x=cols[0], y=cols[1], ax=ax)
st.pyplot(fig)
else:
st.info("Could not find enough numeric columns to plot.")
except Exception as e:
st.error(f"Plotting failed: {e}")
except Exception as e:
st.error(f"Error: {e}")
# Display previous chat history
if st.session_state.chat_history:
st.subheader("📚 Previous Q&A")
for q, a in st.session_state.chat_history:
st.markdown(f"**You:** {q}")
st.markdown(f"**Bot:** {a}") |