Upload folder using huggingface_hub

README.md

@@ -1,12 +1,11 @@
 ---
-title: Padmavathi Ws
-emoji: 🏢
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 6.1.0
+title: padmavathi_ws
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 6.0.1
+python_version: 3.12.10
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+Telugu Regex
+[\u0C00-\u0C7F]+

a.py

@@ -0,0 +1,31 @@
+citation ="""
+        # **Womens Studies Data Analysis Tool**
+        Developed for  
+        **Dr. Padmavathi**  
+        **Department Of Women Studies**  
+        **Sri Padmavathi Mahila Visvavidyalayam (SPMVV), Tirupati**
+
+        ---
+
+        ### **📌 Data Ownership**
+        All data presented, analyzed, or processed in this application  
+        **belongs to the Department of Women Studies, SPMVV**  
+        and has been collected exclusively under their research activities.
+
+        Any reuse, redistribution, or publication of the data  
+        **requires explicit written permission** from the institution.
+
+        ---
+
+        ### **📚 Suggested Research Citation (Copy & Use)**
+
+        > **Dr Padmavathi, [Department Of Women Studies], Sri Padmavathi Mahila Visvavidyalayam (SPMVV).**  
+        > *Data Analysis Dataset, 2025.*  
+        > Department of Women Studies, SPMVV, Tirupati.
+
+        ---
+
+        If you use this tool or the data in academic work,  
+        **please cite using the above citation format.**
+        """
+

