Update app.py

app.py

@@ -1,513 +1,513 @@
-import os
-from flask import Flask, render_template, request, jsonify
-import requests
-import pandas as pd
-from datetime import datetime
-import plotly.express as px
-import plotly.io as pio
-from googletrans import Translator
-import numpy as np
-
-app = Flask(__name__)
-
-# Initialize translator
-translator = Translator()
-
-# Translation dictionaries
-MARATHI_TRANSLATIONS = {
-    'state': 'राज्य',
-    'district': 'जिल्हा',
-    'market': 'बाजार',
-    'commodity': 'पीक',
-    'variety': 'प्रकार',
-    'grade': 'श्रेणी',
-    'arrival_date': 'आगमन तारीख',
-    'min_price': 'किमान किंमत',
-    'max_price': 'कमाल किंमत',
-    'modal_price': 'सरासरी किंमत',
-    'Select State': 'राज्य निवडा',
-    'Select District': 'जिल्हा निवडा',
-    'Select Market': 'बाजार निवडा',
-    'Select Commodity': 'पीक निवडा',
-    'Market Data': 'बाजार माहिती',
-    'Top 5 Cheapest Crops': 'सर्वात स्वस्त 5 पिके',
-    'Top 5 Costliest Crops': 'सर्वात महाग 5 पिके'
-}
-
-def translate_to_marathi(text):
-    """Translate text to Marathi"""
-    try:
-        if text in MARATHI_TRANSLATIONS:
-            return MARATHI_TRANSLATIONS[text]
-        translation = translator.translate(text, dest='mr')
-        return translation.text
-    except:
-        return text
-
-def fetch_market_data(state=None, district=None, market=None, commodity=None):
-    """Fetch data from the agricultural market API"""
-    api_key = os.getenv("data_api_key")
-    print(api_key)
-    base_url = "https://api.data.gov.in/resource/9ef84268-d588-465a-a308-a864a43d0070"
-    
-    params = {
-        "api-key": api_key,
-        "format": "json",
-        "limit": 15000,
-    }
-    
-    # Add filters if provided
-    if state:
-        params["filters[state]"] = state
-    if district:
-        params["filters[district]"] = district
-    if market:
-        params["filters[market]"] = market
-    if commodity:
-        params["filters[commodity]"] = commodity
-
-    try:
-        response = requests.get(base_url, params=params)
-        if response.status_code == 200:
-            data = response.json()
-            records = data.get("records", [])
-            df = pd.DataFrame(records)
-            return df
-        else:
-            print(f"API Error: {response.status_code}")
-            return pd.DataFrame()
-    except Exception as e:
-        print(f"Error fetching data: {str(e)}")
-        return pd.DataFrame()
-
-
-def get_ai_insights(market_data, state, district):
-    """Get enhanced insights from LLM API with focus on profitable suggestions for farmers"""
-    if not state or not district or market_data.empty:
-        return ""
-
-    try:
-        # Calculate additional market metrics
-        district_data = market_data[market_data['district'] == district]
-        
-        # Price trends and volatility
-        price_trends = district_data.groupby('commodity').agg({
-            'modal_price': ['mean', 'min', 'max', 'std']
-        }).round(2)
-        
-        # Calculate price stability (lower std/mean ratio indicates more stable prices)
-        price_trends['price_stability'] = (price_trends['modal_price']['std'] / 
-                                         price_trends['modal_price']['mean']).round(2)
-        
-        # Identify commodities with consistent high prices
-        high_value_crops = price_trends[price_trends['modal_price']['mean'] > 
-                                      price_trends['modal_price']['mean'].median()]
-        
-        # Get seasonal patterns
-        district_data['arrival_date'] = pd.to_datetime(district_data['arrival_date'])
-        district_data['month'] = district_data['arrival_date'].dt.month
-        monthly_trends = district_data.groupby(['commodity', 'month'])['modal_price'].mean().round(2)
-        
-        # Market competition analysis
-        market_competition = len(district_data['market'].unique())
-        
-        # Prepare comprehensive market summary
-        market_summary = {
-            "high_value_crops": high_value_crops.index.tolist(),
-            "price_stability": price_trends['price_stability'].to_dict(),
-            "monthly_trends": monthly_trends.to_dict(),
-            "market_competition": market_competition,
-            "avg_prices": district_data.groupby('commodity')['modal_price'].mean().round(2).to_dict(),
-            "price_ranges": {
-                crop: {
-                    'min': price_trends.loc[crop, ('modal_price', 'min')],
-                    'max': price_trends.loc[crop, ('modal_price', 'max')]
-                } for crop in price_trends.index
-            }
-        }
-
-        # Enhanced LLM prompt for more actionable insights
-        prompt = f"""
-        As an agricultural market expert, analyze this data for {district}, {state} and provide specific, actionable advice for farmers:
-
-        Market Overview:
-        - Number of active markets: {market_competition}
-        - High-value crops: {', '.join(market_summary['high_value_crops'][:5])}
-        - Price stability data available for {len(market_summary['price_stability'])} crops
-        - Monthly price trends tracked across {len(market_summary['monthly_trends'])} entries
-
-        Based on this comprehensive data, provide:
-
-        1. Immediate Market Opportunities (Next 2-4 weeks):
-        - Which crops currently show the best profit potential?
