app.py

import streamlit as st
import os
import json
import pandas as pd
import random
from os.path import join
from datetime import datetime
from src import (
    preprocess_and_load_df,
    get_from_user,
    ask_question,
)
from dotenv import load_dotenv
from langchain_groq import ChatGroq
from langchain_google_genai import ChatGoogleGenerativeAI
from streamlit_feedback import streamlit_feedback
from huggingface_hub import HfApi
from datasets import load_dataset, get_dataset_config_info, Dataset
from PIL import Image
import time
import uuid

# Page config with beautiful theme
st.set_page_config(
    page_title="VayuChat - AI Air Quality Assistant",
    page_icon="V",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for beautiful styling
st.markdown("""
<style>
/* Clean app background */
.stApp {
    background-color: #ffffff;
    color: #212529;
    font-family: 'Segoe UI', sans-serif;
}

/* Reduce main container padding */
.main .block-container {
    padding-top: 0.5rem;
    padding-bottom: 3rem;
    max-width: 100%;
}

/* Remove excessive spacing */
.element-container {
    margin-bottom: 0.5rem !important;
}

/* Fix sidebar spacing */
[data-testid="stSidebar"] .element-container {
    margin-bottom: 0.25rem !important;
}

/* Sidebar */
[data-testid="stSidebar"] {
    background-color: #f8f9fa;
    border-right: 1px solid #dee2e6;
    padding: 1rem;
}

/* Main title */
.main-title {
    text-align: center;
    color: #343a40;
    font-size: 2.5rem;
    font-weight: 700;
    margin-bottom: 0.5rem;
}

/* Subtitle */
.subtitle {
    text-align: center;
    color: #6c757d;
    font-size: 1.1rem;
    margin-bottom: 1.5rem;
}

/* Instructions */
.instructions {
    background-color: #f1f3f5;
    border-left: 4px solid #0d6efd;
    padding: 1rem;
    margin-bottom: 1.5rem;
    border-radius: 6px;
    color: #495057;
    text-align: left;
}

/* Quick prompt buttons */
.quick-prompt-container {
    display: flex;
    flex-wrap: wrap;
    gap: 8px;
    margin-bottom: 1.5rem;
    padding: 1rem;
    background-color: #f8f9fa;
    border-radius: 10px;
    border: 1px solid #dee2e6;
}

.quick-prompt-btn {
    background-color: #0d6efd;
    color: white;
    border: none;
    padding: 8px 16px;
    border-radius: 20px;
    font-size: 0.9rem;
    cursor: pointer;
    transition: all 0.2s ease;
    white-space: nowrap;
}

.quick-prompt-btn:hover {
    background-color: #0b5ed7;
    transform: translateY(-2px);
}

/* User message styling */
.user-message {
    background: #3b82f6;
    color: white;
    padding: 0.75rem 1rem;
    border-radius: 12px;
    max-width: 70%;
}

.user-info {
    font-size: 0.875rem;
    opacity: 0.9;
    margin-bottom: 3px;
}

/* Assistant message styling */
.assistant-message {
    background: #f1f5f9;
    color: #334155;
    padding: 0.75rem 1rem;
    border-radius: 12px;
    max-width: 70%;
}

.assistant-info {
    font-size: 0.875rem;
    color: #6b7280;
    margin-bottom: 5px;
}

/* Processing indicator */
.processing-indicator {
    background: linear-gradient(135deg, #a8edea 0%, #fed6e3 100%);
    color: #333;
    padding: 1rem 1.5rem;
    border-radius: 12px;
    margin: 1rem 0;
    margin-left: 0;
    margin-right: auto;
    max-width: 70%;
    box-shadow: 0 2px 10px rgba(0,0,0,0.1);
    animation: pulse 2s infinite;
}

@keyframes pulse {
    0% { opacity: 1; }
    50% { opacity: 0.7; }
    100% { opacity: 1; }
}

/* Feedback box */
.feedback-section {
    background-color: #f8f9fa;
    border: 1px solid #dee2e6;
    padding: 1rem;
    border-radius: 8px;
    margin: 1rem 0;
}

