app.py

import gradio as gr
import time
import pandas as pd
import asyncio
from uuid import uuid4
from gradio_client import Client, handle_file

# Sample questions for examples 
SAMPLE_QUESTIONS = {
    "Deforestation Analysis": [
        "What are the main deforestation hotspots in Ecuador?",
        "Show me deforestation trends in the uploaded area",
        "What commodities are driving deforestation in Guatemala?"
    ],
    "EUDR Compliance": [
        "What are the key EUDR requirements for coffee imports?",
        "How do I prove due diligence for my supply chain?",
        "What documentation is needed for EUDR compliance?"
    ],
    "Risk Assessment": [
        "What is the deforestation risk level in this region?",
        "How do I assess supply chain risks?",
        "What are the compliance deadlines?"
    ]
}

def format_whisp_statistics(df):
    """Format WhispAPI statistics into a standardized, readable text"""
    try:
        # Extract required indicators from API response
        country = df['Country'].iloc[0]
        admin_level = df['Admin_Level_1'].iloc[0]
        area = round(float(df['Area'].iloc[0]), 2) if 'Area' in df.columns else "Not available"

        risk_level = df['risk_level'].iloc[0] if 'risk_level' in df.columns else "Not available"
        risk_pcrop = df['risk_pcrop'].iloc[0] if 'risk_pcrop' in df.columns else "Not available"
        risk_acrop = df['risk_acrop'].iloc[0] if 'risk_acrop' in df.columns else "Not available"
        risk_timber = df['risk_timber'].iloc[0] if 'risk_timber' in df.columns else "Not available"

        # TMF_def_after_2020 is a risk category, not hectares
        def_after_2020 = df['TMF_def_after_2020'].iloc[0] if 'TMF_def_after_2020' in df.columns else "Not available"

        # Format the text output
        output = f"""📊 Analysis Results for Your Plot

Plot Information:
- Country: {country}
- Administrative Region: {admin_level}
- Total Area: {area} hectares

Risk Assessment:
- Overall Risk Level: {risk_level}
- Permanent Crop Risk: {risk_pcrop}
- Annual Crop Risk: {risk_acrop}
- Timber Risk: {risk_timber}

Deforestation Analysis:
- Deforestation risk after 2020: {def_after_2020}
"""
        return output
    except Exception as e:
        return f"Error formatting statistics: {str(e)}"

# def format_whisp_statistics(df):
#     """Format WhispAPI statistics into a standardized, readable text"""
#     try:
#         # Extract required indicators from API response
#         country = df['Country'].iloc[0]
#         admin_level = df['Admin_Level_1'].iloc[0]
#         area = round(df['Area'].iloc[0], 2)
#         risk_level = df['risk_level'].iloc[0] if 'risk_level' in df.columns else "Not available"
#         risk_pcrop = df['risk_pcrop'].iloc[0] if 'risk_pcrop' in df.columns else "Not available"
#         risk_acrop = df['risk_acrop'].iloc[0] if 'risk_acrop' in df.columns else "Not available"
#         risk_timber = df['risk_timber'].iloc[0] if 'risk_timber' in df.columns else "Not available"
#         deforestation_after_2020 = df['TMF_def_after_2020'].iloc[0] if 'TMF_def_after_2020' in df.columns else 0
        
#         # Format the text output
#         output = f"""📊 Analysis Results for Your Plot

# Plot Information:
# - Country: {country}
# - Administrative Region: {admin_level}
# - Total Area: {area} hectares

# Risk Assessment:
# - Overall Risk Level: {risk_level}
# - Permanent Crop Risk: {risk_pcrop}
# - Annual Crop Risk: {risk_acrop}
# - Timber Risk: {risk_timber}

# Deforestation Analysis:
# - Deforestation after 2020: {round(deforestation_after_2020, 2)} hectares
# """
#         return output
#     except Exception as e:
#         return f"Error formatting statistics: {str(e)}"

def handle_geojson_upload(file):
    """Handle GeoJSON file upload and call WHISP API"""
    if file is not None:
        try:
            # Initialize WHISP API client
            client = Client("https://giz-chatfed-whisp.hf.space/")
            
            # Call the API with the uploaded file
            result = client.predict(
                file=handle_file(file.name),
                api_name="/get_statistics"
            )
            