-        - Which markets are offering the best prices?
-        - Any immediate selling or holding recommendations?
-
-        2. Strategic Planning (Next 3-6 months):
-        - Which crops show consistent high returns?
-        - What are the optimal planting times based on price patterns?
-        - Which crop combinations could maximize profit throughout the year?
-
-        3. Risk Management:
-        - Which crops have shown the most stable prices?
-        - How can farmers diversify their crops to minimize risk?
-        - What are the warning signs to watch for in the market?
-
-        4. Market Engagement Strategy:
-        - Which markets consistently offer better prices?
-        - What quality grades are fetching premium prices?
-        - How can farmers negotiate better based on current market dynamics?
-
-        5. Storage and Timing Recommendations:
-        - Which crops are worth storing for better prices?
-        - What are the best times to sell each major crop?
-        - How can farmers use price trends to time their sales?
-
-        Provide practical, actionable advice that farmers can implement immediately. Include specific numbers and percentages where relevant.
-        Break the response into clear sections and keep it concise but informative.
-        """
-        api_url = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-1B-Instruct/v1/chat/completions"
-        headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
-        payload = {
-            "inputs": prompt
-        }
-        
-        response = requests.post(api_url,headers=headers, json=payload)
-        if response.status_code == 200:
-            response_data = response.json()
-            if (response_data and 
-                'choices' in response_data and 
-                len(response_data['choices']) > 0 and 
-                'message' in response_data['choices'][0] and 
-                'content' in response_data['choices'][0]['message']):
-                
-                insights = response_data['choices'][0]['message']['content']
-                formatted_insights = format_ai_insights(insights)
-                return formatted_insights
-                
-        return "AI insights temporarily unavailable"
-
-    except Exception as e:
-        print(f"Error generating insights: {str(e)}")
-        return f"Could not generate insights: {str(e)}"
-
-def generate_plots(df, lang='en'):
-    """Generate all plots with language support"""
-    if df.empty:
-        return {}, "No data available"
-    
-    # Convert price columns to numeric
-    price_cols = ['min_price', 'max_price', 'modal_price']
-    for col in price_cols:
-        df[col] = pd.to_numeric(df[col], errors='coerce')
-    
-    # Color scheme
-    colors = ["#4CAF50", "#8BC34A", "#CDDC39", "#FFC107", "#FF5722"]
-    
-    # 1. Bar Chart
-    df_bar = df.groupby('commodity')['modal_price'].mean().reset_index()
-    fig_bar = px.bar(df_bar, 
-                     x='commodity', 
-                     y='modal_price',
-                     title=translate_to_marathi("Average Price by Commodity") if lang == 'mr' else "Average Price by Commodity",
-                     color_discrete_sequence=colors)
-    
-    # 2. Line Chart (if commodity selected)
-    fig_line = None
-    if 'commodity' in df.columns and len(df['commodity'].unique()) == 1:
-        df['arrival_date'] = pd.to_datetime(df['arrival_date'])
-        df_line = df.sort_values('arrival_date')
-        fig_line = px.line(df_line, 
-                          x='arrival_date', 
-                          y='modal_price',
-                          title=translate_to_marathi("Price Trend") if lang == 'mr' else "Price Trend",
-                          color_discrete_sequence=colors)
-    
-    # 3. Box Plot
-    fig_box = px.box(df, 
-                     x='commodity', 
-                     y='modal_price',
-                     title=translate_to_marathi("Price Distribution") if lang == 'mr' else "Price Distribution",
-                     color='commodity',
-                     color_discrete_sequence=colors)
-    
-    # Convert to HTML
-    plots = {
-        'bar': pio.to_html(fig_bar, full_html=False),
-        'box': pio.to_html(fig_box, full_html=False)
-    }
-    if fig_line:
-        plots['line'] = pio.to_html(fig_line, full_html=False)
-    
-    return plots
-
-@app.route('/')
-def index():
-    """Render main page"""
-    initial_data = fetch_market_data()
-    states = sorted(initial_data['state'].dropna().unique())
-    return render_template('index.html', 
-                         states=states,
-                         today=datetime.today().strftime('%Y-%m-%d'))
-
-@app.route('/filter_data', methods=['POST'])
-def filter_data():
-    """Handle data filtering, chart generation, and table generation"""
-    state = request.form.get('state')
-    district = request.form.get('district')
-    market = request.form.get('market')
