Update app.py

app.py

@@ -1,529 +1,528 @@
-
-import requests
-import json
-from flask import Flask, render_template, request, jsonify, Response, send_file
-from google import genai
-from gtts import gTTS
-import os
-from dotenv import load_dotenv
-from datetime import datetime, timedelta
-
-# Load .env file
-load_dotenv()
-
-app = Flask(__name__)
-app.config['AUDIO_FOLDER'] = 'static/audio'
-os.makedirs(app.config['AUDIO_FOLDER'], exist_ok=True)
-
-def markdown_to_html(text):
-    """Convert markdown text to HTML for proper rendering."""
-    if not text:
-        return text
-    import markdown as md
-    return md.markdown(text, extensions=['nl2br'])
-
-# IMPORTANT: Replace with your actual Gemini API key
-api_key = os.getenv('GEMINI_API_KEY')
-if not api_key:
-  api_key = os.getenv('GEMINI_API_KEY_1')
-if not api_key:
-  api_key = os.getenv('GEMINI_API_KEY_2')
-
-if not api_key:
-    raise ValueError("GEMINI_API_KEY is not set. Please add it to your .env file.")
-  
-print(f"Initializing Gemini client with API key: {api_key[:10]}...")
-client = genai.Client(api_key=api_key)
-def validate_coordinates(lat, lon):
-    """Validate and convert latitude and longitude to float."""
-    try:
-        return float(lat), float(lon)
-    except (TypeError, ValueError):
-        return None, None
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/get_weather_data', methods=['GET'])
-def get_weather_data():
-    """
-    Fetch weather data using Open-Meteo's forecast endpoint.
-    """
-    lat = request.args.get('lat')
-    lon = request.args.get('lon')
-    lat, lon = validate_coordinates(lat, lon)
-    if lat is None or lon is None:
-        return jsonify({"error": "Invalid coordinates"}), 400
-    
-    try:
-        forecast_url = "https://api.open-meteo.com/v1/forecast"
-        forecast_params = {
-            "latitude": lat,
-            "longitude": lon,
-            "current_weather": "true",
-            "daily": "temperature_2m_max,temperature_2m_min,precipitation_sum",
-            "hourly": "relative_humidity_2m,soil_moisture_3_to_9cm,cloudcover,windspeed_10m",
-            "timezone": "auto"
-        }
-        resp = requests.get(forecast_url, params=forecast_params)
-        resp.raise_for_status()
-        data = resp.json()
-
-        daily   = data.get("daily", {})
-        hourly  = data.get("hourly", {})
-        current = data.get("current_weather", {})
-
-        # Daily data
-        max_temp = daily.get("temperature_2m_max", [None])[0]
-        min_temp = daily.get("temperature_2m_min", [None])[0]
-        rain     = daily.get("precipitation_sum", [None])[0]
-
-        # Hourly data (averages)
-        humidity_list     = hourly.get("relative_humidity_2m", [])
-        soil_list         = hourly.get("soil_moisture_3_to_9cm", [])
-        cloud_list        = hourly.get("cloudcover", [])
-        
-        avg_humidity      = sum(humidity_list)/len(humidity_list) if humidity_list else None
-        avg_soil_moisture = sum(soil_list)/len(soil_list) if soil_list else None
-        avg_cloud_cover   = sum(cloud_list)/len(cloud_list) if cloud_list else None
-
-        # Current weather
-        current_temp = current.get("temperature")
-        wind_speed   = current.get("windspeed")
-
-        weather = {
-            "max_temp": max_temp, "min_temp": min_temp, "rainfall": rain,
-            "humidity": avg_humidity, "soil_moisture": avg_soil_moisture,
-            "current_temp": current_temp, "wind_speed": wind_speed, "cloud_cover": avg_cloud_cover
-        }
-        return jsonify(weather)
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-def get_historical_weather_summary(lat, lon, start_date_str, end_date_str):
-    """
-    Fetches historical weather data from Open-Meteo Archive for the specified period.
-    If period is in future, shifts to previous year.
-    Returns a text summary of monthly averages.
-    """
-    try:
-        if not start_date_str or not end_date_str:
-            return "Weather data unavailable (dates missing)."
-
-        start = datetime.strptime(start_date_str, '%Y-%m-%d')
-        end = datetime.strptime(end_date_str, '%Y-%m-%d')
-        today = datetime.now()
-
-        # Logic: If start date is in future, use last year's data as proxy
-        is_proxy = False
-        if start > today:
-            start = start.replace(year=start.year - 1)
-            end = end.replace(year=end.year - 1)
-            is_proxy = True
-        
-        # Ensure we don't query future for the archive (e.g. if harvest is next month)
-        # If the *adjusted* end date is still after today (rare if we shifted year, but possible), clip it.
-        if end > today:
-             end = today
-
-        # Call Open-Meteo Archive
-        archive_url = "https://archive-api.open-meteo.com/v1/archive"
-        params = {
-            "latitude": lat,
-            "longitude": lon,
-            "start_date": start.strftime('%Y-%m-%d'),
-            "end_date": end.strftime('%Y-%m-%d'),
-            "daily": "temperature_2m_max,temperature_2m_min,precipitation_sum,relative_humidity_2m_mean",
-            "timezone": "auto"
-        }
-        
-        resp = requests.get(archive_url, params=params)
-        
-        if resp.status_code != 200:
-             print(f"DEBUG: API Failed {resp.status_code} - {resp.text}")
-             return f"Could not fetch weather data: {resp.status_code}", []
-             
-        data = resp.json()
-        daily = data.get("daily", {})
-        
-        dates = daily.get("time", [])
-        
-        print(f"DEBUG: Retrieved {len(dates)} days.")