            # Convert result to DataFrame
            df = pd.DataFrame(result['data'], columns=result['headers'])
            
            # Format statistics into readable text
            formatted_stats = format_whisp_statistics(df)
            
            return (
                formatted_stats,  # Keep formatted statistics for chat
                gr.update(visible=True),  # Keep status visible
                gr.update(visible=False)  # Always hide results table
            )
            
        except Exception as e:
            error_msg = f"❌ Error processing GeoJSON file: {str(e)}"
            return (
                error_msg,
                gr.update(visible=True),  # upload_status
                gr.update(visible=False)  # results_table
            )
    else:
        return (
            "",
            gr.update(visible=False),  # upload_status
            gr.update(visible=False)   # results_table
        )

def retrieve_paragraphs(query):
    """Connect to retriever and retrieve paragraphs"""

    try:        
        # Call the API with the uploaded file
        client = Client("https://giz-eudr-retriever.hf.space/")
        result = client.predict(
                query=query,
                reports_filter="",
                sources_filter="",
                subtype_filter="",
                year_filter="",
                api_name="/retrieve"
        )    

        return (
            results,  
            gr.update(visible=True),  # Keep status visible
            gr.update(visible=False)  # Always hide results table
        )

    except Exception as e:
        error_msg=(f"Error retrieving paragraphs: {str(e)}")
        return (
                    error_msg,
                    gr.update(visible=True),  # upload_status
                    gr.update(visible=False)  # results_table
                )
    else:
        return (
            "",
            gr.update(visible=False),  # upload_status
            gr.update(visible=False)   # results_table
        )

def start_chat(query, history):
    """Start a new chat interaction"""
    history = history + [(query, None)]
    return gr.update(interactive=False), gr.update(selected=1), history

def finish_chat():
    """Finish chat and reset input"""
    return gr.update(interactive=True, value="")

async def chat_response(query, history, method, country, uploaded_file):

    """Generate chat response based on method and inputs"""
    
    # Validate inputs based on method
    if method == "Upload GeoJSON":
        if uploaded_file is None:
            warning_message = "⚠️ **No GeoJSON file uploaded.** Please upload a GeoJSON file first."
            history[-1] = (query, warning_message)
            yield history, ""
            return
    else:  # "Talk to Reports"
        if not country:
            warning_message = "⚠️ **No country selected.** Please select a country to analyze reports."
            history[-1] = (query, warning_message)
            yield history, ""
            return
    
    # Get the formatted statistics if a file was just uploaded
    if method == "Upload GeoJSON" and uploaded_file:
        try:
            stats_result = handle_geojson_upload(uploaded_file)
            formatted_stats = stats_result[0]  # Get the formatted statistics
            response = formatted_stats
        except Exception as e:
            response = f"Error processing file: {str(e)}"
    
    # Talk to report
    else: 
        try: 
            response = f"Based on EUDR reports for {country}, I can help you understand deforestation patterns and compliance requirements. Your question: '{query}' is being analyzed against our {country} database."
            
            # Retrieve info
            retrieved_info = retrieve_paragraphs(query)[0]
            response = retrieved_info

        except Exception as e:
            response = f"Error retrieving information: {str(e)}"


    # else:
    #     # Default response for other queries
    #     if method == "Upload GeoJSON":
    #         response = f"Based on your uploaded GeoJSON file, I can help you analyze the deforestation patterns and EUDR compliance aspects in your area of interest. Your question: '{query}' is being processed against the geographic data you provided."
    #     else:
    #         response = f"Based on EUDR reports for {country}, I can help you understand deforestation patterns and compliance requirements. Your question: '{query}' is being analyzed against our {country} database."
    
    # Simulate streaming response
    words = response.split()
    for word in words:
        history[-1] = (query, " ".join(words[:words.index(word)+1]))
        yield history, "**Sources:** Sample source documents would appear here..."
        await asyncio.sleep(0.05)

def toggle_search_method(method):
    """Toggle between GeoJSON upload and country selection"""
    if method == "Upload GeoJSON":
        return (
            gr.update(visible=True),   # geojson_section
            gr.update(visible=False),  # reports_section
            gr.update(value=None),     # dropdown_country
        )
    else:  # "Talk to Reports"
        return (
            gr.update(visible=False),  # geojson_section 
            gr.update(visible=True),   # reports_section
            gr.update(),               # dropdown_country
        )

def change_sample_questions(key):
    """Update visible examples based on selected category"""
    keys = list(SAMPLE_QUESTIONS.keys())
    index = keys.index(key)
    visible_bools = [False] * len(keys)
    visible_bools[index] = True
    return [gr.update(visible=visible_bools[i]) for i in range(len(keys))]

# Set up Gradio Theme
theme = gr.themes.Base(
    primary_hue="green",
    secondary_hue="blue",
    font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"],
    text_size=gr.themes.utils.sizes.text_sm,
)

# Custom CSS for DataFrame styling
custom_css = """
/* DataFrame text sizing - Modify these values to change text size */
.dataframe table {
    font-size: 12px !important;  /* Change this value (e.g., 10px, 14px, 16px) */
}

.dataframe th {
    font-size: 13px !important;  /* Header text size */
    font-weight: 600 !important;
}

.dataframe td {
    font-size: 12px !important;  /* Cell text size */
    padding: 8px !important;     /* Cell padding */
}