/* Success and error messages */
.success-message {
    background-color: #d1e7dd;
    color: #0f5132;
    padding: 1rem;
    border-radius: 6px;
    border: 1px solid #badbcc;
}

.error-message {
    background-color: #f8d7da;
    color: #842029;
    padding: 1rem;
    border-radius: 6px;
    border: 1px solid #f5c2c7;
}

/* Chat input styling like mockup */
.stChatInput {
    border-radius: 8px;
    border: 1px solid #d1d5db;
    background: #ffffff;
    padding: 0.75rem 1rem;
    font-size: 1rem;
}

.stChatInput:focus {
    border-color: #3b82f6;
    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1);
}

/* Button */
.stButton > button {
    background-color: #0d6efd;
    color: white;
    border-radius: 6px;
    padding: 0.5rem 1.25rem;
    border: none;
    font-weight: 600;
    transition: background-color 0.2s ease;
}

.stButton > button:hover {
    background-color: #0b5ed7;
}

/* Sidebar button styling - smaller, left-aligned */
[data-testid="stSidebar"] .stButton > button {
    background-color: #f8fafc;
    color: #475569;
    border: 1px solid #e2e8f0;
    padding: 0.375rem 0.75rem;
    font-size: 0.65rem;
    font-weight: normal;
    text-align: left;
    white-space: normal;
    height: auto;
    line-height: 1.2;
    transition: all 0.2s ease;
    cursor: pointer;
    margin-bottom: 0.25rem;
    width: 100%;
    display: flex;
    justify-content: flex-start;
}

[data-testid="stSidebar"] .stButton > button:hover {
    background-color: #e0f2fe;
    border-color: #0ea5e9;
    color: #0c4a6e;
}

[data-testid="stSidebar"] .stButton > button:active {
    transform: translateY(0);
    box-shadow: none;
}

/* Code container styling */
.code-container {
    margin: 1rem 0;
    border: 1px solid #d1d5db;
    border-radius: 12px;
    background: white;
    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
}

.code-header {
    display: flex;
    justify-content: space-between;
    align-items: center;
    padding: 0.875rem 1.25rem;
    background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
    border-bottom: 1px solid #e2e8f0;
    cursor: pointer;
    transition: all 0.2s ease;
    border-radius: 12px 12px 0 0;
}

.code-header:hover {
    background: linear-gradient(135deg, #e2e8f0 0%, #cbd5e1 100%);
}

.code-title {
    font-size: 0.9rem;
    font-weight: 600;
    color: #1e293b;
    display: flex;
    align-items: center;
    gap: 0.5rem;
}

.code-title:before {
    content: "⚡";
    font-size: 0.8rem;
}

.toggle-text {
    font-size: 0.75rem;
    color: #64748b;
    font-weight: 500;
}

.code-block {
    background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
    color: #e2e8f0;
    padding: 1.5rem;
    font-family: 'SF Mono', 'Monaco', 'Menlo', 'Consolas', monospace;
    font-size: 0.875rem;
    overflow-x: auto;
    line-height: 1.6;
    border-radius: 0 0 12px 12px;
}

.answer-container {
    background: #f8fafc;
    border: 1px solid #e2e8f0;
    border-radius: 8px;
    padding: 1.5rem;
    margin: 1rem 0;
}

.answer-text {
    font-size: 1.125rem;
    color: #1e293b;
    line-height: 1.6;
    margin-bottom: 1rem;
}

.answer-highlight {
    background: #fef3c7;
    padding: 0.125rem 0.375rem;
    border-radius: 4px;
    font-weight: 600;
    color: #92400e;
}

.context-info {
    background: #f1f5f9;
    border-left: 4px solid #3b82f6;
    padding: 0.75rem 1rem;
    margin: 1rem 0;
    font-size: 0.875rem;
    color: #475569;
}

/* Hide default menu and footer */
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
header {visibility: hidden;}

/* Auto scroll */
.main-container {
    height: 70vh;
    overflow-y: auto;
}
</style>
""", unsafe_allow_html=True)