-        if dates:
-            print(f"DEBUG: Range {dates[0]} to {dates[-1]}")
-        
-        if not dates:
-            return "No weather data available for this range.", []
-
-        summary_parts = []
-        structured_data = [] 
-        if is_proxy:
-             summary_parts.append("(Note: Using last year's weather as proxy for future dates)")
-
-        # Simple aggregation loop
-        current_month = None
-        temp_sum = 0
-        hum_sum = 0
-        rain_sum = 0
-        count = 0
-        
-        # Extract lists
-        max_temps = daily.get("temperature_2m_max", [])
-        humidities = daily.get("relative_humidity_2m_mean", [])
-        rains = daily.get("precipitation_sum", [])
-
-
-        # Simple aggregation loop
-        current_month = None
-        temp_sum = 0
-        hum_sum = 0
-        rain_sum = 0
-        count = 0
-        month_days = []
-        
-        for i, d_str in enumerate(dates):
-            date_obj = datetime.strptime(d_str, '%Y-%m-%d')
-            month_key = date_obj.strftime('%B %Y')
-            
-            # Accumulate values (handle None)
-            t = max_temps[i] if max_temps[i] is not None else 0
-            h = humidities[i] if humidities[i] is not None else 0
-            r = rains[i] if rains[i] is not None else 0
-            
-            if current_month is None: current_month = month_key
-            
-            if month_key != current_month:
-                # Flush previous month
-                avg_t = temp_sum / count if count else 0
-                avg_h = hum_sum / count if count else 0
-                summary_parts.append(f"{current_month}: Avg Temp {avg_t:.1f}C, Rain {rain_sum:.1f}mm, Humidity {avg_h:.1f}%")
-                
-                # Add to structured data
-                structured_data.append({
-                    "month": current_month,
-                    "avg_temp": round(avg_t, 1),
-                    "rainfall": round(rain_sum, 1),
-                    "humidity": round(avg_h, 1),
-                    "days": month_days
-                })
-                
-                # Reset
-                current_month = month_key
-                temp_sum = 0; hum_sum = 0; rain_sum = 0; count = 0
-                month_days = []
-            
-            # Accumulate
-            temp_sum += t
-            hum_sum += h
-            rain_sum += r
-            count += 1
-            
-            # Add to daily list
-            month_days.append({
-                "date": d_str,
-                "temp": t,
-                "humidity": h,
-                "rain": r
-            })
-            
-        # Flush last month
-        if count > 0:
-            avg_t = temp_sum / count
-            avg_h = hum_sum / count
-            summary_parts.append(f"{current_month}: Avg Temp {avg_t:.1f}C, Rain {rain_sum:.1f}mm, Humidity {avg_h:.1f}%")
-            structured_data.append({
-                "month": current_month,
-                "avg_temp": round(avg_t, 1),
-                "rainfall": round(rain_sum, 1),
-                "humidity": round(avg_h, 1),
-                "days": month_days
-            })
-
-        return "\n".join(summary_parts), structured_data
-
-    except Exception as e:
-        print(f"Weather Fetch Error: {e}")
-        return "Weather data processing failed.", []
-
-def calculate_season_dates(season_name):
-    """
-    Derives standard sowing and harvest dates based on the Indian agricultural season.
-    """
-    today = datetime.today()
-    current_year = today.year
-    
-    # Defaults
-    sowing_date = today
-    harvest_date = today + timedelta(days=120)
-
-    try:
-        if season_name == "Kharif":
-            # June 15 to Oct 15
-            sowing_date = datetime(current_year, 6, 15)
-            harvest_date = datetime(current_year, 10, 15)
-            # If we are past Oct, maybe predicts for next year? 
-            # For simplicity, assume current year context or next if late.
-            if today.month > 10:
-                sowing_date = datetime(current_year + 1, 6, 15)
-                harvest_date = datetime(current_year + 1, 10, 15)
-
-        elif season_name == "Rabi":
-            # Nov 1 to April 1 (Crosses year boundary)
-            if today.month > 4: 
-                # Predicting for upcoming Rabi
-                sowing_date = datetime(current_year, 11, 1)
-                harvest_date = datetime(current_year + 1, 4, 1)
-            else:
-                # We are IN Rabi or just past
-                sowing_date = datetime(current_year - 1, 11, 1)
-                harvest_date = datetime(current_year, 4, 1)
-
-        elif season_name == "Zaid":
-            # March 1 to June 1
-            sowing_date = datetime(current_year, 3, 1)
-            harvest_date = datetime(current_year, 6, 1)
-            if today.month > 6:
-                sowing_date = datetime(current_year + 1, 3, 1)
-                harvest_date = datetime(current_year + 1, 6, 1)
-
-        elif season_name == "Annual":
-            # 1 Year cycle
-            sowing_date = today
-            harvest_date = today + timedelta(days=365)
-            
-    except Exception as e:
-        print(f"Date Calc Error: {e}")
-
-    return sowing_date.strftime('%Y-%m-%d'), harvest_date.strftime('%Y-%m-%d')
-
-def call_gemini_api(input_data, language):
-    """
-    Calls the Gemini API to get a pest outbreak report in a structured JSON format.