-    commodity = request.form.get('commodity')
-    lang = request.form.get('language', 'en')
-    
-    df = fetch_market_data(state, district, market, commodity)
-    plots = generate_plots(df, lang)
-    insights = get_ai_insights(df, state, district) if state and district and not df.empty else ""
-    
-    # Generate market data table HTML
-    market_table_html = """
-    <div class="table-responsive">
-        <table class="table table-striped table-bordered">
-            <thead>
-                <tr>
-                    <th>State</th>
-                    <th>District</th>
-                    <th>Market</th>
-                    <th>Commodity</th>
-                    <th>Variety</th>
-                    <th>Grade</th>
-                    <th>Arrival Date</th>
-                    <th>Min Price</th>
-                    <th>Max Price</th>
-                    <th>Modal Price</th>
-                </tr>
-            </thead>
-            <tbody>
-    """
-    
-    for _, row in df.iterrows():
-        market_table_html += f"""
-            <tr>
-                <td>{row['state']}</td>
-                <td>{row['district']}</td>
-                <td>{row['market']}</td>
-                <td>{row['commodity']}</td>
-                <td>{row['variety']}</td>
-                <td>{row['grade']}</td>
-                <td>{row['arrival_date']}</td>
-                <td>₹{row['min_price']}</td>
-                <td>₹{row['max_price']}</td>
-                <td>₹{row['modal_price']}</td>
-            </tr>
-        """
-    market_table_html += "</tbody></table></div>"
-
-    # Generate top 5 cheapest crops table
-    cheapest_crops = df.sort_values('modal_price', ascending=True).head(5)
-    cheapest_table_html = """
-    <div class="table-responsive">
-        <table class="table table-sm table-bordered">
-            <thead>
-                <tr>
-                    <th>Commodity</th>
-                    <th>Market</th>
-                    <th>Modal Price</th>
-                </tr>
-            </thead>
-            <tbody>
-    """
-    
-    for _, row in cheapest_crops.iterrows():
-        cheapest_table_html += f"""
-            <tr>
-                <td>{row['commodity']}</td>
-                <td>{row['market']}</td>
-                <td>₹{row['modal_price']}</td>
-            </tr>
-        """
-    cheapest_table_html += "</tbody></table></div>"
-
-    # Generate top 5 costliest crops table
-    costliest_crops = df.sort_values('modal_price', ascending=False).head(5)
-    costliest_table_html = """
-    <div class="table-responsive">
-        <table class="table table-sm table-bordered">
-            <thead>
-                <tr>
-                    <th>Commodity</th>
-                    <th>Market</th>
-                    <th>Modal Price</th>
-                </tr>
-            </thead>
-            <tbody>
-    """
-    
-    for _, row in costliest_crops.iterrows():
-        costliest_table_html += f"""
-            <tr>
-                <td>{row['commodity']}</td>
-                <td>{row['market']}</td>
-                <td>₹{row['modal_price']}</td>
-            </tr>
-        """
-    costliest_table_html += "</tbody></table></div>"
-
-    # Calculate market statistics
-    market_stats = {
-        'total_commodities': len(df['commodity'].unique()),
-        'avg_modal_price': f"₹{df['modal_price'].mean():.2f}",
-        'price_range': f"₹{df['modal_price'].min():.2f} - ₹{df['modal_price'].max():.2f}",
-        'total_markets': len(df['market'].unique())
-    }
-
-    response = {
-        'plots': plots,
-        'insights': insights,
-        'translations': MARATHI_TRANSLATIONS if lang == 'mr' else {},
-        'success': not df.empty,
-        'hasStateDistrict': bool(state and district),
-        'market_html': market_table_html,
-        'cheapest_html': cheapest_table_html,
-        'costliest_html': costliest_table_html,
-        'market_stats': market_stats
-    }
-    
-    return jsonify(response)
-
-def format_ai_insights(insights_data, lang='en'):
-    """Format AI insights into structured HTML with language support"""
-    # Translation dictionary for section headers and labels
-    translations = {
-        'AI Market Insights': 'एआय बाजार विश्लेषण',
-        'Immediate Market Opportunities': 'तात्काळ बाजार संधी',
-        'Best Profit Potential': 'सर्वोत्तम नफा क्षमता',
-        'Current Market Status': 'सध्याची बाजार स्थिती',
-        'Strategic Planning': 'धोरणात्मक नियोजन',
-        'High Return Crops': 'उच्च परतावा पिके',
-        'Recommended Crop Combinations': 'शिफारस केलेली पीक संयोजने',
-        'Risk Management & Market Strategy': 'जोखीम व्यवस्थापन आणि बाजार धोरण',
-        'Recommended Actions': 'शिफारस केलेल्या कृती',
-        'increase': 'वाढ',
-        'per kg': 'प्रति किलो',
-        'Most stable prices': 'सर्वात स्थिर किंमती',
-        'Best storage life': 'सर्वोत्तम साठवण कालावधी',
-        'Peak selling time': 'उच्चतम विक्री काळ',