# JavaScript for interactions
st.markdown("""
<script>
function scrollToBottom() {
    setTimeout(function() {
        const mainContainer = document.querySelector('.main-container');
        if (mainContainer) {
            mainContainer.scrollTop = mainContainer.scrollHeight;
        }
        window.scrollTo(0, document.body.scrollHeight);
    }, 100);
}

function toggleCode(header) {
    const codeBlock = header.nextElementSibling;
    const toggleText = header.querySelector('.toggle-text');
    
    if (codeBlock.style.display === 'none') {
        codeBlock.style.display = 'block';
        toggleText.textContent = 'Click to collapse';
    } else {
        codeBlock.style.display = 'none';
        toggleText.textContent = 'Click to expand';
    }
}
</script>
""", unsafe_allow_html=True)

# FORCE reload environment variables
load_dotenv(override=True)

# Get API keys
Groq_Token = os.getenv("GROQ_API_KEY")
hf_token = os.getenv("HF_TOKEN")
gemini_token = os.getenv("GEMINI_TOKEN")

models = {
    "gpt-oss-20b": "openai/gpt-oss-20b",
    "gpt-oss-120b": "openai/gpt-oss-120b",
    "llama3.1": "llama-3.1-8b-instant",
    "llama3.3": "llama-3.3-70b-versatile",
    "deepseek-R1": "deepseek-r1-distill-llama-70b",
    "llama4 maverik":"meta-llama/llama-4-maverick-17b-128e-instruct",
    "llama4 scout":"meta-llama/llama-4-scout-17b-16e-instruct",
    "gemini-pro": "gemini-1.5-pro"
}

self_path = os.path.dirname(os.path.abspath(__file__))

# Initialize session ID for this session
if "session_id" not in st.session_state:
    st.session_state.session_id = str(uuid.uuid4())

def upload_feedback(feedback, error, output, last_prompt, code, status):
    """Enhanced feedback upload function with better logging and error handling"""
    try:
        if not hf_token or hf_token.strip() == "":
            st.warning("Cannot upload feedback - HF_TOKEN not available")
            return False

        # Create comprehensive feedback data
        feedback_data = {
            "timestamp": datetime.now().isoformat(),
            "session_id": st.session_state.session_id,
            "feedback_score": feedback.get("score", ""),
            "feedback_comment": feedback.get("text", ""),
            "user_prompt": last_prompt,
            "ai_output": str(output),
            "generated_code": code or "",
            "error_message": error or "",
            "is_image_output": status.get("is_image", False),
            "success": not bool(error)
        }

        # Create unique folder name with timestamp
        timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S")
        random_id = str(uuid.uuid4())[:8]
        folder_name = f"feedback_{timestamp_str}_{random_id}"
        
        # Create markdown feedback file
        markdown_content = f"""# VayuChat Feedback Report

## Session Information
- **Timestamp**: {feedback_data['timestamp']}
- **Session ID**: {feedback_data['session_id']}

## User Interaction
**Prompt**: {feedback_data['user_prompt']}

## AI Response
**Output**: {feedback_data['ai_output']}

## Generated Code
```python
{feedback_data['generated_code']}
```

## Technical Details
- **Error Message**: {feedback_data['error_message']}
- **Is Image Output**: {feedback_data['is_image_output']}
- **Success**: {feedback_data['success']}

## User Feedback
- **Score**: {feedback_data['feedback_score']}
- **Comments**: {feedback_data['feedback_comment']}
"""

        # Save markdown file locally
        markdown_filename = f"{folder_name}.md"
        markdown_local_path = f"/tmp/{markdown_filename}"
        
        with open(markdown_local_path, "w", encoding="utf-8") as f:
            f.write(markdown_content)

        # Upload to Hugging Face
        api = HfApi(token=hf_token)
        
        # Upload markdown feedback
        api.upload_file(
            path_or_fileobj=markdown_local_path,
            path_in_repo=f"data/{markdown_filename}",
            repo_id="SustainabilityLabIITGN/VayuChat_Feedback",
            repo_type="dataset",
        )
        