/* Alternative size classes - change elem_classes="dataframe-small" in DataFrame component */
.dataframe-small table { font-size: 10px !important; }
.dataframe-small th { font-size: 11px !important; }
.dataframe-small td { font-size: 10px !important; }

.dataframe-medium table { font-size: 14px !important; }
.dataframe-medium th { font-size: 15px !important; }
.dataframe-medium td { font-size: 14px !important; }

.dataframe-large table { font-size: 16px !important; }
.dataframe-large th { font-size: 17px !important; }
.dataframe-large td { font-size: 16px !important; }
"""

init_prompt = """
Hello, I am EUDR Q&A, an AI-powered conversational assistant designed to help you understand EU Deforestation Regulation compliance and analysis. I will answer your questions by using **EUDR reports and uploaded GeoJSON files**.

💡 **How to use (tabs on right)**
- **Data Sources**: Choose to either upload a GeoJSON file for analysis or talk to EUDR reports filtered by country.
- **Examples**: Select from curated example questions across different categories.
- **Sources**: View the content sources used to generate answers for fact-checking.

⚠️ For limitations and data collection information, please check the **Disclaimer** tab.
"""

with gr.Blocks(title="EUDR Q&A", theme=theme, css=custom_css) as demo:
    
    # Main Chat Interface
    with gr.Tab("EUDR Q&A"):
        with gr.Row():
            # Left column - Chat interface (2/3 width)
            with gr.Column(scale=2):
                chatbot = gr.Chatbot(
                    value=[(None, init_prompt)],
                    show_copy_button=True,
                    show_label=False,
                    layout="panel",
                    avatar_images=(None, "🌳"),
                    height=500
                )
                
                # Feedback UI
                with gr.Column():
                    with gr.Row(visible=False) as feedback_row:
                        gr.Markdown("Was this response helpful?")
                        with gr.Row():
                            okay_btn = gr.Button("👍 Okay", size="sm")
                            not_okay_btn = gr.Button("👎 Not to expectations", size="sm")
                    feedback_thanks = gr.Markdown("Thanks for the feedback!", visible=False)
                
                # Input textbox
                with gr.Row():
                    textbox = gr.Textbox(
                        placeholder="Ask me anything about EUDR compliance or upload your GeoJSON for analysis!",
                        show_label=False,
                        scale=7,
                        lines=1,
                        interactive=True
                    )
            
            # Right column - Controls and tabs (1/3 width)
            with gr.Column(scale=1, variant="panel"):
                with gr.Tabs() as tabs:
                    
                    # Data Sources Tab
                    with gr.Tab("Data Sources", id=2):
                        search_method = gr.Radio(
                            choices=["Upload GeoJSON", "Talk to Reports"],
                            label="Choose data source",
                            info="Upload a GeoJSON file for analysis or select country-specific EUDR reports",
                            value="Upload GeoJSON",
                        )
                        
                        # GeoJSON Upload Section
                        with gr.Group(visible=True) as geojson_section:
                            uploaded_file = gr.File(
                                label="Upload GeoJSON File",
                                file_types=[".geojson", ".json"],
                                file_count="single"
                            )
                            upload_status = gr.Markdown("", visible=False)
                            
                            # Results table for WHISP API response
                            results_table = gr.DataFrame(
                                label="Analysis Results",
                                visible=False,
                                interactive=False,
                                wrap=True,
                                elem_classes="dataframe"
                            )
                        
                        # Talk to Reports Section
                        with gr.Group(visible=False) as reports_section:
                            dropdown_country = gr.Dropdown(
                                ["Ecuador", "Guatemala"],
                                label="Select Country",
                                value=None,
                                interactive=True,
                            )
                    
                    # Examples Tab
                    with gr.Tab("Examples", id=0):
                        examples_hidden = gr.Textbox(visible=False)
                        
                        first_key = list(SAMPLE_QUESTIONS.keys())[0]
                        dropdown_samples = gr.Dropdown(
                            SAMPLE_QUESTIONS.keys(),
                            value=first_key,
                            interactive=True,
                            show_label=True,
                            label="Select a category of sample questions"
                        )
                        
                        # Create example sections
                        sample_groups = []
                        for i, (key, questions) in enumerate(SAMPLE_QUESTIONS.items()):
                            examples_visible = True if i == 0 else False
                            with gr.Row(visible=examples_visible) as group_examples:
                                gr.Examples(
                                    questions,
                                    [examples_hidden],
                                    examples_per_page=8,
                                    run_on_click=False,
                                )
                            sample_groups.append(group_examples)
                    
                    # Sources Tab
                    with gr.Tab("Sources", id=1):
                        sources_textbox = gr.HTML(
                            show_label=False,
                            value="Source documents will appear here after you ask a question..."
                        )
    
    # Guidelines Tab
    with gr.Tab("Guidelines"):
        gr.Markdown("""
        #### Welcome to EUDR Q&A! 
        
        This AI-powered assistant helps you understand EU Deforestation Regulation compliance and analyze geographic data.
        