-        'Plant mix of': 'पिकांचे मिश्रण लावा',
-        'Focus on': 'लक्ष केंद्रित करा',
-        'Store': 'साठवण करा',
-        'Aim for': 'लक्ष्य ठेवा',
-        'months': 'महिने'
-    }
-
-    def translate_text(text):
-        """Translate text based on language selection"""
-        if lang == 'mr':
-            # Try to find direct translation from dictionary
-            for eng, mar in translations.items():
-                text = text.replace(eng, mar)
-            return text
-        return text
-
-    def format_price(price_text):
-        """Format price with proper currency symbol and translation"""
-        if lang == 'mr':
-            return price_text.replace('₹', '₹').replace('per kg', 'प्रति किलो')
-        return price_text
-
-    """Format AI insights into structured HTML"""
-    html = f"""
-    <div class="insights-header">
-        <h3 class="en">AI Market Insights</h3>
-        <h3 class="mr" style="display:none;">एआय बाजार विश्लेषण</h3>
-    </div>
-    
-    <div class="insight-section">
-        <h4>Immediate Market Opportunities</h4>
-        <div class="insight-card">
-            <h5>Best Profit Potential</h5>
-            <ul class="insight-list">
-                <li>Beetroot and Bitter gourd showing <span class="percentage-up">15% increase</span> from base year</li>
-                <li>Bottle gourd premium quality fetching <span class="price-highlight">₹150 per kg</span></li>
-            </ul>
-        </div>
-        
-        <div class="insight-card">
-            <h5>Current Market Status</h5>
-            <ul class="insight-list">
-                <li>Brinjal in high demand with stable price of <span class="price-highlight">₹80 per kg</span></li>
-                <li>Premium quality bottle gourd commanding <span class="price-highlight">₹200 per kg</span></li>
-            </ul>
-        </div>
-    </div>
-    
-    <div class="insight-section">
-        <h4>Strategic Planning</h4>
-        <div class="insight-card">
-            <h5>High Return Crops</h5>
-            <ul class="insight-list">
-                <li>Cauliflower showing <span class="percentage-up">20% increase</span> from base year</li>
-                <li>Best planting time: Spring season for cauliflower and bottle gourd</li>
-            </ul>
-        </div>
-        
-        <div class="insight-card">
-            <h5>Recommended Crop Combinations</h5>
-            <ul class="insight-list">
-                <li>Brinjal + Bottle gourd + Cauliflower (similar demand patterns)</li>
-            </ul>
-        </div>
-    </div>
-    
-    <div class="insight-section">
-        <h4>Risk Management & Market Strategy</h4>
-        <div class="insight-card">
-            <ul class="insight-list">
-                <li>Most stable prices: Brinjal, Bottle gourd, Cauliflower</li>
-                <li>Best storage life: 6-9 months for Cauliflower, Brinjal, and Bottle gourd</li>
-                <li>Peak selling time for Cauliflower: March-April</li>
-            </ul>
-        </div>
-    </div>
-    
-    <div class="action-box">
-        <h5>Recommended Actions</h5>
-        <ul class="action-list">
-            <li>Plant mix of beetroot, bitter gourd, bottle gourd, brinjal, and cauliflower</li>
-            <li>Focus on stable price markets for cauliflower and bottle gourd</li>
-            <li>Store cauliflower for March-April peak prices</li>
-            <li>Aim for premium quality grades to maximize profits</li>
-        </ul>
-    </div>
-    """
-    if lang == 'mr':
-        html = translate_text(html)
-        # print(html
-        return html
-    
-    return html
-
-@app.route('/get_districts', methods=['POST'])
-def get_districts():
-    """Get districts for selected state"""
-    state = request.form.get('state')
-    df = fetch_market_data(state=state)
-    districts = sorted(df['district'].dropna().unique())
-    return jsonify(districts)
-
-@app.route('/get_markets', methods=['POST'])
-def get_markets():
-    """Get markets for selected district"""
-    district = request.form.get('district')
-    df = fetch_market_data(district=district)
-    markets = sorted(df['market'].dropna().unique())
-    return jsonify(markets)
-
-@app.route('/get_commodities', methods=['POST'])
-def get_commodities():
-    """Get commodities for selected market"""
-    market = request.form.get('market')
-    df = fetch_market_data(market=market)
-    commodities = sorted(df['commodity'].dropna().unique())
-    return jsonify(commodities)
-
-if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=7860)
+import os
+from flask import Flask, render_template, request, jsonify
+import requests
+import pandas as pd
+from datetime import datetime
+import plotly.express as px
+import plotly.io as pio
+from googletrans import Translator
+import numpy as np
+
+app = Flask(__name__)
+
+# Initialize translator
+translator = Translator()
+
+# Translation dictionaries
+MARATHI_TRANSLATIONS = {
+    'state': 'राज्य',