        # Upload image if it exists and is an image output
        if status.get("is_image", False) and isinstance(output, str) and os.path.exists(output):
            try:
                image_filename = f"{folder_name}_plot.png"
                api.upload_file(
                    path_or_fileobj=output,
                    path_in_repo=f"data/{image_filename}",
                    repo_id="SustainabilityLabIITGN/VayuChat_Feedback",
                    repo_type="dataset",
                )
            except Exception as img_error:
                print(f"Error uploading image: {img_error}")
        
        # Clean up local files
        if os.path.exists(markdown_local_path):
            os.remove(markdown_local_path)
        
        st.success("Feedback uploaded successfully!")
        return True
        
    except Exception as e:
        st.error(f"Error uploading feedback: {e}")
        print(f"Feedback upload error: {e}")
        return False

# Filter available models
available_models = []
model_names = list(models.keys())
groq_models = []
gemini_models = []
for model_name in model_names:
    if "gemini" not in model_name:
        groq_models.append(model_name)
    else:
        gemini_models.append(model_name)
if Groq_Token and Groq_Token.strip():
    available_models.extend(groq_models)
if gemini_token and gemini_token.strip():
    available_models.extend(gemini_models)

if not available_models:
    st.error("No API keys available! Please set up your API keys in the .env file")
    st.stop()

# Set DeepSeek-R1 as default if available
default_index = 0
if "deepseek-R1" in available_models:
    default_index = available_models.index("deepseek-R1")

# Header with logo, title and model selector
header_col1, header_col2 = st.columns([2, 1])

with header_col1:
    st.markdown("""
    <div style='display: flex; align-items: center; gap: 0.75rem; margin-bottom: 0.25rem;'>
        <div style='width: 32px; height: 32px; background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%); border-radius: 8px; display: flex; align-items: center; justify-content: center; color: white; font-weight: bold; font-size: 1rem; box-shadow: 0 2px 4px rgba(59, 130, 246, 0.2);'>V</div>
        <div>
            <h1 style='margin: 0; font-size: 1.25rem; font-weight: 600; color: #1e293b;'>
                VayuChat 
                <span style='font-size: 0.875rem; font-weight: 400; color: #6b7280; margin-left: 0.5rem;'>• Environmental Data Analysis</span>
            </h1>
        </div>
    </div>
    """, unsafe_allow_html=True)

with header_col2:
    st.markdown("<p style='margin: 0 0 0.25rem 0; font-size: 0.75rem; color: #6b7280;'>AI Model:</p>", unsafe_allow_html=True)
    model_name = st.selectbox(
        "Model:",
        available_models,
        index=default_index,
        help="Choose your AI model",
        label_visibility="collapsed"
    )

st.markdown("<hr style='margin: 0.25rem 0; border: none; border-top: 1px solid #e2e8f0;'>", unsafe_allow_html=True)


# Load data with caching for better performance
@st.cache_data
def load_data():
    return preprocess_and_load_df(join(self_path, "Data.csv"))

try:
    df = load_data()
    # Data loaded silently - no success message needed
except Exception as e:
    st.error(f"Error loading data: {e}")
    st.stop()

inference_server = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
image_path = "IITGN_Logo.png"

# Clean sidebar  
with st.sidebar:
    # Quick Queries Section - moved to top
    st.markdown("### Quick Queries")
    
    # Load quick prompts with caching
    @st.cache_data
    def load_questions():
        questions = []
        questions_file = join(self_path, "questions.txt")
        if os.path.exists(questions_file):
            try:
                with open(questions_file, 'r', encoding='utf-8') as f:
                    content = f.read()
                    questions = [q.strip() for q in content.split("\n") if q.strip()]
            except Exception as e:
                questions = []
        return questions
    
    questions = load_questions()
    
    # Add default prompts if file doesn't exist or is empty
    if not questions:
        questions = [
            "Which month had highest pollution?",
            "Which city has worst air quality?", 
            "Show annual PM2.5 average",
            "Plot monthly average PM2.5 for 2023",
            "List all cities by pollution level",
            "Compare winter vs summer pollution",
            "Show seasonal pollution patterns",
            "Which areas exceed WHO guidelines?",
            "What are peak pollution hours?",
            "Show PM10 vs PM2.5 comparison",
            "Which station records highest variability in PM2.5?",
            "Calculate pollution improvement rate year-over-year by city",
            "Identify cities with PM2.5 levels consistently above 50 μg/m³ for >6 months",
            "Find correlation between PM2.5 and PM10 across different seasons and cities",
            "Compare weekday vs weekend levels",
            "Plot yearly trend analysis",
            "Show pollution distribution by city",
            "Create correlation plot between pollutants"
        ]
    
