app.py.v1

# app.py
import gradio as gr
import pandas as pd
import requests
import io
import dask.dataframe as dd
from datasets import load_dataset, Image
from mlcroissant import Dataset as CroissantDataset
from huggingface_hub import get_token
import polars as pl
import warnings
import traceback
import json
import tempfile # Added for creating temporary files

# 🤫 Let's ignore those pesky warnings, shall we?
warnings.filterwarnings("ignore")

# --- ⚙️ Configuration & Constants ---
DATASET_CONFIG = {
    "caselaw": {
        "name": "common-pile/caselaw_access_project", "emoji": "⚖️",
        "methods": ["💨 API (requests)", "🧊 Dask", "🥐 Croissant"], "is_public": True,
    },
    "prompts": {
        "name": "fka/awesome-chatgpt-prompts", "emoji": "🤖",
        "methods": ["🐼 Pandas", "💨 API (requests)", "🥐 Croissant"], "is_public": True,
    },
    "finance": {
        "name": "snorkelai/agent-finance-reasoning", "emoji": "💰",
        "methods": ["🐼 Pandas", "🧊 Polars", "💨 API (requests)", "🥐 Croissant"], "is_public": False,
    },
    "medical": {
        "name": "FreedomIntelligence/medical-o1-reasoning-SFT", "emoji": "🩺",
        "methods": ["🐼 Pandas", "🧊 Polars", "💨 API (requests)", "🥐 Croissant"], "is_public": False,
    },
    "inscene": {
        "name": "peteromallet/InScene-Dataset", "emoji": "🖼️",
        "methods": ["🤗 Datasets", "🐼 Pandas", "🧊 Polars", "💨 API (requests)", "🥐 Croissant"], "is_public": False,
    },
}

# --- 🔧 Helpers & Utility Functions ---

def get_auth_headers():
    token = get_token()
    return {"Authorization": f"Bearer {token}"} if token else {}

# --- ✨ FIXED: dataframe_to_outputs to use temporary files ---
def dataframe_to_outputs(df: pd.DataFrame):
    """
    📜 Takes a DataFrame and transforms it into various formats.
    Now uses temporary files for maximum Gradio compatibility.
    """
    if df.empty:
        return "No results found. 🤷", None, None, "No results to copy."

    df_str = df.astype(str)
    markdown_output = df_str.to_markdown(index=False)
    
    # Create a temporary CSV file
    with tempfile.NamedTemporaryFile(mode='w+', delete=False, suffix='.csv', encoding='utf-8') as tmp_csv:
        df.to_csv(tmp_csv.name, index=False)
        csv_path = tmp_csv.name

    # Create a temporary XLSX file
    with tempfile.NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp_xlsx:
        df.to_excel(tmp_xlsx.name, index=False, engine='openpyxl')
        xlsx_path = tmp_xlsx.name

    tab_delimited_output = df.to_csv(sep='\t', index=False)
    
    return (
        markdown_output,
        csv_path,
        xlsx_path,
        tab_delimited_output,
    )

def handle_error(e: Exception, request=None, response=None):
    """
    😱 Oh no! An error! This function now creates a detailed debug log.
    """
    error_message = f"🚨 An error occurred: {str(e)}\n"
    auth_tip = "🔑 For gated datasets, did you log in? Try `huggingface-cli login` in your terminal."
    full_trace = traceback.format_exc()
    print(full_trace)
    if "401" in str(e) or "Gated" in str(e):
        error_message += auth_tip
    
    debug_log = f"""--- 🐞 DEBUG LOG ---\nTraceback:\n{full_trace}\n\nException Type: {type(e).__name__}\nException Details: {e}\n"""
    if request:
        debug_log += f"""\n--- REQUEST ---\nMethod: {request.method}\nURL: {request.url}\nHeaders: {json.dumps(dict(request.headers), indent=2)}\n"""
    if response is not None:
        try:
            response_text = json.dumps(response.json(), indent=2)
        except json.JSONDecodeError:
            response_text = response.text
        debug_log += f"""\n--- RESPONSE ---\nStatus Code: {response.status_code}\nHeaders: {json.dumps(dict(response.headers), indent=2)}\nContent:\n{response_text}\n"""
    