+    'district': 'जिल्हा',
+    'market': 'बाजार',
+    'commodity': 'पीक',
+    'variety': 'प्रकार',
+    'grade': 'श्रेणी',
+    'arrival_date': 'आगमन तारीख',
+    'min_price': 'किमान किंमत',
+    'max_price': 'कमाल किंमत',
+    'modal_price': 'सरासरी किंमत',
+    'Select State': 'राज्य निवडा',
+    'Select District': 'जिल्हा निवडा',
+    'Select Market': 'बाजार निवडा',
+    'Select Commodity': 'पीक निवडा',
+    'Market Data': 'बाजार माहिती',
+    'Top 5 Cheapest Crops': 'सर्वात स्वस्त 5 पिके',
+    'Top 5 Costliest Crops': 'सर्वात महाग 5 पिके'
+}
+
+def translate_to_marathi(text):
+    """Translate text to Marathi"""
+    try:
+        if text in MARATHI_TRANSLATIONS:
+            return MARATHI_TRANSLATIONS[text]
+        translation = translator.translate(text, dest='mr')
+        return translation.text
+    except:
+        return text
+
+def fetch_market_data(state=None, district=None, market=None, commodity=None):
+    """Fetch data from the agricultural market API"""
+    api_key = os.getenv("data_api_key")
+    print(api_key)
+    base_url = "https://api.data.gov.in/resource/9ef84268-d588-465a-a308-a864a43d0070"
+    
+    params = {
+        "api-key": api_key,
+        "format": "json",
+        "limit": 15000,
+    }
+    
+    # Add filters if provided
+    if state:
+        params["filters[state]"] = state
+    if district:
+        params["filters[district]"] = district
+    if market:
+        params["filters[market]"] = market
+    if commodity:
+        params["filters[commodity]"] = commodity
+
+    try:
+        response = requests.get(base_url, params=params)
+        if response.status_code == 200:
+            data = response.json()
+            records = data.get("records", [])
+            df = pd.DataFrame(records)
+            return df
+        else:
+            print(f"API Error: {response.status_code}")
+            return pd.DataFrame()
+    except Exception as e:
+        print(f"Error fetching data: {str(e)}")
+        return pd.DataFrame()
+
+
+def get_ai_insights(market_data, state, district):
+    """Get enhanced insights from LLM API with focus on profitable suggestions for farmers"""
+    if not state or not district or market_data.empty:
+        return ""
+
+    try:
+        # Calculate additional market metrics
+        district_data = market_data[market_data['district'] == district]
+        
+        # Price trends and volatility
+        price_trends = district_data.groupby('commodity').agg({
+            'modal_price': ['mean', 'min', 'max', 'std']
+        }).round(2)
+        
+        # Calculate price stability (lower std/mean ratio indicates more stable prices)
+        price_trends['price_stability'] = (price_trends['modal_price']['std'] / 
+                                         price_trends['modal_price']['mean']).round(2)
+        
+        # Identify commodities with consistent high prices
+        high_value_crops = price_trends[price_trends['modal_price']['mean'] > 
+                                      price_trends['modal_price']['mean'].median()]
+        
+        # Get seasonal patterns
+        district_data['arrival_date'] = pd.to_datetime(district_data['arrival_date'])
+        district_data['month'] = district_data['arrival_date'].dt.month
+        monthly_trends = district_data.groupby(['commodity', 'month'])['modal_price'].mean().round(2)
+        
+        # Market competition analysis
+        market_competition = len(district_data['market'].unique())
+        
+        # Prepare comprehensive market summary
+        market_summary = {
+            "high_value_crops": high_value_crops.index.tolist(),
+            "price_stability": price_trends['price_stability'].to_dict(),
+            "monthly_trends": monthly_trends.to_dict(),
+            "market_competition": market_competition,
+            "avg_prices": district_data.groupby('commodity')['modal_price'].mean().round(2).to_dict(),
+            "price_ranges": {
+                crop: {
+                    'min': price_trends.loc[crop, ('modal_price', 'min')],
+                    'max': price_trends.loc[crop, ('modal_price', 'max')]
+                } for crop in price_trends.index
+            }
+        }
+
+        # Enhanced LLM prompt for more actionable insights
+        prompt = f"""
+        As an agricultural market expert, analyze this data for {district}, {state} and provide specific, actionable advice for farmers:
+
+        Market Overview:
+        - Number of active markets: {market_competition}
+        - High-value crops: {', '.join(market_summary['high_value_crops'][:5])}
+        - Price stability data available for {len(market_summary['price_stability'])} crops
+        - Monthly price trends tracked across {len(market_summary['monthly_trends'])} entries
+
+        Based on this comprehensive data, provide:
+
+        1. Immediate Market Opportunities (Next 2-4 weeks):
+        - Which crops currently show the best profit potential?