    # Quick query buttons in sidebar
    selected_prompt = None
    
    
    for i, question in enumerate(questions[:15]):  # Show more questions
        # Simple left-aligned buttons without icons for cleaner look
        if st.button(question, key=f"sidebar_prompt_{i}", use_container_width=True, help=f"Click to analyze: {question}"):
            if question != st.session_state.get("last_selected_prompt"):
                selected_prompt = question
                st.session_state.last_selected_prompt = question
    
    st.markdown("---")
    
    
    # Clear Chat Button
    if st.button("Clear Chat", use_container_width=True):
        st.session_state.responses = []
        st.session_state.processing = False
        st.session_state.session_id = str(uuid.uuid4())
        try:
            st.rerun()
        except AttributeError:
            st.experimental_rerun()

# Initialize session state first
if "responses" not in st.session_state:
    st.session_state.responses = []
if "processing" not in st.session_state:
    st.session_state.processing = False
if "session_id" not in st.session_state:
    st.session_state.session_id = str(uuid.uuid4())




def show_custom_response(response):
    """Custom response display function with improved styling"""
    role = response.get("role", "assistant")
    content = response.get("content", "")
    
    if role == "user":
        # User message with right alignment - reduced margins
        st.markdown(f"""
        <div style='display: flex; justify-content: flex-end; margin: 1rem 0;'>
            <div class='user-message'>
                {content}
            </div>
        </div>
        """, unsafe_allow_html=True)
    elif role == "assistant":
        # Check if content is an image filename - don't display the filename text
        is_image_path = isinstance(content, str) and any(ext in content for ext in ['.png', '.jpg', '.jpeg'])
        
        # Check if content is a pandas DataFrame
        import pandas as pd
        is_dataframe = isinstance(content, pd.DataFrame)
        
        # Assistant message with left alignment - reduced margins
        if not is_image_path and not is_dataframe:
            st.markdown(f"""
            <div style='display: flex; justify-content: flex-start; margin: 1rem 0;'>
                <div class='assistant-message'>
                    <div class='assistant-info'>VayuChat</div>
                    {content if isinstance(content, str) else str(content)}
                </div>
            </div>
            """, unsafe_allow_html=True)
        elif is_dataframe:
            # Display DataFrame with nice formatting
            st.markdown("""
            <div style='display: flex; justify-content: flex-start; margin: 1rem 0;'>
                <div class='assistant-message'>
                    <div class='assistant-info'>VayuChat</div>
                    Here are the results:
                </div>
            </div>
            """, unsafe_allow_html=True)
            
            # Add context info for dataframes
            st.markdown("""
            <div class='context-info'>
                💡 This table is interactive - click column headers to sort, or scroll to view all data.
            </div>
            """, unsafe_allow_html=True)
            
            st.dataframe(content, use_container_width=True)
        
        # Show generated code with Streamlit expander
        if response.get("gen_code"):
            with st.expander("📋 View Generated Code", expanded=False):
                st.code(response["gen_code"], language="python")
        
        # Try to display image if content is a file path
        try:
            if isinstance(content, str) and (content.endswith('.png') or content.endswith('.jpg')):
                if os.path.exists(content):
                    # Display image without showing filename
                    st.image(content, use_column_width=True)
                    return {"is_image": True}
            # Also handle case where content shows filename but we want to show image
            elif isinstance(content, str) and any(ext in content for ext in ['.png', '.jpg']):
                # Extract potential filename from content
                import re
                filename_match = re.search(r'([^/\\]+\.(?:png|jpg|jpeg))', content)
                if filename_match:
                    filename = filename_match.group(1)
                    if os.path.exists(filename):
                        st.image(filename, use_column_width=True)
                        return {"is_image": True}
        except:
            pass
            