-    Implements fallback mechanism to try multiple models if primary fails.
-    """
-    
-    # 1. Logic: Determine Dates from Season
-    lat = input_data.get('latitude')
-    lon = input_data.get('longitude')
-    season = input_data.get('season')
-    
-    # Auto-calculate dates based on Season
-    if season:
-        sowing, harvest = calculate_season_dates(season)
-    else:
-        # Fallback to manual if ever used, or defaults
-        sowing = input_data.get('sowing_date', datetime.today().strftime('%Y-%m-%d'))
-        harvest = input_data.get('harvest_date', (datetime.today() + timedelta(days=120)).strftime('%Y-%m-%d'))
-
-    print(f"Analysis Period: {season} ({sowing} to {harvest})")
-
-    # 2. Fetch Historical/Season Weather Profile
-    weather_summary, weather_profile = get_historical_weather_summary(lat, lon, sowing, harvest)
-    print(f"Generated Weather Summary: {weather_summary[:100]}...")
-
-    prompt = f"""
-    You are an expert Agricultural Entomologist. Analyze the provided inputs and the MONTH-WISE WEATHER PROFILE to generate a precise Pest Outbreak Prediction.
-    
-    INPUTS:
-    - Crop: {input_data.get('crop_type')}
-    - Soil: {input_data.get('soil_type')}
-    - Season: {season} ({sowing} to {harvest})
-    - Location: {input_data.get('derived_location', 'Unknown')}
-    
-    WEATHER PROFILE (Month-by-Month):
-    {weather_summary}
-    
-    CRITICAL INSTRUCTION:
-    1. ANALYZE EACH MONTH SEPARATELY. Correlation: High Rainfall in Month 2 -> Risk of Fungal Diseases.
-    2. MULTIPLE PESTS: If a month has multiple risky pests, create SEPARATE entries for each pest in the table. Do not limit to just one per month.
-    3. LANGUAGE RULE: The JSON KEYS (e.g., "report_title", "pest_name") MUST REMAIN IN ENGLISH. Only translate the VALUES into '{language}'.
-    4. ACCURACY: Only predict pests that genuinely thrive in the given weather conditions. If a month is low risk, it is okay to have no pests for that month.
-    5. MONTH NAMING: The 'outbreak_months' field MUST contain the exact full English month names (e.g., "June", "July") to match the weather profile.
-    
-    Your response MUST be a single, valid JSON object and nothing else. Do not wrap it in markdown backticks.
-
-    This is the required JSON structure:
-    {{
-      "report_title": "Pest Outbreak Dashboard Report",
-      "location_info": {{
-        "latitude": "{lat}",
-        "longitude": "{lon}",
-        "derived_location": "A human-readable location derived from the coordinates (e.g., 'Nagpur, India')."
-      }},
-      "agricultural_inputs_analysis": "A detailed bullet-point analysis of how the chosen Season and Weather Profile impacts this specific crop.",
-      "pest_prediction_table": [
-        {{
-          "pest_name": "Name of the predicted pest",
-          "outbreak_months": "Predicted month(s) for the outbreak",
-          "severity": "Predicted severity level (e.g., Low, Medium, High)",
-          "impacting_stage": "The specific crop stage affected (e.g., Flowering, Vegetative)",
-          "potential_damage": "Short description of the damage caused (e.g., 'Causes dead hearts', 'Sucks sap leading to yellowing').",
-          "precautionary_measures": "A short description of key precautionary measures."
-        }}
-      ],
-      "pest_avoidance_practices": [
-        "A detailed, specific pest avoidance practice based on the inputs.",
-        "Another specific recommendation.",
-        "Provide 10-12 detailed bullet points."
-      ],
-      "agricultural_best_practices": [
-        "A specific agricultural best practice based on the inputs.",
-        "Another specific recommendation related to crop management."
-      ],
-      "predicted_pest_damage_info": "Detailed bullet points (markdown format using -) describing the potential damage the predicted pests could cause."
-    }}
-
-    Use the following data for your analysis:
-    - Location: {input_data.get('derived_location', 'Unknown')} (Lat: {input_data.get('latitude')}, Lon: {input_data.get('longitude')})
-    - Crop: {input_data.get('crop_type')}
-    - Sowing Date: {input_data.get('sowing_date')}
-    - Harvest Date: {input_data.get('harvest_date')}
-    - Current Growth Stage: {input_data.get('growth_stage')}
-    - Irrigation Frequency: {input_data.get('irrigation_freq')}
-    - Irrigation Method: {input_data.get('irrigation_method')}
-    - Soil Type: {input_data.get('soil_type')}
-    
-    --- SEASONAL WEATHER PROFILE (Aggregated Monthly Data) ---
-    {weather_summary}
-    ----------------------------------------------------------
-    """
-    
-    models_to_try = [
-            "gemini-3.0-flash",         # Requested
-            "gemini-2.5-flash",         # Requested 
-            "gemini-2.5-flash-lite",    # Requested
-            "gemma-3-27b-it",           # Requested
-            "gemma-3-12b-it",           # Requested
-            "gemma-3-4b-it",            # Requested
-            "gemma-3-1b-it",            # Requested
-            "gemini-2.0-flash-exp",     # Likely intended "modern" fallback
-    ]
-    
-    report_data = {} # Default
-    
-    for model_name in models_to_try:
-        try:
-            print(f"DEBUG: Attempting model {model_name}...")
