Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,226 @@
-import altair as alt
-import numpy as np
+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
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# ======================================================
+# ⚙️ 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 and local open-source models.")
+
+# ======================================================
+# 🔐 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 SETTINGS
+# ======================================================
+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"
+        ],
+        index=0
+    )
+
+    ANALYST_MODEL = st.selectbox(
+        "Select Analysis Model (Local Open-Source Recommended):",
+        [            
+            "meta-llama/Meta-Llama-3-8B-Instruct",
+            "Qwen/Qwen2.5-Coder-7B-Instruct",
+            "HuggingFaceH4/zephyr-7b-beta",
+            "mistralai/Mistral-7B-Instruct-v0.3"
+        ],
+        index=0
+    )
 
+    temperature = st.slider("Temperature", 0.0, 1.0, 0.3)
+    max_tokens = st.slider("Max Tokens", 128, 2048, 512)
+
+# Initialize cleaner client (HF API)
+cleaner_client = InferenceClient(model=CLEANER_MODEL, token=HF_TOKEN)
+
+# Initialize local analyst if open-source
+local_analyst = None
+if ANALYST_MODEL in ["meta-llama/Meta-Llama-3-8B-Instruct"]:
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(ANALYST_MODEL)
+        model = AutoModelForCausalLM.from_pretrained(ANALYST_MODEL)
+        local_analyst = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    except Exception as e:
+        st.warning(f"⚠️ Failed to load local analyst: {e}")
+
+# ======================================================
+# 🧩 DATA CLEANING FUNCTIONS
+# ======================================================
+def fallback_clean(df: pd.DataFrame) -> pd.DataFrame:
+    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":
+            df[col].fillna(df[col].mode()[0] if not df[col].mode().empty else "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:
+    raw_preview = df.head(5).to_csv(index=False)
+    prompt = f"""
+You are a Python data cleaning expert.
+Clean and standardize the dataset dynamically:
+- Handle missing values logically
+- Correct and normalize column names
+- Detect and fix datatype inconsistencies
+- Remove duplicates or invalid rows
+Return ONLY valid CSV text (no Markdown).
+
+--- RAW SAMPLE ---
+{raw_preview}
 """
-# Welcome to Streamlit!
+    try:
+        response = cleaner_client.text_generation(prompt, max_new_tokens=1024, temperature=0.1, return_full_text=False)
+        cleaned_str = response.strip()
+    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 = [l for l in cleaned_str.splitlines() if "," in l]
+    cleaned_str = "\n".join(lines)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+    try:
+        cleaned_df = pd.read_csv(StringIO(cleaned_str), on_bad_lines="skip")
+        cleaned_df.dropna(axis=1, how="all", inplace=True)
+        cleaned_df.columns = [c.strip().replace(" ", "_").lower() for c in cleaned_df.columns]
+        return cleaned_df
+    except Exception as e:
+        st.warning(f"⚠️ CSV parse failed: {e}")
+        return fallback_clean(df)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+def summarize_dataframe(df: pd.DataFrame) -> str:
+    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]):
+            mean = df[col].mean()
+            median = 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)
+
+# ======================================================
+# 🧠 ANALYSIS FUNCTION
+# ======================================================
+def query_analysis_model(df: pd.DataFrame, user_query: str, dataset_name: str) -> str:
+    df_summary = summarize_dataframe(df)
+    sample = df.head(6).to_csv(index=False)
+    prompt = f"""
+You are a data analyst.
+Analyze '{dataset_name}' and answer the question below.
+Base your insights only on the provided data.
+
+--- SUMMARY ---
+{df_summary}
+
+--- SAMPLE DATA ---
+{sample}
+
+--- QUESTION ---
+{user_query}
+
+Respond concisely with key insights, numbers, patterns, and recommended steps.
 """
+    if local_analyst:
+        try:
+            response = local_analyst(prompt, max_new_tokens=max_tokens, temperature=temperature)
+            return response[0]['generated_text']
+        except Exception as e:
+            return f"⚠️ Local analysis failed: {e}"
+    else:
+        st.warning("⚠️ Analyst model is not local. Using HF API may require payment.")
+        return "Analysis not available for free model."
+
+# ======================================================
+# 🚀 MAIN APP
+# ======================================================
+uploaded = st.file_uploader("📎 Upload CSV or Excel file", type=["csv", "xlsx"])
+
+if uploaded:
+    try:
+        df = pd.read_csv(uploaded) if uploaded.name.endswith(".csv") else pd.read_excel(uploaded)
+    except Exception as e:
+        st.error(f"❌ File load failed: {e}")
+        st.stop()
+
+    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"):
+        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.")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    st.subheader("💬 Ask AI About Your Data")
+    user_query = st.text_area("Enter your question:", placeholder="e.g. What factors influence sales?")
+    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.")