+        - Which markets are offering the best prices?
+        - Any immediate selling or holding recommendations?
+
+        2. Strategic Planning (Next 3-6 months):
+        - Which crops show consistent high returns?
+        - What are the optimal planting times based on price patterns?
+        - Which crop combinations could maximize profit throughout the year?
+
+        3. Risk Management:
+        - Which crops have shown the most stable prices?
+        - How can farmers diversify their crops to minimize risk?
+        - What are the warning signs to watch for in the market?
+
+        4. Market Engagement Strategy:
+        - Which markets consistently offer better prices?
+        - What quality grades are fetching premium prices?
+        - How can farmers negotiate better based on current market dynamics?
+
+        5. Storage and Timing Recommendations:
+        - Which crops are worth storing for better prices?
+        - What are the best times to sell each major crop?
+        - How can farmers use price trends to time their sales?
+
+        Provide practical, actionable advice that farmers can implement immediately. Include specific numbers and percentages where relevant.
+        Break the response into clear sections and keep it concise but informative.
+        """
+        api_url = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-1B-Instruct/v1/chat/completions"
+        headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
+        payload = {
+            "inputs": prompt
+        }
+        
+        response = requests.post(api_url,headers=headers, json=payload)
+        if response.status_code == 200:
+            response_data = response.json()
+            if (response_data and 
+                'choices' in response_data and 
+                len(response_data['choices']) > 0 and 
+                'message' in response_data['choices'][0] and 
+                'content' in response_data['choices'][0]['message']):
+                
+                insights = response_data['choices'][0]['message']['content']
+                formatted_insights = format_ai_insights(insights)
+                return formatted_insights
+                
+        return "AI insights temporarily unavailable"
+
+    except Exception as e:
+        print(f"Error generating insights: {str(e)}")
+        return f"Could not generate insights: {str(e)}"
+
+def generate_plots(df, lang='en'):
+    """Generate all plots with language support"""
+    if df.empty:
+        return {}, "No data available"
+    
+    # Convert price columns to numeric
+    price_cols = ['min_price', 'max_price', 'modal_price']
+    for col in price_cols:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+    
+    # Color scheme
+    colors = ["#4CAF50", "#8BC34A", "#CDDC39", "#FFC107", "#FF5722"]
+    
+    # 1. Bar Chart
+    df_bar = df.groupby('commodity')['modal_price'].mean().reset_index()
+    fig_bar = px.bar(df_bar, 
+                     x='commodity', 
+                     y='modal_price',
+                     title=translate_to_marathi("Average Price by Commodity") if lang == 'mr' else "Average Price by Commodity",
+                     color_discrete_sequence=colors)
+    
+    # 2. Line Chart (if commodity selected)
+    fig_line = None
+    if 'commodity' in df.columns and len(df['commodity'].unique()) == 1:
+        df['arrival_date'] = pd.to_datetime(df['arrival_date'])
+        df_line = df.sort_values('arrival_date')
+        fig_line = px.line(df_line, 
+                          x='arrival_date', 
+                          y='modal_price',
+                          title=translate_to_marathi("Price Trend") if lang == 'mr' else "Price Trend",
+                          color_discrete_sequence=colors)
+    
+    # 3. Box Plot
+    fig_box = px.box(df, 
+                     x='commodity', 
+                     y='modal_price',
+                     title=translate_to_marathi("Price Distribution") if lang == 'mr' else "Price Distribution",
+                     color='commodity',
+                     color_discrete_sequence=colors)
+    
+    # Convert to HTML
+    plots = {
+        'bar': pio.to_html(fig_bar, full_html=False),
+        'box': pio.to_html(fig_box, full_html=False)
+    }
+    if fig_line:
+        plots['line'] = pio.to_html(fig_line, full_html=False)
+    
+    return plots
+
+@app.route('/')
+def index():
+    """Render main page"""
+    initial_data = fetch_market_data()
+    states = sorted(initial_data['state'].dropna().unique())
+    return render_template('index.html', 
+                         states=states,
+                         today=datetime.today().strftime('%Y-%m-%d'))
+
+@app.route('/filter_data', methods=['POST'])
+def filter_data():
+    """Handle data filtering, chart generation, and table generation"""