-            response = client.models.generate_content(
-                model=model_name,
-                contents=prompt
-            )
-            
-            # Robust JSON extraction
-            raw_text = response.text
-            # print(f"DEBUG: Raw response: {raw_text[:100]}...") # Limit log size
-            
-            start_idx = raw_text.find('{')
-            end_idx = raw_text.rfind('}') + 1
-            
-            if start_idx != -1 and end_idx != 0:
-                json_text = raw_text[start_idx:end_idx]
-                report_data = json.loads(json_text)
-                print(f"DEBUG: Successfully parsed JSON from {model_name}")
-                break # Success
-            else:
-                 print(f"DEBUG: No JSON found in response from {model_name}")
-                 raise ValueError("No JSON definition found")
-
-        except json.JSONDecodeError as e:
-            print(f"CRITICAL: JSON parsing error with {model_name}: {e}")
-            continue
-        except Exception as e:
-            print(f"CRITICAL: Error calling {model_name}: {e}")
-            continue
-    
-    if not report_data:
-         print("DEBUG: All models failed to generate valid report.")
-         return {"error": "Failed to generate a valid report from the AI model. All fallback models failed. Please try again later."}, []
-         
-    return report_data, weather_profile
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    print("----- PREDICT ROUTE HIT -----")
-    form_data = request.form.to_dict()
-    print(f"Form Data Received: {form_data}")
-    language = form_data.get("language", "English")
-    
-    report_data, weather_profile = call_gemini_api(form_data, language)
-
-    # ... (Error handling remains similar but simplified for template)
-    if "error" in report_data:
-        return render_template('results.html', report_data={"report_title": "Error", "predicted_pest_damage_info": report_data['error']}, location={}, current_date=datetime.now().strftime("%B %d, %Y"), audio_url=None, language_code="en", weather_profile=[])
-
-    # Build the HTML report dynamically from the JSON data
-    location = report_data.get('location_info', {})
-
-    # Convert markdown to HTML in text fields
-    if 'agricultural_inputs_analysis' in report_data:
-        report_data['agricultural_inputs_analysis'] = markdown_to_html(report_data['agricultural_inputs_analysis'])
-    if 'predicted_pest_damage_info' in report_data:
-        report_data['predicted_pest_damage_info'] = markdown_to_html(report_data['predicted_pest_damage_info'])
-    if 'pest_prediction_table' in report_data:
-        for pest in report_data['pest_prediction_table']:
-            if 'precautionary_measures' in pest:
-                 pest['precautionary_measures'] = markdown_to_html(pest['precautionary_measures'])
-
-    # Generate summary for voice (short summary)
-    pest_table = report_data.get('pest_prediction_table', [])
-    summary = f"Pest Outbreak Report for {location.get('derived_location', 'your location')}. "
-    summary += (report_data.get('agricultural_inputs_analysis', '')[:200] + "... ")
-    if pest_table:
-        summary += f"Predicted pests: " + ', '.join([p.get('pest_name', '') for p in pest_table]) + ". "
-        summary += f"Severity: " + ', '.join([p.get('severity', '') for p in pest_table]) + ". "
-    summary += report_data.get('predicted_pest_damage_info', '')[:200]
-
-    # Generate audio file
-    lang_mapping = {
-        "English": "en", "Hindi": "hi", "Bengali": "bn", "Telugu": "te",
-        "Marathi": "mr", "Tamil": "ta", "Gujarati": "gu", "Urdu": "ur",
-        "Kannada": "kn", "Odia": "or", "Malayalam": "ml"
-    }
-    gtts_lang = lang_mapping.get(language, 'en')
-    
-    audio_url = None
-    try:
-        tts = gTTS(summary, lang=gtts_lang)
-        # Sanitize filename
-        safe_lat = str(location.get('latitude', '0')).replace('.', '_')
-        safe_lon = str(location.get('longitude', '0')).replace('.', '_')
-        audio_filename = f"pest_report_{safe_lat}_{safe_lon}.mp3"
-        
-        # Ensure directory exists
-        os.makedirs(app.config['AUDIO_FOLDER'], exist_ok=True)
-        
-        audio_path = os.path.join(app.config['AUDIO_FOLDER'], audio_filename)
-        tts.save(audio_path)
-        audio_url = f"/static/audio/{audio_filename}"
-    except Exception as e:
-        print(f"Error generating audio: {e}")
-
-    return render_template(
-        'results.html',
-        report_data=report_data,
-        location=location,
-        current_date=datetime.now().strftime("%B %d, %Y"),
-        audio_url=audio_url,
-        language_code=gtts_lang,
-        weather_profile=weather_profile
-    )
-
-@app.route('/get_timeline_weather', methods=['GET'])
-def get_timeline_weather():
-    """Returns the seasonal weather profile for the frontend timeline."""