    return (
        pd.DataFrame(), gr.Gallery(None), f"### 🚨 Error\nAn error occurred. See the debug log below for details.",
        "", None, None, "", f"```python\n# 🚨 Error during execution:\n# {e}\n```",
        gr.Code(value=debug_log, visible=True)
    )

def search_dataframe(df: pd.DataFrame, query: str):
    if not query:
        return df.head(100)
    string_cols = df.select_dtypes(include=['object', 'string']).columns
    if string_cols.empty:
        return pd.DataFrame()
    mask = pd.Series([False] * len(df))
    for col in string_cols:
        mask |= df[col].astype(str).str.contains(query, case=False, na=False)
    return df[mask]

def generate_code_snippet(dataset_key: str, access_method: str, query: str):
    """
    💻 Generate Python code snippet for the current operation
    """
    config = DATASET_CONFIG[dataset_key]
    repo_id = config["name"]
    
    if "API" in access_method:
        return f'''# 🌐 API Access for {repo_id}
import requests
import pandas as pd

url = "https://datasets-server.huggingface.co/rows"
params = {{
    "dataset": "{repo_id}",
    "config": "default",
    "split": "train",
    "offset": 0,
    "length": 100
}}

headers = {{"Authorization": "Bearer YOUR_HF_TOKEN"}} if needed else {{}}
response = requests.get(url, params=params, headers=headers)

if response.status_code == 200:
    data = response.json()
    rows_data = [item['row'] for item in data['rows']]
    df = pd.json_normalize(rows_data)
    
    # Search for: "{query}"
    if "{query}":
        string_cols = df.select_dtypes(include=['object', 'string']).columns
        mask = pd.Series([False] * len(df))
        for col in string_cols:
            mask |= df[col].astype(str).str.contains("{query}", case=False, na=False)
        df = df[mask]
    
    print(f"Found {{len(df)}} results")
    print(df.head())
else:
    print(f"Error: {{response.status_code}} - {{response.text}}")
'''
    
    elif "Pandas" in access_method:
        file_path = "prompts.csv" if repo_id == "fka/awesome-chatgpt-prompts" else "train.parquet"
        return f'''# 🐼 Pandas Access for {repo_id}
import pandas as pd

# You may need: huggingface-cli login
df = pd.read_{"csv" if "csv" in file_path else "parquet"}("hf://datasets/{repo_id}/{file_path}")

# Search for: "{query}"
if "{query}":
    string_cols = df.select_dtypes(include=['object', 'string']).columns
    mask = pd.Series([False] * len(df))
    for col in string_cols:
        mask |= df[col].astype(str).str.contains("{query}", case=False, na=False)
    df = df[mask]

print(f"Found {{len(df)}} results")
print(df.head())
'''
    
    elif "Datasets" in access_method:
        return f'''# 🤗 Datasets Library Access for {repo_id}
from datasets import load_dataset
import pandas as pd

# You may need: huggingface-cli login
ds = load_dataset("{repo_id}", split="train", streaming=True)
data = list(ds.take(1000))
df = pd.DataFrame(data)

# Search for: "{query}"
if "{query}":
    string_cols = df.select_dtypes(include=['object', 'string']).columns
    mask = pd.Series([False] * len(df))
    for col in string_cols:
        mask |= df[col].astype(str).str.contains("{query}", case=False, na=False)
    df = df[mask]

print(f"Found {{len(df)}} results")
print(df.head())
'''
    
    else:
        return f"# Code generation for {access_method} not implemented yet"

# --- 🎣 Data Fetching & Processing Functions ---
def fetch_data(dataset_key: str, access_method: str, query: str):
    """
    🚀 Main mission control. Always yields a tuple of 9 values to match the UI components.
    """
    outputs = [pd.DataFrame(), None, "🏁 Ready.", "", None, None, "", "", gr.Code(visible=False)]
    req, res = None, None
    try:
        config = DATASET_CONFIG[dataset_key]
        repo_id = config["name"]
        
