Update app.py

app.py

@@ -8,7 +8,7 @@ import plotly.io as pio
 from googletrans import Translator
 import numpy as np
 
-app = Flask(__name__)
+app = Flask(_name_)
 
 # Initialize translator
 translator = Translator()
@@ -34,6 +34,7 @@ MARATHI_TRANSLATIONS = {
     'Top 5 Costliest Crops': 'सर्वात महाग 5 पिके'
 }
 
+
 def translate_to_marathi(text):
     """Translate text to Marathi"""
     try:
@@ -44,18 +45,19 @@ def translate_to_marathi(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 = "579b464db66ec23bdd000001189bbb99e979428764bdbe8fdd44ebb7"
+    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": 100,
+        "limit": 15000,
     }
-    
+
     # Add filters if provided
     if state:
         params["filters[state]"] = state
@@ -89,28 +91,28 @@ def get_ai_insights(market_data, state, district):
     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)
-        
+        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()]
-        
+        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(),
@@ -168,69 +170,71 @@ def get_ai_insights(market_data, state, district):
         """
         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)
+
+        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']):
-                
+            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', 
+    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",
+                     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)
-    
+        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', 
+    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),
@@ -238,17 +242,19 @@ def generate_plots(df, lang='en'):
     }
     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'))
+    return render_template('index.html',
+                           states=states,
+                           today=datetime.today().strftime('%Y-%m-%d'))
+
 
 @app.route('/filter_data', methods=['POST'])
 def filter_data():
@@ -258,11 +264,11 @@ def filter_data():
     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">
@@ -283,7 +289,7 @@ def filter_data():
             </thead>
             <tbody>
     """
-    
+
     for _, row in df.iterrows():
         market_table_html += f"""
             <tr>
@@ -315,7 +321,7 @@ def filter_data():
             </thead>
             <tbody>
     """
-    
+
     for _, row in cheapest_crops.iterrows():
         cheapest_table_html += f"""
             <tr>
@@ -340,7 +346,7 @@ def filter_data():
             </thead>
             <tbody>
     """
-    
+
     for _, row in costliest_crops.iterrows():
         costliest_table_html += f"""
             <tr>
@@ -370,9 +376,10 @@ def filter_data():
         '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
@@ -419,7 +426,7 @@ def format_ai_insights(insights_data, lang='en'):
         <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">
@@ -429,7 +436,7 @@ def format_ai_insights(insights_data, lang='en'):
                 <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">
@@ -438,7 +445,7 @@ def format_ai_insights(insights_data, lang='en'):
             </ul>
         </div>
     </div>
-    
+
     <div class="insight-section">
         <h4>Strategic Planning</h4>
         <div class="insight-card">
@@ -448,7 +455,7 @@ def format_ai_insights(insights_data, lang='en'):
                 <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">
@@ -456,7 +463,7 @@ def format_ai_insights(insights_data, lang='en'):
             </ul>
         </div>
     </div>
-    
+
     <div class="insight-section">
         <h4>Risk Management & Market Strategy</h4>
         <div class="insight-card">
@@ -467,7 +474,7 @@ def format_ai_insights(insights_data, lang='en'):
             </ul>
         </div>
     </div>
-    
+
     <div class="action-box">
         <h5>Recommended Actions</h5>
         <ul class="action-list">
@@ -482,9 +489,10 @@ def format_ai_insights(insights_data, lang='en'):
         html = translate_text(html)
         # print(html
         return html
-    
+
     return html
 
+
 @app.route('/get_districts', methods=['POST'])
 def get_districts():
     """Get districts for selected state"""
@@ -493,6 +501,7 @@ def get_districts():
     districts = sorted(df['district'].dropna().unique())
     return jsonify(districts)
 
+
 @app.route('/get_markets', methods=['POST'])
 def get_markets():
     """Get markets for selected district"""
@@ -501,6 +510,7 @@ def get_markets():
     markets = sorted(df['market'].dropna().unique())
     return jsonify(markets)
 
+
 @app.route('/get_commodities', methods=['POST'])
 def get_commodities():
     """Get commodities for selected market"""
@@ -509,5 +519,6 @@ def get_commodities():
     commodities = sorted(df['commodity'].dropna().unique())
     return jsonify(commodities)
 
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+
+if _name_ == '_main_':
+    app.run(debug=True, host='0.0.0.0', port=7860)