-    lat = request.args.get('lat')
-    lon = request.args.get('lon')
-    start = request.args.get('start_date')
-    end = request.args.get('end_date')
-    
-    if not all([lat, lon, start, end]):
-        return jsonify({"error": "Missing parameters"}), 400
-        
-    _, profile = get_historical_weather_summary(lat, lon, start, end)
-    return jsonify(profile)
-
-
-
-if __name__ == '__main__':
+
+import requests
+import json
+from flask import Flask, render_template, request, jsonify, Response, send_file
+from google import genai
+from gtts import gTTS
+import os
+from dotenv import load_dotenv
+from datetime import datetime, timedelta
+
+# Load .env file
+load_dotenv()
+
+app = Flask(__name__)
+app.config['AUDIO_FOLDER'] = 'static/audio'
+os.makedirs(app.config['AUDIO_FOLDER'], exist_ok=True)
+
+def markdown_to_html(text):
+    """Convert markdown text to HTML for proper rendering."""
+    if not text:
+        return text
+    import markdown as md
+    return md.markdown(text, extensions=['nl2br'])
+
+# IMPORTANT: Replace with your actual Gemini API key
+api_key = os.getenv('GEMINI_API_KEY')
+if not api_key:
+  api_key = os.getenv('GEMINI_API_KEY_1')
+if not api_key:
+  api_key = os.getenv('GEMINI_API_KEY_2')
+
+if not api_key:
+    raise ValueError("GEMINI_API_KEY is not set. Please add it to your .env file.")
+  
+print(f"Initializing Gemini client with API key: {api_key[:10]}...")
+client = genai.Client(api_key=api_key)
+def validate_coordinates(lat, lon):
+    """Validate and convert latitude and longitude to float."""
+    try:
+        return float(lat), float(lon)
+    except (TypeError, ValueError):
+        return None, None
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/get_weather_data', methods=['GET'])
+def get_weather_data():
+    """
+    Fetch weather data using Open-Meteo's forecast endpoint.
+    """
+    lat = request.args.get('lat')
+    lon = request.args.get('lon')
+    lat, lon = validate_coordinates(lat, lon)
+    if lat is None or lon is None:
+        return jsonify({"error": "Invalid coordinates"}), 400
+    
+    try:
+        forecast_url = "https://api.open-meteo.com/v1/forecast"
+        forecast_params = {
+            "latitude": lat,
+            "longitude": lon,
+            "current_weather": "true",
+            "daily": "temperature_2m_max,temperature_2m_min,precipitation_sum",
+            "hourly": "relative_humidity_2m,soil_moisture_3_to_9cm,cloudcover,windspeed_10m",
+            "timezone": "auto"
+        }
+        resp = requests.get(forecast_url, params=forecast_params)
+        resp.raise_for_status()
+        data = resp.json()
+
+        daily   = data.get("daily", {})
+        hourly  = data.get("hourly", {})
+        current = data.get("current_weather", {})
+
+        # Daily data
+        max_temp = daily.get("temperature_2m_max", [None])[0]
+        min_temp = daily.get("temperature_2m_min", [None])[0]
+        rain     = daily.get("precipitation_sum", [None])[0]
+
+        # Hourly data (averages)
+        humidity_list     = hourly.get("relative_humidity_2m", [])
+        soil_list         = hourly.get("soil_moisture_3_to_9cm", [])
+        cloud_list        = hourly.get("cloudcover", [])
+        
+        avg_humidity      = sum(humidity_list)/len(humidity_list) if humidity_list else None
+        avg_soil_moisture = sum(soil_list)/len(soil_list) if soil_list else None
+        avg_cloud_cover   = sum(cloud_list)/len(cloud_list) if cloud_list else None
+
+        # Current weather
+        current_temp = current.get("temperature")
+        wind_speed   = current.get("windspeed")
+
+        weather = {
+            "max_temp": max_temp, "min_temp": min_temp, "rainfall": rain,
+            "humidity": avg_humidity, "soil_moisture": avg_soil_moisture,
+            "current_temp": current_temp, "wind_speed": wind_speed, "cloud_cover": avg_cloud_cover
+        }
+        return jsonify(weather)
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+def get_historical_weather_summary(lat, lon, start_date_str, end_date_str):
+    """
+    Fetches historical weather data from Open-Meteo Archive for the specified period.
+    If period is in future, shifts to previous year.
+    Returns a text summary of monthly averages.
+    """
+    try:
+        if not start_date_str or not end_date_str:
+            return "Weather data unavailable (dates missing)."
+
+        start = datetime.strptime(start_date_str, '%Y-%m-%d')
+        end = datetime.strptime(end_date_str, '%Y-%m-%d')
+        today = datetime.now()
+
+        # Logic: If start date is in future, use last year's data as proxy
+        is_proxy = False
+        if start > today:
+            start = start.replace(year=start.year - 1)
+            end = end.replace(year=end.year - 1)
+            is_proxy = True
+        
+        # Ensure we don't query future for the archive (e.g. if harvest is next month)
+        # If the *adjusted* end date is still after today (rare if we shifted year, but possible), clip it.
+        if end > today:
+             end = today
+
+        # Call Open-Meteo Archive
+        archive_url = "https://archive-api.open-meteo.com/v1/archive"
+        params = {
+            "latitude": lat,
+            "longitude": lon,
+            "start_date": start.strftime('%Y-%m-%d'),
+            "end_date": end.strftime('%Y-%m-%d'),
+            "daily": "temperature_2m_max,temperature_2m_min,precipitation_sum,relative_humidity_2m_mean",
+            "timezone": "auto"
+        }
+        
+        resp = requests.get(archive_url, params=params)
+        
+        if resp.status_code != 200:
+             print(f"DEBUG: API Failed {resp.status_code} - {resp.text}")
+             return f"Could not fetch weather data: {resp.status_code}", []
+             
+        data = resp.json()
+        daily = data.get("daily", {})
+        
+        dates = daily.get("time", [])
+        
+        print(f"DEBUG: Retrieved {len(dates)} days.")