        return {"is_image": False}

def show_processing_indicator(model_name, question):
    """Show processing indicator with clear question and status"""
    st.markdown(f"""
    <div style='display: flex; justify-content: flex-start; margin: 1rem 0;'>
        <div class='processing-indicator'>
            <div style='font-size: 0.875rem; color: #6b7280; margin-bottom: 8px;'>🤖 VayuChat • Processing with {model_name}</div>
            <div style='background: rgba(255,255,255,0.9); padding: 0.75rem; border-radius: 8px; margin-bottom: 8px; border-left: 3px solid #3b82f6;'>
                <strong style='color: #1e293b;'>Your Question:</strong><br>
                <span style='color: #374151; font-size: 0.95rem;'>{question}</span>
            </div>
            <div style='display: flex; align-items: center; gap: 8px;'>
                <div style='width: 16px; height: 16px; border: 2px solid #3b82f6; border-top: 2px solid transparent; border-radius: 50%; animation: spin 1s linear infinite;'></div>
                <span style='color: #374151; font-style: italic;'>Analyzing data and generating response...</span>
            </div>
        </div>
    </div>
    <style>
    @keyframes spin {{
        0% {{ transform: rotate(0deg); }}
        100% {{ transform: rotate(360deg); }}
    }}
    </style>
    """, unsafe_allow_html=True)

# Main chat container with mockup styling
st.markdown("""
<div style='background: white; min-height: 60vh; padding: 1.5rem;'>
""", unsafe_allow_html=True)

chat_container = st.container()

with chat_container:
    # Display chat history
    for response_id, response in enumerate(st.session_state.responses):
        status = show_custom_response(response)
        
        # Show feedback section for assistant responses
        if response["role"] == "assistant":
            feedback_key = f"feedback_{int(response_id/2)}"
            error = response.get("error", "")
            output = response.get("content", "")
            last_prompt = response.get("last_prompt", "")
            code = response.get("gen_code", "")

            if "feedback" in st.session_state.responses[response_id]:
                feedback_data = st.session_state.responses[response_id]["feedback"]
                st.markdown(f"""
                <div class='feedback-section'>
                    <strong>Your Feedback:</strong> {feedback_data.get('score', '')} 
                    {f"- {feedback_data.get('text', '')}" if feedback_data.get('text') else ""}
                </div>
                """, unsafe_allow_html=True)
            else:
                # Beautiful feedback section
                st.markdown("---")
                st.markdown("**Rate this response:**")
                
                # More detailed feedback options
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    excellent = st.button("🎯 Excellent", key=f"{feedback_key}_excellent", use_container_width=True)
                with col2:
                    good = st.button("✅ Good", key=f"{feedback_key}_good", use_container_width=True)
                with col3:
                    okay = st.button("⚠️ Okay", key=f"{feedback_key}_okay", use_container_width=True)
                with col4:
                    poor = st.button("❌ Poor", key=f"{feedback_key}_poor", use_container_width=True)
                
                if excellent or good or okay or poor:
                    if excellent:
                        thumbs = "🎯 Excellent"
                    elif good:
                        thumbs = "✅ Good"
                    elif okay:
                        thumbs = "⚠️ Okay"
                    else:
                        thumbs = "❌ Poor"
                    comments = st.text_area(
                        "Tell us more (optional):", 
                        key=f"{feedback_key}_comments",
                        placeholder="What could be improved? Any suggestions?",
                        max_chars=500
                    )
                    
                    if st.button("Submit Feedback", key=f"{feedback_key}_submit"):
                        feedback = {"score": thumbs, "text": comments}
                        
                        # Upload feedback with enhanced error handling
                        if upload_feedback(feedback, error, output, last_prompt, code, status or {}):
                            st.session_state.responses[response_id]["feedback"] = feedback
                            time.sleep(1)  # Give user time to see success message
                            st.rerun()
                        else:
                            st.error("Failed to submit feedback. Please try again.")