app.py

@@ -0,0 +1,321 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import gradio as gr
+from io import BytesIO
+import base64
+import random
+import scipy.stats as ss
+
+from PIL import Image
+
+def fig_to_pil(fig):
+    buf = BytesIO()
+    fig.savefig(buf, format="png", bbox_inches="tight")
+    buf.seek(0)
+    return Image.open(buf)
+
+# -----------------------------
+# Load Data
+# -----------------------------
+df = pd.read_excel("pmusha.xlsx")
+
+numeric_cols = df.select_dtypes(include=['number']).columns
+categorical_cols = df.select_dtypes(include=['object', 'category']).columns
+
+# ----------------------------------------------------
+# 1. Descriptive Statistics
+# ----------------------------------------------------
+def get_descriptive_stats():
+    stats = df[numeric_cols].describe().T
+    stats = stats.reset_index().rename(columns={"index": "Feature"})
+    return stats
+
+def download_keyword_counts(df):
+    r = random.randint(1,1000)
+    path = f"keyword_counts_{r}.csv"
+    df.to_csv(path, index=False)
+    return path
+
+# ----------------------------------------------------
+# 2. Keyword Frequency Table + Plots
+# ----------------------------------------------------
+
+def keyword_frequency(column):
+    series = df[column].dropna().astype(str).str.split(',').explode().str.strip()
+    counts = series.value_counts().reset_index()
+    counts.columns = ["Keyword", "Count"]
+
+    # --- BAR CHART (matplotlib → PIL) ---
+    fig_bar, ax_bar = plt.subplots(figsize=(8,4))
+    ax_bar.bar(counts["Keyword"].head(15), counts["Count"].head(15))
+    ax_bar.set_title(f"Top Keywords in {column}")
+    ax_bar.set_xticklabels(counts["Keyword"].head(15), rotation=45, ha='right')
+    bar_img = fig_to_pil(fig_bar)
+    plt.close(fig_bar)
+
+    # --- PIE CHART (matplotlib → PIL) ---
+    fig_pie, ax_pie = plt.subplots(figsize=(6,6))
+    ax_pie.pie(
+        counts["Count"].head(10),
+        labels=counts["Keyword"].head(10),
+        autopct="%1.1f%%"
+    )
+    ax_pie.set_title(f"Pie Chart {column} (Distribution)")
+    pie_img = fig_to_pil(fig_pie)
+    plt.close(fig_pie)
+
+    # --- HORIZONTAL BAR CHART ---
+    fig_hbar, ax_hbar = plt.subplots(figsize=(8,6))
+    ax_hbar.barh(counts["Keyword"].head(15), counts["Count"].head(15))
+    ax_hbar.set_title(f"Top Keywords in {column} (Horizontal Bar)")
+    plt.tight_layout()
+    hbar_img = fig_to_pil(fig_hbar)
+    plt.close(fig_hbar)
+
+    # --- PARETO CHART (80/20) ---
+    counts_sorted = counts.sort_values("Count", ascending=False)
+    cum_percentage = (counts_sorted["Count"].cumsum() / counts_sorted["Count"].sum()) * 100
+
+    fig_pareto, ax1 = plt.subplots(figsize=(8,4))
+    ax1.bar(counts_sorted["Keyword"].head(15), counts_sorted["Count"].head(15), color='skyblue')
+    ax2 = ax1.twinx()
+    ax2.plot(counts_sorted["Keyword"].head(15), cum_percentage.head(15), color='red', marker="o")
+    ax1.set_xticklabels(counts_sorted["Keyword"].head(15), rotation=45, ha='right')
+    ax1.set_title(f"Pareto Analysis of {column}")
+    pareto_img = fig_to_pil(fig_pareto)
+    plt.close(fig_pareto)
+
+    # --- SCATTER PLOT (Rank vs Frequency) ---
+    counts["Rank"] = range(1, len(counts) + 1)
+
+    fig_scatter, ax_scatter = plt.subplots(figsize=(6,4))
+    ax_scatter.scatter(counts["Rank"], counts["Count"])
+    ax_scatter.set_title(f"Rank vs Frequency for {column}")
+    ax_scatter.set_xlabel("Rank (1 = most common)")
+    ax_scatter.set_ylabel("Frequency")
+    scatter_img = fig_to_pil(fig_scatter)
+    plt.close(fig_scatter)
+
+    # --- CUMULATIVE DISTRIBUTION PLOT ---
+    fig_cum, ax_cum = plt.subplots(figsize=(6,4))
+    ax_cum.plot(cum_percentage.values)
+    ax_cum.set_title(f"Cumulative Distribution of {column}")
+    ax_cum.set_ylabel("Cumulative %")
+    ax_cum.set_xlabel("Keyword Rank")
+    cum_img = fig_to_pil(fig_cum)
+    plt.close(fig_cum)
+
+
+    return counts, bar_img, pie_img, hbar_img, pareto_img, scatter_img, cum_img
+
+# ----------------------------------------------------
+# 3. Correlation Explorer
+# ----------------------------------------------------
+def explore_two_columns(col1, col2):
+    c1 = df[col1]
+    c2 = df[col2]
+
+    images = []
+    result_text = ""
+
+    # NUMERIC vs NUMERIC
+    if col1 in numeric_cols and col2 in numeric_cols:
+        # Pearson
+        corr = c1.corr(c2)
+        result_text = f"Pearson Correlation = {corr:.4f}"
+
+        # Scatter
+        fig, ax = plt.subplots(figsize=(6,4))
+        ax.scatter(c1, c2)
+        ax.set_xlabel(col1)
+        ax.set_ylabel(col2)
+        ax.set_title(f"{col1} vs {col2} (Scatter)")
+        images.append(fig_to_pil(fig))
+        plt.close(fig)
+
+        # Regression
+        fig, ax = plt.subplots(figsize=(6,4))
+        sns.regplot(x=c1, y=c2, ax=ax)
+        ax.set_title("Regression Line")
+        images.append(fig_to_pil(fig))
+        plt.close(fig)
+
+        # Distributions
+        fig, ax = plt.subplots(figsize=(6,4))
+        sns.histplot(c1, color="blue", kde=True, label=col1)
+        sns.histplot(c2, color="orange", kde=True, label=col2)
+        ax.legend()
+        ax.set_title("Distribution Comparison")
+        images.append(fig_to_pil(fig))
+        plt.close(fig)
+
+        print(result_text)
+        return result_text, None, images[0], images[1], images[2]
+
+    # CATEGORICAL vs CATEGORICAL
+    if col1 in categorical_cols and col2 in categorical_cols:
+        confusion = pd.crosstab(c1, c2)
+        v = cramers_v(confusion)
+        result_text = f"Cramér’s V = {v:.4f}"
+
+        conf = pd.crosstab(c1,c2, margins=True, margins_name="Total")    
+        conf_table = conf.reset_index()
+        conf_table.columns = ["Category_1"] + list(conf.columns)
+
+        # Heatmap
+        fig, ax = plt.subplots(figsize=(6,4))
+        sns.heatmap(confusion, cmap="Blues", annot=True, fmt="d")
+        ax.set_title("Crosstab Heatmap")