+        if dates:
+            print(f"DEBUG: Range {dates[0]} to {dates[-1]}")
+        
+        if not dates:
+            return "No weather data available for this range.", []
+
+        summary_parts = []
+        structured_data = [] 
+        if is_proxy:
+             summary_parts.append("(Note: Using last year's weather as proxy for future dates)")
+
+        # Simple aggregation loop
+        current_month = None
+        temp_sum = 0
+        hum_sum = 0
+        rain_sum = 0
+        count = 0
+        
+        # Extract lists
+        max_temps = daily.get("temperature_2m_max", [])
+        humidities = daily.get("relative_humidity_2m_mean", [])
+        rains = daily.get("precipitation_sum", [])
+
+
+        # Simple aggregation loop
+        current_month = None
+        temp_sum = 0
+        hum_sum = 0
+        rain_sum = 0
+        count = 0
+        month_days = []
+        
+        for i, d_str in enumerate(dates):
+            date_obj = datetime.strptime(d_str, '%Y-%m-%d')
+            month_key = date_obj.strftime('%B %Y')
+            
+            # Accumulate values (handle None)
+            t = max_temps[i] if max_temps[i] is not None else 0
+            h = humidities[i] if humidities[i] is not None else 0
+            r = rains[i] if rains[i] is not None else 0
+            
+            if current_month is None: current_month = month_key
+            
+            if month_key != current_month:
+                # Flush previous month
+                avg_t = temp_sum / count if count else 0
+                avg_h = hum_sum / count if count else 0
+                summary_parts.append(f"{current_month}: Avg Temp {avg_t:.1f}C, Rain {rain_sum:.1f}mm, Humidity {avg_h:.1f}%")
+                
+                # Add to structured data
+                structured_data.append({
+                    "month": current_month,
+                    "avg_temp": round(avg_t, 1),
+                    "rainfall": round(rain_sum, 1),
+                    "humidity": round(avg_h, 1),
+                    "days": month_days
+                })
+                
+                # Reset
+                current_month = month_key
+                temp_sum = 0; hum_sum = 0; rain_sum = 0; count = 0
+                month_days = []
+            
+            # Accumulate
+            temp_sum += t
+            hum_sum += h
+            rain_sum += r
+            count += 1
+            
+            # Add to daily list
+            month_days.append({
+                "date": d_str,
+                "temp": t,
+                "humidity": h,
+                "rain": r
+            })
+            
+        # Flush last month
+        if count > 0:
+            avg_t = temp_sum / count
+            avg_h = hum_sum / count
+            summary_parts.append(f"{current_month}: Avg Temp {avg_t:.1f}C, Rain {rain_sum:.1f}mm, Humidity {avg_h:.1f}%")
+            structured_data.append({
+                "month": current_month,
+                "avg_temp": round(avg_t, 1),
+                "rainfall": round(rain_sum, 1),
+                "humidity": round(avg_h, 1),
+                "days": month_days
+            })
+
+        return "\n".join(summary_parts), structured_data
+
+    except Exception as e:
+        print(f"Weather Fetch Error: {e}")
+        return "Weather data processing failed.", []
+
+def calculate_season_dates(season_name):
+    """
+    Derives standard sowing and harvest dates based on the Indian agricultural season.
+    """
+    today = datetime.today()
+    current_year = today.year
+    
+    # Defaults
+    sowing_date = today
+    harvest_date = today + timedelta(days=120)
+
+    try:
+        if season_name == "Kharif":
+            # June 15 to Oct 15
+            sowing_date = datetime(current_year, 6, 15)
+            harvest_date = datetime(current_year, 10, 15)
+            # If we are past Oct, maybe predicts for next year? 
+            # For simplicity, assume current year context or next if late.
+            if today.month > 10:
+                sowing_date = datetime(current_year + 1, 6, 15)
+                harvest_date = datetime(current_year + 1, 10, 15)
+
+        elif season_name == "Rabi":
+            # Nov 1 to April 1 (Crosses year boundary)
+            if today.month > 4: 
+                # Predicting for upcoming Rabi
+                sowing_date = datetime(current_year, 11, 1)
+                harvest_date = datetime(current_year + 1, 4, 1)
+            else:
+                # We are IN Rabi or just past
+                sowing_date = datetime(current_year - 1, 11, 1)
+                harvest_date = datetime(current_year, 4, 1)
+
+        elif season_name == "Zaid":
+            # March 1 to June 1
+            sowing_date = datetime(current_year, 3, 1)
+            harvest_date = datetime(current_year, 6, 1)
+            if today.month > 6:
+                sowing_date = datetime(current_year + 1, 3, 1)
+                harvest_date = datetime(current_year + 1, 6, 1)
+
+        elif season_name == "Annual":
+            # 1 Year cycle
+            sowing_date = today
+            harvest_date = today + timedelta(days=365)
+            
+    except Exception as e:
+        print(f"Date Calc Error: {e}")
+
+    return sowing_date.strftime('%Y-%m-%d'), harvest_date.strftime('%Y-%m-%d')
+
+def call_gemini_api(input_data, language):
+    """
+    Calls the Gemini API to get a pest outbreak report in a structured JSON format.