    # Show processing indicator if processing
    if st.session_state.get("processing"):
        show_processing_indicator(
            st.session_state.get("current_model", "Unknown"), 
            st.session_state.get("current_question", "Processing...")
        )

# Chat input with better guidance
prompt = st.chat_input("💬 Ask about air quality trends, compare cities, or request visualizations...", key="main_chat")

# Handle selected prompt from quick prompts
if selected_prompt:
    prompt = selected_prompt

# Handle new queries
if prompt and not st.session_state.get("processing"):
    # Prevent duplicate processing
    if "last_prompt" in st.session_state:
        last_prompt = st.session_state["last_prompt"]
        last_model_name = st.session_state.get("last_model_name", "")
        if (prompt == last_prompt) and (model_name == last_model_name):
            prompt = None

    if prompt:
        # Add user input to chat history
        user_response = get_from_user(prompt)
        st.session_state.responses.append(user_response)
        
        # Set processing state
        st.session_state.processing = True
        st.session_state.current_model = model_name
        st.session_state.current_question = prompt
        
        # Rerun to show processing indicator
        st.rerun()

# Process the question if we're in processing state
if st.session_state.get("processing"):
    prompt = st.session_state.get("current_question")
    model_name = st.session_state.get("current_model")
    
    try:
        response = ask_question(model_name=model_name, question=prompt)
        
        if not isinstance(response, dict):
            response = {
                "role": "assistant",
                "content": "Error: Invalid response format",
                "gen_code": "",
                "ex_code": "",
                "last_prompt": prompt,
                "error": "Invalid response format"
            }
        
        response.setdefault("role", "assistant")
        response.setdefault("content", "No content generated")
        response.setdefault("gen_code", "")
        response.setdefault("ex_code", "")
        response.setdefault("last_prompt", prompt)
        response.setdefault("error", None)
        
    except Exception as e:
        response = {
            "role": "assistant",
            "content": f"Sorry, I encountered an error: {str(e)}",
            "gen_code": "",
            "ex_code": "",
            "last_prompt": prompt,
            "error": str(e)
        }

    st.session_state.responses.append(response)
    st.session_state["last_prompt"] = prompt
    st.session_state["last_model_name"] = model_name
    st.session_state.processing = False
    
    # Clear processing state
    if "current_model" in st.session_state:
        del st.session_state.current_model
    if "current_question" in st.session_state:
        del st.session_state.current_question
    
    st.rerun()

# Close chat container
st.markdown("</div>", unsafe_allow_html=True)

# Minimal auto-scroll - only scroll when processing
if st.session_state.get("processing"):
    st.markdown("<script>scrollToBottom();</script>", unsafe_allow_html=True)

# Beautiful sidebar footer
# with st.sidebar:
#     st.markdown("---")
#     st.markdown("""
#     <div class='contact-section'>
#         <h4>📄 Paper on VayuChat</h4>
#         <p>Learn more about VayuChat in our <a href='https://arxiv.org/abs/2411.12760' target='_blank'>Research Paper</a>.</p>
#     </div>
#     """, unsafe_allow_html=True)
    
    # Dataset Info Section (matching mockup)
    st.markdown("### Dataset Info")
    st.markdown("""
    <div style='background: #f1f5f9; border-radius: 8px; padding: 1rem; margin-bottom: 1rem;'>
        <h4 style='margin: 0 0 0.5rem 0; color: #1e293b; font-size: 0.9rem;'>PM2.5 Air Quality Data</h4>
        <p style='margin: 0; font-size: 0.75rem; color: #475569;'><strong>Time Range:</strong> 2022 - 2023</p>
        <p style='margin: 0; font-size: 0.75rem; color: #475569;'><strong>Locations:</strong> 300+ cities across India</p>
        <p style='margin: 0; font-size: 0.75rem; color: #475569;'><strong>Records:</strong> 100,000+ measurements</p>
    </div>
    """, unsafe_allow_html=True)

# Footer at absolute bottom
st.markdown("""
<div style='position: fixed; bottom: 0; left: 0; right: 0; background: white; border-top: 1px solid #e2e8f0; text-align: center; padding: 0.5rem; font-size: 0.7rem; color: #6b7280; z-index: 1000;'>
    © 2024 IIT Gandhinagar Sustainability Lab
</div>
""", unsafe_allow_html=True)