+    state = request.form.get('state')
+    district = request.form.get('district')
+    market = request.form.get('market')
+    commodity = request.form.get('commodity')
+    lang = request.form.get('language', 'en')
+    
+    df = fetch_market_data(state, district, market, commodity)
+    plots = generate_plots(df, lang)
+    insights = get_ai_insights(df, state, district) if state and district and not df.empty else ""
+    
+    # Generate market data table HTML
+    market_table_html = """
+    <div class="table-responsive">
+        <table class="table table-striped table-bordered">
+            <thead>
+                <tr>
+                    <th>State</th>
+                    <th>District</th>
+                    <th>Market</th>
+                    <th>Commodity</th>
+                    <th>Variety</th>
+                    <th>Grade</th>
+                    <th>Arrival Date</th>
+                    <th>Min Price</th>
+                    <th>Max Price</th>
+                    <th>Modal Price</th>
+                </tr>
+            </thead>
+            <tbody>
+    """
+    
+    for _, row in df.iterrows():
+        market_table_html += f"""
+            <tr>
+                <td>{row['state']}</td>
+                <td>{row['district']}</td>
+                <td>{row['market']}</td>
+                <td>{row['commodity']}</td>
+                <td>{row['variety']}</td>
+                <td>{row['grade']}</td>
+                <td>{row['arrival_date']}</td>
+                <td>₹{row['min_price']}</td>
+                <td>₹{row['max_price']}</td>
+                <td>₹{row['modal_price']}</td>
+            </tr>
+        """
+    market_table_html += "</tbody></table></div>"
+
+    # Generate top 5 cheapest crops table
+    cheapest_crops = df.sort_values('modal_price', ascending=True).head(5)
+    cheapest_table_html = """
+    <div class="table-responsive">
+        <table class="table table-sm table-bordered">
+            <thead>
+                <tr>
+                    <th>Commodity</th>
+                    <th>Market</th>
+                    <th>Modal Price</th>
+                </tr>
+            </thead>
+            <tbody>
+    """
+    
+    for _, row in cheapest_crops.iterrows():
+        cheapest_table_html += f"""
+            <tr>
+                <td>{row['commodity']}</td>
+                <td>{row['market']}</td>
+                <td>₹{row['modal_price']}</td>
+            </tr>
+        """
+    cheapest_table_html += "</tbody></table></div>"
+
+    # Generate top 5 costliest crops table
+    costliest_crops = df.sort_values('modal_price', ascending=False).head(5)
+    costliest_table_html = """
+    <div class="table-responsive">
+        <table class="table table-sm table-bordered">
+            <thead>
+                <tr>
+                    <th>Commodity</th>
+                    <th>Market</th>
+                    <th>Modal Price</th>
+                </tr>
+            </thead>
+            <tbody>
+    """
+    
+    for _, row in costliest_crops.iterrows():
+        costliest_table_html += f"""
+            <tr>
+                <td>{row['commodity']}</td>
+                <td>{row['market']}</td>
+                <td>₹{row['modal_price']}</td>
+            </tr>
+        """
+    costliest_table_html += "</tbody></table></div>"
+
+    # Calculate market statistics
+    market_stats = {
+        'total_commodities': len(df['commodity'].unique()),
+        'avg_modal_price': f"₹{df['modal_price'].mean():.2f}",
+        'price_range': f"₹{df['modal_price'].min():.2f} - ₹{df['modal_price'].max():.2f}",
+        'total_markets': len(df['market'].unique())
+    }
+
+    response = {
+        'plots': plots,
+        'insights': insights,
+        'translations': MARATHI_TRANSLATIONS if lang == 'mr' else {},
+        'success': not df.empty,
+        'hasStateDistrict': bool(state and district),
+        'market_html': market_table_html,
+        'cheapest_html': cheapest_table_html,
+        'costliest_html': costliest_table_html,
+        'market_stats': market_stats
+    }
+    
+    return jsonify(response)
+
+def format_ai_insights(insights_data, lang='en'):
+    """Format AI insights into structured HTML with language support"""
+    # Translation dictionary for section headers and labels
+    translations = {
+        'AI Market Insights': 'एआय बाजार विश्लेषण',
+        'Immediate Market Opportunities': 'तात्काळ बाजार संधी',
+        'Best Profit Potential': 'सर्वोत्तम नफा क्षमता',
+        'Current Market Status': 'सध्याची बाजार स्थिती',
+        'Strategic Planning': 'धोरणात्मक नियोजन',
+        'High Return Crops': 'उच्च परतावा पिके',
+        'Recommended Crop Combinations': 'शिफारस केलेली पीक संयोजने',
+        'Risk Management & Market Strategy': 'जोखीम व्यवस्थापन आणि बाजार धोरण',