+    Implements fallback mechanism to try multiple models if primary fails.
+    """
+    
+    # 1. Logic: Determine Dates from Season
+    lat = input_data.get('latitude')
+    lon = input_data.get('longitude')
+    season = input_data.get('season')
+    
+    # Auto-calculate dates based on Season
+    if season:
+        sowing, harvest = calculate_season_dates(season)
+    else:
+        # Fallback to manual if ever used, or defaults
+        sowing = input_data.get('sowing_date', datetime.today().strftime('%Y-%m-%d'))
+        harvest = input_data.get('harvest_date', (datetime.today() + timedelta(days=120)).strftime('%Y-%m-%d'))
+
+    print(f"Analysis Period: {season} ({sowing} to {harvest})")
+
+    # 2. Fetch Historical/Season Weather Profile
+    weather_summary, weather_profile = get_historical_weather_summary(lat, lon, sowing, harvest)
+    print(f"Generated Weather Summary: {weather_summary[:100]}...")
+
+    prompt = f"""
+    You are an expert Agricultural Entomologist. Analyze the provided inputs and the MONTH-WISE WEATHER PROFILE to generate a precise Pest Outbreak Prediction.
+    
+    INPUTS:
+    - Crop: {input_data.get('crop_type')}
+    - Soil: {input_data.get('soil_type')}
+    - Season: {season} ({sowing} to {harvest})
+    - Location: {input_data.get('derived_location', 'Unknown')}
+    
+    WEATHER PROFILE (Month-by-Month):
+    {weather_summary}
+    
+    CRITICAL INSTRUCTION:
+    1. ANALYZE EACH MONTH SEPARATELY. Correlation: High Rainfall in Month 2 -> Risk of Fungal Diseases.
+    2. MULTIPLE PESTS: If a month has multiple risky pests, create SEPARATE entries for each pest in the table. Do not limit to just one per month.
+    3. LANGUAGE RULE: The JSON KEYS (e.g., "report_title", "pest_name") MUST REMAIN IN ENGLISH. Only translate the VALUES into '{language}'.
+    4. ACCURACY: Only predict pests that genuinely thrive in the given weather conditions. If a month is low risk, it is okay to have no pests for that month.
+    5. MONTH NAMING: The 'outbreak_months' field MUST contain the exact full English month names (e.g., "June", "July") to match the weather profile.
+    
+    Your response MUST be a single, valid JSON object and nothing else. Do not wrap it in markdown backticks.
+
+    This is the required JSON structure:
+    {{
+      "report_title": "Pest Outbreak Dashboard Report",
+      "location_info": {{
+        "latitude": "{lat}",
+        "longitude": "{lon}",
+        "derived_location": "A human-readable location derived from the coordinates (e.g., 'Nagpur, India')."
+      }},
+      "agricultural_inputs_analysis": "A detailed bullet-point analysis of how the chosen Season and Weather Profile impacts this specific crop.",
+      "pest_prediction_table": [
+        {{
+          "pest_name": "Name of the predicted pest",
+          "outbreak_months": "Predicted month(s) for the outbreak",
+          "severity": "Predicted severity level (e.g., Low, Medium, High)",
+          "impacting_stage": "The specific crop stage affected (e.g., Flowering, Vegetative)",
+          "potential_damage": "Short description of the damage caused (e.g., 'Causes dead hearts', 'Sucks sap leading to yellowing').",
+          "precautionary_measures": "A short description of key precautionary measures."
+        }}
+      ],
+      "pest_avoidance_practices": [
+        "A detailed, specific pest avoidance practice based on the inputs.",
+        "Another specific recommendation.",
+        "Provide 10-12 detailed bullet points."
+      ],
+      "agricultural_best_practices": [
+        "A specific agricultural best practice based on the inputs.",
+        "Another specific recommendation related to crop management."
+      ],
+      "predicted_pest_damage_info": "Detailed bullet points (markdown format using -) describing the potential damage the predicted pests could cause."
+    }}
+
+    Use the following data for your analysis:
+    - Location: {input_data.get('derived_location', 'Unknown')} (Lat: {input_data.get('latitude')}, Lon: {input_data.get('longitude')})
+    - Crop: {input_data.get('crop_type')}
+    - Sowing Date: {input_data.get('sowing_date')}
+    - Harvest Date: {input_data.get('harvest_date')}
+    - Current Growth Stage: {input_data.get('growth_stage')}
+    - Irrigation Frequency: {input_data.get('irrigation_freq')}
+    - Irrigation Method: {input_data.get('irrigation_method')}
+    - Soil Type: {input_data.get('soil_type')}
+    
+    --- SEASONAL WEATHER PROFILE (Aggregated Monthly Data) ---
+    {weather_summary}
+    ----------------------------------------------------------
+    """
+    
+    models_to_try = [         
+            "gemini-2.5-flash",         # Requested 
+            "gemini-2.5-flash-lite",    # Requested
+            "gemma-3-27b-it",           # Requested
+            "gemma-3-12b-it",           # Requested
+            "gemma-3-4b-it",            # Requested
+            "gemma-3-1b-it",            # Requested
+            "gemini-2.0-flash-exp",     # Likely intended "modern" fallback
+    ]
+    
+    report_data = {} # Default
+    
+    for model_name in models_to_try:
+        try:
+            print(f"DEBUG: Attempting model {model_name}...")
