src/streamlit_app.py

import os
import pandas as pd
import numpy as np
import streamlit as st
import plotly.express as px
import plotly.figure_factory as ff
from dotenv import load_dotenv
from huggingface_hub import InferenceClient, login
from io import StringIO

# ======================================================
# ⚙️ APP CONFIGURATION
# ======================================================
st.set_page_config(page_title="📊 Smart Data Analyst Pro", layout="wide")
st.title("📊 Smart Data Analyst Pro")
st.caption("AI that cleans, analyzes, and visualizes your data — powered by Hugging Face Inference API.")

# ======================================================
# 🔐 Load Environment Variables
# ======================================================
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_API_KEY")
if not HF_TOKEN:
    st.error("❌ Missing HF_TOKEN. Please set it in your .env file.")
else:
    login(token=HF_TOKEN)

# ======================================================
# 🧠 MODEL SETUP
# ======================================================
with st.sidebar:
    st.header("⚙️ Model Settings")

    CLEANER_MODEL = st.selectbox(
        "Select Cleaner Model:",
        [
            "Qwen/Qwen2.5-Coder-7B-Instruct",
            "meta-llama/Meta-Llama-3-8B-Instruct",
            "microsoft/Phi-3-mini-4k-instruct",
            "mistralai/Mistral-7B-Instruct-v0.3"
        ],
        index=0
    )

    ANALYST_MODEL = st.selectbox(
        "Select Analysis Model:",
        [
            "Qwen/Qwen2.5-14B-Instruct",
            "mistralai/Mistral-7B-Instruct-v0.3",
            "HuggingFaceH4/zephyr-7b-beta"
        ],
        index=0
    )

    temperature = st.slider("Temperature", 0.0, 1.0, 0.3)
    max_tokens = st.slider("Max Tokens", 128, 2048, 512)

# Initialize inference clients
cleaner_client = InferenceClient(model=CLEANER_MODEL, token=HF_TOKEN)
analyst_client = InferenceClient(model=ANALYST_MODEL, token=HF_TOKEN)

# ======================================================
# 🧩 SAFE GENERATION FUNCTION
# ======================================================
def safe_hf_generate(client, prompt, temperature=0.3, max_tokens=512):
    """
    Tries text_generation first, then falls back to chat_completion if not supported.
    Returns plain string content.
    """
    try:
        resp = client.text_generation(
            prompt,
            temperature=temperature,
            max_new_tokens=max_tokens,
            return_full_text=False,
        )
        return resp.strip()
    except Exception as e:
        if "Supported task: conversational" in str(e) or "not supported" in str(e):
            chat_resp = client.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                max_tokens=max_tokens,
                temperature=temperature,
            )
            return chat_resp["choices"][0]["message"]["content"].strip()
        else:
            raise e

# ======================================================
# 🧩 SMART DATA CLEANING
# ======================================================
def fallback_clean(df: pd.DataFrame) -> pd.DataFrame:
    """Backup rule-based cleaner."""
    df = df.copy()
    df.dropna(axis=1, how="all", inplace=True)
    df.columns = [c.strip().replace(" ", "_").lower() for c in df.columns]
    for col in df.columns:
        if df[col].dtype == "O":
            if not df[col].mode().empty:
                df[col].fillna(df[col].mode()[0], inplace=True)
            else:
                df[col].fillna("Unknown", inplace=True)
        else:
            df[col].fillna(df[col].median(), inplace=True)
    df.drop_duplicates(inplace=True)
    return df


def ai_clean_dataset(df: pd.DataFrame) -> pd.DataFrame:
    """Cleans the dataset using the selected AI model. Falls back gracefully if the model fails."""
    raw_preview = df.head(5).to_csv(index=False)
    prompt = f"""
You are a professional data cleaning assistant.
Clean and standardize the dataset below dynamically:
1. Handle missing values
2. Fix column name inconsistencies
3. Convert data types (dates, numbers, categories)
4. Remove irrelevant or duplicate rows
Return ONLY a valid CSV text (no markdown, no explanations).

--- RAW SAMPLE ---
{raw_preview}
"""

    try:
        cleaned_str = safe_hf_generate(cleaner_client, prompt, temperature=0.1, max_tokens=1024)
    except Exception as e:
        st.warning(f"⚠️ AI cleaning failed: {e}")
        return fallback_clean(df)

    cleaned_str = (
        cleaned_str.replace("```csv", "")
        .replace("```", "")
        .replace("###", "")
        .replace(";", ",")
        .strip()
    )

    lines = cleaned_str.splitlines()
    lines = [line for line in lines if "," in line and not line.lower().startswith(("note", "summary"))]
    cleaned_str = "\n".join(lines)

    try:
        cleaned_df = pd.read_csv(StringIO(cleaned_str), on_bad_lines="skip")
        cleaned_df = cleaned_df.dropna(axis=1, how="all")
        cleaned_df.columns = [c.strip().replace(" ", "_").lower() for c in cleaned_df.columns]
        return cleaned_df
    except Exception as e:
        st.warning(f"⚠️ AI CSV parse failed: {e}")
        return fallback_clean(df)