        # Generate code snippet
        code_snippet = generate_code_snippet(dataset_key, access_method, query)
        outputs[7] = code_snippet
        
        if "API" in access_method:
            all_results_df = pd.DataFrame()
            MAX_PAGES = 5
            PAGE_SIZE = 100

            if not query:
                MAX_PAGES = 1
                outputs[2] = "⏳ No search term. Fetching first 100 records as a sample..."
                yield tuple(outputs)

            for page in range(MAX_PAGES):
                if query:
                    outputs[2] = f"⏳ Searching page {page + 1}..."
                    yield tuple(outputs)
                
                offset = page * PAGE_SIZE
                url = f"https://datasets-server.huggingface.co/rows?dataset={repo_id}&config=default&split=train&offset={offset}&length={PAGE_SIZE}"
                headers = get_auth_headers() if not config["is_public"] else {}
                
                res = requests.get(url, headers=headers)
                req = res.request
                res.raise_for_status()
                data = res.json()

                if not data.get('rows'):
                    outputs[2] = "🏁 No more data to search."
                    yield tuple(outputs)
                    break

                # --- ✨ FIXED: JSON processing logic ---
                # Extract the actual data from the 'row' key of each item in the list
                rows_data = [item['row'] for item in data['rows']]
                page_df = pd.json_normalize(rows_data)
                
                found_in_page = search_dataframe(page_df, query)

                if not found_in_page.empty:
                    all_results_df = pd.concat([all_results_df, found_in_page]).reset_index(drop=True)
                    outputs[0] = all_results_df
                    outputs[3], outputs[4], outputs[5], outputs[6] = dataframe_to_outputs(all_results_df)
                    outputs[2] = f"✅ Found **{len(all_results_df)}** results so far..."
                    
                    if dataset_key == 'inscene':
                        gallery_data = [(row['image'], row.get('text', '')) for _, row in all_results_df.iterrows() if 'image' in row and isinstance(row['image'], Image.Image)]
                        outputs[1] = gr.Gallery(gallery_data, label="🖼️ Image Results", height=400)
                    yield tuple(outputs)

            outputs[2] = f"🏁 Search complete. Found a total of **{len(all_results_df)}** results."
            yield tuple(outputs)
            return

        outputs[2] = f"⏳ Loading data via `{access_method}`..."
        yield tuple(outputs)
        
        df = pd.DataFrame()
        
        if "Pandas" in access_method:
            file_path = f"hf://datasets/{repo_id}/"
            if repo_id == "fka/awesome-chatgpt-prompts": 
                file_path += "prompts.csv"
                df = pd.read_csv(file_path)
            else: 
                try: 
                    df = pd.read_parquet(f"{file_path}data/train-00000-of-00001.parquet")
                except:
                     try: 
                         df = pd.read_parquet(f"{file_path}train.parquet")
                     except: 
                         df = pd.read_json(f"{file_path}medical_o1_sft.json")
                         
        elif "Datasets" in access_method:
            ds = load_dataset(repo_id, split='train', streaming=True).take(1000)
            df = pd.DataFrame(ds)
            
        elif "Polars" in access_method:
            outputs[2] = "⏳ Loading with Polars..."
            yield tuple(outputs)
            if repo_id == "fka/awesome-chatgpt-prompts":
                pl_df = pl.read_csv(f"hf://datasets/{repo_id}/prompts.csv")
            else:
                pl_df = pl.read_parquet(f"hf://datasets/{repo_id}/train.parquet")
            df = pl_df.to_pandas()
            
        elif "Dask" in access_method:
            outputs[2] = "⏳ Loading with Dask..."
            yield tuple(outputs)
            dask_df = dd.read_json(f"hf://datasets/{repo_id}/**/*.jsonl.gz")
            df = dask_df.head(1000)  # Convert to pandas for processing
            
        elif "Croissant" in access_method:
            outputs[2] = "⏳ Loading with Croissant..."
            yield tuple(outputs)
            headers = get_auth_headers() if not config["is_public"] else {}
            croissant_url = f"https://huggingface.co/api/datasets/{repo_id}/croissant"
            response = requests.get(croissant_url, headers=headers)
            response.raise_for_status()
            jsonld = response.json()
            ds = CroissantDataset(jsonld=jsonld)
            records = list(ds.records("default"))[:1000]  # Take first 1000
            df = pd.DataFrame(records)
        
        outputs[2] = "🔍 Searching loaded data..."
        yield tuple(outputs)

        final_df = search_dataframe(df, query)
        
        outputs[0] = final_df
        outputs[3], outputs[4], outputs[5], outputs[6] = dataframe_to_outputs(final_df)
        outputs[2] = f"🏁 Search complete. Found **{len(final_df)}** results."
        