+            response = client.models.generate_content(
+                model=model_name,
+                contents=prompt
+            )
+            
+            # Robust JSON extraction
+            raw_text = response.text
+            # print(f"DEBUG: Raw response: {raw_text[:100]}...") # Limit log size
+            
+            start_idx = raw_text.find('{')
+            end_idx = raw_text.rfind('}') + 1
+            
+            if start_idx != -1 and end_idx != 0:
+                json_text = raw_text[start_idx:end_idx]
+                report_data = json.loads(json_text)
+                print(f"DEBUG: Successfully parsed JSON from {model_name}")
+                break # Success
+            else:
+                 print(f"DEBUG: No JSON found in response from {model_name}")
+                 raise ValueError("No JSON definition found")
+
+        except json.JSONDecodeError as e:
+            print(f"CRITICAL: JSON parsing error with {model_name}: {e}")
+            continue
+        except Exception as e:
+            print(f"CRITICAL: Error calling {model_name}: {e}")
+            continue
+    
+    if not report_data:
+         print("DEBUG: All models failed to generate valid report.")
+         return {"error": "Failed to generate a valid report from the AI model. All fallback models failed. Please try again later."}, []
+         
+    return report_data, weather_profile
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    print("----- PREDICT ROUTE HIT -----")
+    form_data = request.form.to_dict()
+    print(f"Form Data Received: {form_data}")
+    language = form_data.get("language", "English")
+    
+    report_data, weather_profile = call_gemini_api(form_data, language)
+
+    # ... (Error handling remains similar but simplified for template)
+    if "error" in report_data:
+        return render_template('results.html', report_data={"report_title": "Error", "predicted_pest_damage_info": report_data['error']}, location={}, current_date=datetime.now().strftime("%B %d, %Y"), audio_url=None, language_code="en", weather_profile=[])
+
+    # Build the HTML report dynamically from the JSON data
+    location = report_data.get('location_info', {})
+
+    # Convert markdown to HTML in text fields
+    if 'agricultural_inputs_analysis' in report_data:
+        report_data['agricultural_inputs_analysis'] = markdown_to_html(report_data['agricultural_inputs_analysis'])
+    if 'predicted_pest_damage_info' in report_data:
+        report_data['predicted_pest_damage_info'] = markdown_to_html(report_data['predicted_pest_damage_info'])
+    if 'pest_prediction_table' in report_data:
+        for pest in report_data['pest_prediction_table']:
+            if 'precautionary_measures' in pest:
+                 pest['precautionary_measures'] = markdown_to_html(pest['precautionary_measures'])
+
+    # Generate summary for voice (short summary)
+    pest_table = report_data.get('pest_prediction_table', [])
+    summary = f"Pest Outbreak Report for {location.get('derived_location', 'your location')}. "
+    summary += (report_data.get('agricultural_inputs_analysis', '')[:200] + "... ")
+    if pest_table:
+        summary += f"Predicted pests: " + ', '.join([p.get('pest_name', '') for p in pest_table]) + ". "
+        summary += f"Severity: " + ', '.join([p.get('severity', '') for p in pest_table]) + ". "
+    summary += report_data.get('predicted_pest_damage_info', '')[:200]
+
+    # Generate audio file
+    lang_mapping = {
+        "English": "en", "Hindi": "hi", "Bengali": "bn", "Telugu": "te",
+        "Marathi": "mr", "Tamil": "ta", "Gujarati": "gu", "Urdu": "ur",
+        "Kannada": "kn", "Odia": "or", "Malayalam": "ml"
+    }
+    gtts_lang = lang_mapping.get(language, 'en')
+    
+    audio_url = None
+    try:
+        tts = gTTS(summary, lang=gtts_lang)
+        # Sanitize filename
+        safe_lat = str(location.get('latitude', '0')).replace('.', '_')
+        safe_lon = str(location.get('longitude', '0')).replace('.', '_')
+        audio_filename = f"pest_report_{safe_lat}_{safe_lon}.mp3"
+        
+        # Ensure directory exists
+        os.makedirs(app.config['AUDIO_FOLDER'], exist_ok=True)
+        
+        audio_path = os.path.join(app.config['AUDIO_FOLDER'], audio_filename)
+        tts.save(audio_path)
+        audio_url = f"/static/audio/{audio_filename}"
+    except Exception as e:
+        print(f"Error generating audio: {e}")
+
+    return render_template(
+        'results.html',
+        report_data=report_data,
+        location=location,
+        current_date=datetime.now().strftime("%B %d, %Y"),
+        audio_url=audio_url,
+        language_code=gtts_lang,
+        weather_profile=weather_profile
+    )
+
+@app.route('/get_timeline_weather', methods=['GET'])
+def get_timeline_weather():
+    """Returns the seasonal weather profile for the frontend timeline."""
+    lat = request.args.get('lat')
+    lon = request.args.get('lon')
+    start = request.args.get('start_date')
+    end = request.args.get('end_date')
+    
+    if not all([lat, lon, start, end]):
+        return jsonify({"error": "Missing parameters"}), 400
+        
+    _, profile = get_historical_weather_summary(lat, lon, start, end)
+    return jsonify(profile)
+
+
+
+if __name__ == '__main__':
     app.run(debug=True, port=5001)