def summarize_dataframe(df: pd.DataFrame) -> str:
    """Generate a concise summary of the dataframe."""
    lines = [f"Rows: {len(df)} | Columns: {len(df.columns)}", "Column summaries:"]
    for col in df.columns[:10]:
        non_null = int(df[col].notnull().sum())
        if pd.api.types.is_numeric_dtype(df[col]):
            desc = df[col].describe().to_dict()
            mean = float(desc.get("mean", np.nan))
            median = float(df[col].median()) if non_null > 0 else None
            lines.append(f"- {col}: mean={mean:.3f}, median={median}, non_null={non_null}")
        else:
            top = df[col].value_counts().head(3).to_dict()
            lines.append(f"- {col}: top_values={top}, non_null={non_null}")
    return "\n".join(lines)


def query_analysis_model(df: pd.DataFrame, user_query: str, dataset_name: str) -> str:
    """Send the dataframe and user query to the analysis model for interpretation."""
    df_summary = summarize_dataframe(df)
    sample = df.head(6).to_csv(index=False)
    prompt = f"""
You are a professional data analyst.
Analyze the dataset '{dataset_name}' and answer the user's question.

--- SUMMARY ---
{df_summary}

--- SAMPLE DATA ---
{sample}

--- USER QUESTION ---
{user_query}

Respond with:
1. Key insights and patterns
2. Quantitative findings
3. Notable relationships or anomalies
4. Data-driven recommendations
"""

    try:
        response = safe_hf_generate(analyst_client, prompt, temperature=temperature, max_tokens=max_tokens)
        return response
    except Exception as e:
        return f"⚠️ Analysis failed: {e}"

# ======================================================
# 🚀 MAIN APP LOGIC
# ======================================================
uploaded = st.file_uploader("📎 Upload CSV or Excel file", type=["csv", "xlsx"])

if uploaded:
    df = pd.read_csv(uploaded) if uploaded.name.endswith(".csv") else pd.read_excel(uploaded)

    with st.spinner("🧼 AI Cleaning your dataset..."):
        cleaned_df = ai_clean_dataset(df)

    st.subheader("✅ Cleaned Dataset Preview")
    st.dataframe(cleaned_df.head(), use_container_width=True)

    with st.expander("📋 Cleaning Summary", expanded=False):
        st.text(summarize_dataframe(cleaned_df))

    with st.expander("📈 Quick Visualizations", expanded=True):
        numeric_cols = cleaned_df.select_dtypes(include="number").columns.tolist()
        categorical_cols = cleaned_df.select_dtypes(exclude="number").columns.tolist()

        viz_type = st.selectbox(
            "Visualization Type",
            ["Scatter Plot", "Histogram", "Box Plot", "Correlation Heatmap", "Categorical Count"]
        )

        if viz_type == "Scatter Plot" and len(numeric_cols) >= 2:
            x = st.selectbox("X-axis", numeric_cols)
            y = st.selectbox("Y-axis", numeric_cols, index=min(1, len(numeric_cols)-1))
            color = st.selectbox("Color", ["None"] + categorical_cols)
            fig = px.scatter(cleaned_df, x=x, y=y, color=None if color=="None" else color)
            st.plotly_chart(fig, use_container_width=True)

        elif viz_type == "Histogram" and numeric_cols:
            col = st.selectbox("Column", numeric_cols)
            fig = px.histogram(cleaned_df, x=col, nbins=30)
            st.plotly_chart(fig, use_container_width=True)

        elif viz_type == "Box Plot" and numeric_cols:
            col = st.selectbox("Column", numeric_cols)
            fig = px.box(cleaned_df, y=col)
            st.plotly_chart(fig, use_container_width=True)

        elif viz_type == "Correlation Heatmap" and len(numeric_cols) > 1:
            corr = cleaned_df[numeric_cols].corr()
            fig = ff.create_annotated_heatmap(
                z=corr.values,
                x=list(corr.columns),
                y=list(corr.index),
                annotation_text=corr.round(2).values,
                showscale=True
            )
            st.plotly_chart(fig, use_container_width=True)

        elif viz_type == "Categorical Count" and categorical_cols:
            cat = st.selectbox("Category", categorical_cols)
            fig = px.bar(cleaned_df[cat].value_counts().reset_index(), x="index", y=cat)
            st.plotly_chart(fig, use_container_width=True)
        else:
            st.warning("⚠️ Not enough columns for this visualization type.")

    st.subheader("💬 Ask AI About Your Data")
    user_query = st.text_area("Enter your question:", placeholder="e.g. What factors influence sales the most?")
    if st.button("Analyze with AI", use_container_width=True) and user_query:
        with st.spinner("🤖 Interpreting data..."):
            result = query_analysis_model(cleaned_df, user_query, uploaded.name)
        st.markdown("### 💡 Insights")
        st.markdown(result)
else:
    st.info("📥 Upload a dataset to begin smart analysis.")