+        images.append(fig_to_pil(fig))
+        plt.close(fig)
+
+        # Bar chart
+        fig, ax = plt.subplots(figsize=(6,4))
+        confusion.sum(axis=1).plot(kind='bar', ax=ax)
+        ax.set_title(f"Correlation between {col1} and {col2}")
+        images.append(fig_to_pil(fig))
+        plt.close(fig)
+
+        print(result_text)
+        return result_text, conf_table, images[0], images[1], None
+
+    # MIXED TYPES (numeric + categorical)
+    # Ensure correct assignment
+    if col1 in categorical_cols and col2 in numeric_cols:
+        cat = col1; num = col2
+    else:
+        cat = col2; num = col1
+
+    result_text = f"Numeric vs Categorical Analysis ({num} by {cat})"
+
+    # Boxplot
+    fig, ax = plt.subplots(figsize=(6,4))
+    sns.boxplot(x=df[cat], y=df[num], ax=ax)
+    ax.set_title("Boxplot")
+    plt.xticks(rotation=45, ha='right')
+    images.append(fig_to_pil(fig))
+    plt.close(fig)
+
+    # Violin plot
+    fig, ax = plt.subplots(figsize=(6,4))
+    sns.violinplot(x=df[cat], y=df[num], ax=ax)
+    ax.set_title("Violin Plot")
+    plt.xticks(rotation=45, ha='right')
+    images.append(fig_to_pil(fig))
+    plt.close(fig)
+
+    print(result_text)
+    return result_text, None, images[0], images[1], None
+
+def cramers_v(confusion_matrix):
+    """ Cramér's V for categorical correlation """
+    chi2 = ss.chi2_contingency(confusion_matrix)[0]
+    n = confusion_matrix.sum().sum()
+    r, k = confusion_matrix.shape
+    return np.sqrt(chi2 / (n * (min(r, k) - 1)))
+
+
+def compute_correlation(col1, col2):
+    c1 = df[col1]
+    c2 = df[col2]
+
+    # Case 1: numeric vs numeric
+    if col1 in numeric_cols and col2 in numeric_cols:
+        corr = c1.corr(c2)
+        return f"Pearson Correlation = {corr:.4f}", None
+
+    # Case 2: categorical vs categorical → Cramér’s V
+    if col1 in categorical_cols and col2 in categorical_cols:
+        confusion = pd.crosstab(c1, c2)
+        v = cramers_v(confusion)
+        return f"Cramér’s V = {v:.4f}", confusion
+
+    # Case 3: keyword frequency vs numeric/categorical
+    # Convert col1 or col2 (if comma-separated) into frequency counts
+    def keyword_expand(col):
+        return df[col].dropna().astype(str).str.split(',').explode().str.strip()
+
+    if col1 in categorical_cols:
+        k = keyword_expand(col1)
+        k_counts = k.value_counts()
+        df_k = df.assign(**{f"{col1}_KEYWORD_COUNTS": df[col1].fillna("").apply(
+            lambda x: sum([k_counts.get(i.strip(), 0) for i in x.split(',') if i.strip()])
+        )})
+        c1 = df_k[f"{col1}_KEYWORD_COUNTS"]
+
+    if col2 in categorical_cols:
+        k = keyword_expand(col2)
+        k_counts = k.value_counts()
+        df_k = df.assign(**{f"{col2}_KEYWORD_COUNTS": df[col2].fillna("").apply(
+            lambda x: sum([k_counts.get(i.strip(), 0) for i in x.split(',') if i.strip()])
+        )})
+        c2 = df_k[f"{col2}_KEYWORD_COUNTS"]
+
+    corr = c1.corr(c2)
+    return f"Keyword-Frequency Based Correlation = {corr:.4f}", None
+
+
+# ----------------------------------------------------
+# Gradio UI
+# ----------------------------------------------------
+with gr.Blocks(title="DATA ANALYSIS APP") as app:
+
+    gr.Markdown("# 📊 Youth Nutritional Data Analysis System \nUpload → Analyse → Export\n Developed by Dr. Indira Priyadarsini")
+    with gr.Tab("ℹ️ About & Citation"):
+     from a import citation
+     gr.Markdown(citation)
+    with gr.Tab("1️⃣ Descriptive Statistics"):
+        btn_stats = gr.Button("Generate Stats")
+        stats_out = gr.Dataframe()
+        btn_stats.click(get_descriptive_stats, outputs=stats_out)
+
+    with gr.Tab("2️⃣ Keyword Frequency Explorer"):
+        col_select = gr.Dropdown(choices=list(categorical_cols), label="Select Column")
+        freq_table = gr.Dataframe(label="Keyword Counts")
+        bar_plot = gr.Image(label="Bar Chart")
+        pie_img = gr.Image(label="Pie Chart")
+        hbar_img = gr.Image(label="Horizontal Bar Chart")
+        pareto_img = gr.Image(label="Pareto Chart")
+        scatter_img = gr.Image(label="Rank vs Frequency Scatter")
+        cum_img = gr.Image(label="Cumulative Distribution")
+        
+        download_btn = gr.Button("Download as CSV")
+        download_file = gr.File(label="Download File")
+        col_select.change(keyword_frequency,
+                  inputs=col_select,
+                  outputs=[freq_table, bar_plot, pie_img,hbar_img, pareto_img, scatter_img, cum_img])
+        download_btn.click(
+            download_keyword_counts,
+            inputs=freq_table,
+            outputs=download_file
+        )
+
+    with gr.Tab("4️⃣ Two-Column Relationship Explorer"):
+        colA = gr.Dropdown(choices=df.columns.tolist(), label="Column A")
+        colB = gr.Dropdown(choices=df.columns.tolist(), label="Column B")
+        btn_rel = gr.Button("Explore Relationship")
+
+        rel_text = gr.Textbox(label="Summary")
+        rel_table = gr.Dataframe(label="Crosstab (if categorical)")
+        rel_img1 = gr.Image()
+        rel_img2 = gr.Image()
+        rel_img3 = gr.Image()
+
+        btn_rel.click(
+            explore_two_columns,
+            inputs=[colA, colB],
+            outputs=[rel_text, rel_table, rel_img1, rel_img2, rel_img3]
+        )
+
+    with gr.Tab("3️⃣ Correlation Explorer"):
+        col_select = gr.Dropdown(choices=categorical_cols.tolist(), label="Select Column")
+        col1 = gr.Dropdown(choices=df.columns.tolist(), label="Column 1")
+        col2 = gr.Dropdown(choices=df.columns.tolist(), label="Column 2")
+        corr_btn = gr.Button("Compute Correlation")
+        corr_text = gr.Textbox(label="Correlation Result")
+        confusion_out = gr.Dataframe(label="Categorical Crosstab (if applicable)")
+        corr_btn.click(compute_correlation, inputs=[col1, col2], outputs=[corr_text, confusion_out])
+
+app.launch(theme=gr.themes.Monochrome())