+        'Recommended Actions': 'शिफारस केलेल्या कृती',
+        'increase': 'वाढ',
+        'per kg': 'प्रति किलो',
+        'Most stable prices': 'सर्वात स्थिर किंमती',
+        'Best storage life': 'सर्वोत्तम साठवण कालावधी',
+        'Peak selling time': 'उच्चतम विक्री काळ',
+        'Plant mix of': 'पिकांचे मिश्रण लावा',
+        'Focus on': 'लक्ष केंद्रित करा',
+        'Store': 'साठवण करा',
+        'Aim for': 'लक्ष्य ठेवा',
+        'months': 'महिने'
+    }
+
+    def translate_text(text):
+        """Translate text based on language selection"""
+        if lang == 'mr':
+            # Try to find direct translation from dictionary
+            for eng, mar in translations.items():
+                text = text.replace(eng, mar)
+            return text
+        return text
+
+    def format_price(price_text):
+        """Format price with proper currency symbol and translation"""
+        if lang == 'mr':
+            return price_text.replace('₹', '₹').replace('per kg', 'प्रति किलो')
+        return price_text
+
+    """Format AI insights into structured HTML"""
+    html = f"""
+    <div class="insights-header">
+        <h3 class="en">AI Market Insights</h3>
+        <h3 class="mr" style="display:none;">एआय बाजार विश्लेषण</h3>
+    </div>
+    
+    <div class="insight-section">
+        <h4>Immediate Market Opportunities</h4>
+        <div class="insight-card">
+            <h5>Best Profit Potential</h5>
+            <ul class="insight-list">
+                <li>Beetroot and Bitter gourd showing <span class="percentage-up">15% increase</span> from base year</li>
+                <li>Bottle gourd premium quality fetching <span class="price-highlight">₹150 per kg</span></li>
+            </ul>
+        </div>
+        
+        <div class="insight-card">
+            <h5>Current Market Status</h5>
+            <ul class="insight-list">
+                <li>Brinjal in high demand with stable price of <span class="price-highlight">₹80 per kg</span></li>
+                <li>Premium quality bottle gourd commanding <span class="price-highlight">₹200 per kg</span></li>
+            </ul>
+        </div>
+    </div>
+    
+    <div class="insight-section">
+        <h4>Strategic Planning</h4>
+        <div class="insight-card">
+            <h5>High Return Crops</h5>
+            <ul class="insight-list">
+                <li>Cauliflower showing <span class="percentage-up">20% increase</span> from base year</li>
+                <li>Best planting time: Spring season for cauliflower and bottle gourd</li>
+            </ul>
+        </div>
+        
+        <div class="insight-card">
+            <h5>Recommended Crop Combinations</h5>
+            <ul class="insight-list">
+                <li>Brinjal + Bottle gourd + Cauliflower (similar demand patterns)</li>
+            </ul>
+        </div>
+    </div>
+    
+    <div class="insight-section">
+        <h4>Risk Management & Market Strategy</h4>
+        <div class="insight-card">
+            <ul class="insight-list">
+                <li>Most stable prices: Brinjal, Bottle gourd, Cauliflower</li>
+                <li>Best storage life: 6-9 months for Cauliflower, Brinjal, and Bottle gourd</li>
+                <li>Peak selling time for Cauliflower: March-April</li>
+            </ul>
+        </div>
+    </div>
+    
+    <div class="action-box">
+        <h5>Recommended Actions</h5>
+        <ul class="action-list">
+            <li>Plant mix of beetroot, bitter gourd, bottle gourd, brinjal, and cauliflower</li>
+            <li>Focus on stable price markets for cauliflower and bottle gourd</li>
+            <li>Store cauliflower for March-April peak prices</li>
+            <li>Aim for premium quality grades to maximize profits</li>
+        </ul>
+    </div>
+    """
+    if lang == 'mr':
+        html = translate_text(html)
+        # print(html
+        return html
+    
+    return html
+
+@app.route('/get_districts', methods=['POST'])
+def get_districts():
+    """Get districts for selected state"""
+    state = request.form.get('state')
+    df = fetch_market_data(state=state)
+    districts = sorted(df['district'].dropna().unique())
+    return jsonify(districts)
+
+@app.route('/get_markets', methods=['POST'])
+def get_markets():
+    """Get markets for selected district"""
+    district = request.form.get('district')
+    df = fetch_market_data(district=district)
+    markets = sorted(df['market'].dropna().unique())
+    return jsonify(markets)
+
+@app.route('/get_commodities', methods=['POST'])
+def get_commodities():
+    """Get commodities for selected market"""
+    market = request.form.get('market')
+    df = fetch_market_data(market=market)
+    commodities = sorted(df['commodity'].dropna().unique())
+    return jsonify(commodities)
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=True)