        if dataset_key == 'inscene' and not final_df.empty:
            gallery_data = [(row['image'], row.get('text', '')) for _, row in final_df.iterrows() if 'image' in row and isinstance(row.get('image'), Image.Image)]
            outputs[1] = gr.Gallery(gallery_data, label="🖼️ Image Results", height=400)
        
        yield tuple(outputs)

    except Exception as e:
        yield handle_error(e, req, res)


# --- 🖼️ UI Generation ---
def create_dataset_tab(dataset_key: str):
    config = DATASET_CONFIG[dataset_key]
    
    with gr.Tab(f"{config['emoji']} {dataset_key.capitalize()}"):
        gr.Markdown(f"## {config['emoji']} Query the `{config['name']}` Dataset")
        if not config['is_public']:
            gr.Markdown("**Note:** This is a gated dataset. Please log in via `huggingface-cli login` in your terminal first.")
        
        with gr.Row():
            access_method = gr.Radio(config['methods'], label="🔑 Access Method", value=config['methods'][0])
            query = gr.Textbox(label="🔍 Search Query", placeholder="Enter any text to search, or leave blank for samples...")
        
        fetch_button = gr.Button("🚀 Go Fetch!")
        status_output = gr.Markdown("🏁 Ready to search.")
        df_output = gr.DataFrame(label="📊 Results DataFrame", interactive=False, wrap=True)
        gallery_output = gr.Gallery(visible=(dataset_key == 'inscene'), label="🖼️ Image Results")

        with gr.Accordion("📂 View/Export Full Results", open=False):
            markdown_output = gr.Markdown(label="📝 Markdown View")
            with gr.Row():
                csv_output = gr.File(label="⬇️ Download CSV")
                xlsx_output = gr.File(label="⬇️ Download XLSX")
            copy_output = gr.Code(label="📋 Copy-Paste (Tab-Delimited)")
        
        code_output = gr.Code(label="💻 Python Code Snippet", language="python")
        
        debug_log_output = gr.Code(label="🐞 Debug Log", visible=False)
        
        fetch_button.click(
            fn=fetch_data,
            inputs=[gr.State(dataset_key), access_method, query],
            outputs=[
                df_output, gallery_output, status_output, markdown_output,
                csv_output, xlsx_output, copy_output, code_output,
                debug_log_output
            ]
        )

# --- 🚀 Main App ---
with gr.Blocks(theme=gr.themes.Soft(), title="Hugging Face Dataset Explorer") as demo:
    gr.Markdown("# 🤗 Hugging Face Dataset Explorer")
    gr.Markdown(
        "Select a dataset, choose an access method, and type a query. "
        "If an error occurs, a detailed debug log will appear to help troubleshoot the issue."
    )
    
    with gr.Accordion("🔧 Quick Start Guide", open=False):
        gr.Markdown("""
        ### 🚀 Quick Start:
        1. **🤖 Prompts Tab**: Try API method, search for "translator" or "linux"
        2. **⚖️ Caselaw Tab**: Try API method, search for "contract" or "court"
        3. **💰 Finance Tab**: Requires login, try API method first
        4. **🩺 Medical Tab**: Requires login, try API method first  
        5. **🖼️ InScene Tab**: Requires login, try Datasets method for images
        
        ### 🔑 Authentication:
        For gated datasets, run in terminal: `huggingface-cli login`
        
        ### 🛠️ Methods:
        - **💨 API**: Fast, reliable, works without login (100 rows max)
        - **🐼 Pandas**: Full dataset access, requires login for gated datasets
        - **🤗 Datasets**: Good for streaming large datasets
        - **🧊 Polars/Dask**: Alternative fast data processing
        - **🥐 Croissant**: Metadata-aware loading
        """)
    
    with gr.Tabs():
        for key in DATASET_CONFIG.keys():
            create_dataset_tab(key)

if __name__ == "__main__":
    demo.launch(debug=True)