requirements.txt

@@ -0,0 +1,64 @@
+aiofiles==24.1.0
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.11.0
+brotli==1.2.0
+cerebras_cloud_sdk==1.56.1
+certifi==2025.10.5
+click==8.3.1
+contourpy==1.3.3
+cycler==0.12.1
+distro==1.9.0
+et_xmlfile==2.0.0
+fastapi==0.122.0
+ffmpy==1.0.0
+filelock==3.20.0
+fonttools==4.60.1
+fsspec==2025.10.0
+gradio==6.0.1
+gradio_client==2.0.0
+groovy==0.1.2
+h11==0.16.0
+hf-xet==1.2.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface_hub==1.1.5
+idna==3.11
+Jinja2==3.1.6
+kiwisolver==1.4.9
+markdown-it-py==4.0.0
+MarkupSafe==3.0.3
+matplotlib==3.10.7
+mdurl==0.1.2
+numpy==2.3.5
+openpyxl==3.1.5
+orjson==3.11.4
+packaging==25.0
+pandas==2.3.3
+pillow==12.0.0
+pydantic==2.12.3
+pydantic_core==2.41.4
+pydub==0.25.1
+Pygments==2.19.2
+pyparsing==3.2.5
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.3
+rich==14.2.0
+safehttpx==0.1.7
+scipy==1.16.3
+seaborn==0.13.2
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.50.0
+tomlkit==0.13.3
+tqdm==4.67.1
+typer==0.20.0
+typer-slim==0.20.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.2
+uvicorn==0.38.0