        ## 💬 How to Ask Effective Questions
        
        | ❌ Less Effective | ✅ More Effective |
        |------------------|-------------------|
        | "What is deforestation?" | "What are the main deforestation hotspots in Ecuador?" |
        | "Tell me about compliance" | "What EUDR requirements apply to coffee imports from Guatemala?" |
        | "Show me data" | "What is the deforestation rate in the uploaded region?" |
        
        ## 🔍 Using Data Sources
        
        **Upload GeoJSON:** Upload your geographic data files for automatic analysis via WHISP API  
        **Talk to Reports:** Select Ecuador or Guatemala for country-specific EUDR analysis
        
        ## ⭐ Best Practices
        
        - Be specific about regions, commodities, or time periods
        - Ask one question at a time for clearer answers
        - Use follow-up questions to explore topics deeper
        - Provide context when possible
        """)
    
    # About Tab
    with gr.Tab("About"):
        gr.Markdown("""
        ## About EUDR Q&A
        
        The **EU Deforestation Regulation (EUDR)** requires companies to ensure that specific commodities 
        placed on the EU market are deforestation-free and legally produced.
        
        This AI-powered tool helps stakeholders:
        - Understand EUDR compliance requirements
        - Analyze geographic deforestation data using WHISP API
        - Assess supply chain risks
        - Navigate complex regulatory landscapes
        
        **Developed by GIZ** to enhance accessibility and understanding of EUDR requirements 
        through advanced AI and geographic data processing capabilities.
        
        ### Key Features:
        - Automatic analysis of uploaded GeoJSON files via WHISP API
        - Country-specific EUDR compliance guidance
        - Real-time question answering with source citations
        - User-friendly interface for complex regulatory information
        """)
    
    # Disclaimer Tab
    with gr.Tab("Disclaimer"):
        gr.Markdown("""
        ## Important Disclaimers
        
        ⚠️ **Scope & Limitations:**
        - This tool is designed for EUDR compliance assistance and geographic data analysis
        - Responses should not be considered official legal or compliance advice
        - Always consult qualified professionals for official compliance decisions
        
        ⚠️ **Data & Privacy:**
        - Uploaded GeoJSON files are processed via external WHISP API for analysis
        - We collect usage statistics to improve the tool
        - Files are processed temporarily and not permanently stored
        
        ⚠️ **AI Limitations:**
        - Responses are AI-generated and may contain inaccuracies
        - The tool is a prototype under continuous development
        - Always verify important information with authoritative sources
        
        **Data Collection:** We collect questions, answers, feedback, and anonymized usage statistics 
        to improve tool performance based on legitimate interest in service enhancement.
        
        By using this tool, you acknowledge these limitations and agree to use responses responsibly.
        """)
    
    # Event Handlers
    
    # Toggle search method
    search_method.change(
        fn=toggle_search_method,
        inputs=[search_method],
        outputs=[geojson_section, reports_section, dropdown_country]
    )
    
    # File upload - automatically process when file is uploaded
    uploaded_file.change(
        fn=handle_geojson_upload,
        inputs=[uploaded_file],
        outputs=[upload_status, upload_status, results_table]
    )
    
    # Chat functionality
    textbox.submit(
        start_chat,
        [textbox, chatbot],
        [textbox, tabs, chatbot],
        queue=False
    ).then(
        chat_response,
        [textbox, chatbot, search_method, dropdown_country, uploaded_file],
        [chatbot, sources_textbox]
    ).then(
        lambda: gr.update(visible=True),
        outputs=[feedback_row]
    ).then(
        finish_chat,
        outputs=[textbox]
    )
    
    # Examples functionality
    examples_hidden.change(
        start_chat,
        [examples_hidden, chatbot],
        [textbox, tabs, chatbot],
        queue=False
    ).then(
        chat_response,
        [examples_hidden, chatbot, search_method, dropdown_country, uploaded_file],
        [chatbot, sources_textbox]
    ).then(
        lambda: gr.update(visible=True),
        outputs=[feedback_row]
    ).then(
        finish_chat,
        outputs=[textbox]
    )
    
    # Sample questions dropdown
    dropdown_samples.change(
        change_sample_questions,
        [dropdown_samples],
        sample_groups
    )
    
    # Feedback buttons
    okay_btn.click(
        lambda: (gr.update(visible=False), gr.update(visible=True)),
        outputs=[feedback_row, feedback_thanks]
    )
    
    not_okay_btn.click(
        lambda: (gr.update(visible=False), gr.update(visible=True)),
        outputs=[feedback_row, feedback_thanks]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()