Update app.py

app.py

@@ -1,231 +1,231 @@
-from flask import Flask, render_template, request
-import joblib
-import pandas as pd
-import google.generativeai as genai
-from openai import OpenAI
-import os
-import time
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-app = Flask(__name__)
-
-# Load the trained Random Forest models
-rf_ferti_name = joblib.load('rf_ferti_name.pkl')
-rf_ferti_value = joblib.load('rf_ferti_value.pkl')
-
-# Manually define the encodings based on the provided dictionaries
-soil_type_encodings = {'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4}
-crop_type_encodings = {'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,
-                       'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,
-                       'Tobacco': 10, 'Wheat': 11}
-fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,
-                             '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,
-                             'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,
-                             'Potassium chloride/Muriate of potash (MOP)': 13, 'Potassium sulfate/Sulfate of potash (SOP)': 14,
-                             'Rock phosphate (RP)': 15, 'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,
-                             'Urea': 18, 'Zinc sulfate': 19}
-
-# --- ENHANCED LLM CONFIGURATION ---
-GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
-NVIDIA_API_KEY = os.getenv('NVIDIA_API_KEY')
-
-if GEMINI_API_KEY:
-    genai.configure(api_key=GEMINI_API_KEY)
-
-# Model configurations with retry settings
-GEMINI_MODELS = [
-    {"name": "gemini-2.0-flash-exp", "max_retries": 2, "timeout": 30, "description": "Latest experimental"},
-    {"name": "gemini-1.5-pro-latest", "max_retries": 2, "timeout": 45, "description": "Most capable"},
-    {"name": "gemini-1.5-flash", "max_retries": 3, "timeout": 20, "description": "Fast and reliable"},
-    {"name": "gemini-1.5-flash-8b", "max_retries": 3, "timeout": 15, "description": "Lightweight"},
-]
-
-NVIDIA_MODELS = [
-    {"name": "meta/llama-3.2-90b-vision-instruct", "max_retries": 2, "timeout": 40, "description": "High capability"},
-    {"name": "meta/llama-3.2-11b-vision-instruct", "max_retries": 2, "timeout": 30, "description": "Balanced"},
-]
-
-
-def retry_with_backoff(func, max_retries=3, initial_delay=1):
-    """Retry a function with exponential backoff."""
-    for attempt in range(max_retries):
-        try:
-            return func()
-        except Exception as e:
-            if attempt == max_retries - 1:
-                raise
-            delay = initial_delay * (2 ** attempt)
-            print(f"  >> Retry {attempt + 1}/{max_retries} after {delay}s (Error: {type(e).__name__})")
-            time.sleep(delay)
-
-
-def generate_with_gemini(prompt, model_config):
-    """Generate text using a specific Gemini model with retry logic."""
-    model_name = model_config["name"]
-    max_retries = model_config.get("max_retries", 2)
-    
-    def _attempt():
-        print(f"  >> Attempting Gemini: {model_name}")
-        model = genai.GenerativeModel(model_name)
-        response = model.generate_content(prompt)
-        
-        if not response or not response.text:
-            raise ValueError("Empty response from model")
-        
-        return response.text
-    
-    try:
-        return retry_with_backoff(_attempt, max_retries=max_retries)
-    except Exception as e:
-        print(f"  >> FAILED {model_name}: {type(e).__name__}")
-        return None
-
-
-def generate_with_nvidia(prompt, model_config):
-    """Generate text using NVIDIA API with retry logic."""
-    if not NVIDIA_API_KEY:
-        return None
-    
-    model_name = model_config["name"]
-    max_retries = model_config.get("max_retries", 2)
-    
-    def _attempt():
-        print(f"  >> Attempting NVIDIA: {model_name}")
-        client = OpenAI(
-            base_url="https://integrate.api.nvidia.com/v1",
-            api_key=NVIDIA_API_KEY
-        )
-        
-        completion = client.chat.completions.create(
-            model=model_name,
-            messages=[{"role": "user", "content": prompt}],
-            max_tokens=500,
-            temperature=0.7
-        )
-        
-        response_text = completion.choices[0].message.content
-        if not response_text:
-            raise ValueError("Empty response from NVIDIA")
-        
-        return response_text
-    
-    try:
-        return retry_with_backoff(_attempt, max_retries=max_retries)
-    except Exception as e:
-        print(f"  >> FAILED NVIDIA {model_name}: {type(e).__name__}")
-        return None
-
-
-def generate_ai_suggestions(pred_fertilizer_name):
-    """Generate AI suggestions with enhanced fallback system."""
-    print("\n" + "=" * 60)
-    print(f"🌱 GENERATING AI SUGGESTIONS FOR: {pred_fertilizer_name}")
-    print("=" * 60)
-    
-    prompt = (
-        f"For {pred_fertilizer_name} fertilizer, generate 3-4 sentences each on a new line. "
-        f"Text should be justified and should not contain any special characters."
-    )
-    
-    response_text = None
-    used_model = "None"
-    
-    # PHASE 1: Try Gemini models
-    if GEMINI_API_KEY:
-        print("\n--- PHASE 1: Trying Gemini Models ---")
-        for idx, model_config in enumerate(GEMINI_MODELS, 1):
-            print(f"[{idx}/{len(GEMINI_MODELS)}] Testing {model_config['name']}...")
-            response_text = generate_with_gemini(prompt, model_config)
-            
-            if response_text:
-                used_model = f"Gemini-{model_config['name']}"
-                print(f"  ✓ SUCCESS with {used_model}")
-                break
-    
-    # PHASE 2: Try NVIDIA models (fallback)
-    if not response_text and NVIDIA_API_KEY:
-        print("\n--- PHASE 2: Trying NVIDIA Models (Fallback) ---")
-        for idx, model_config in enumerate(NVIDIA_MODELS, 1):
-            print(f"[{idx}/{len(NVIDIA_MODELS)}] Testing {model_config['name']}...")
-            response_text = generate_with_nvidia(prompt, model_config)
-            
-            if response_text:
-                used_model = f"NVIDIA-{model_config['name']}"
-                print(f"  ✓ SUCCESS with {used_model}")
-                break
-    
-    # PHASE 3: Final fallback
-    if not response_text:
-        print("\n❌ All LLM providers failed. Using fallback text.")
-        response_text = (
-            f"{pred_fertilizer_name} is a commonly used fertilizer in agriculture. "
-            f"It provides essential nutrients to crops. "
-            f"Follow recommended dosage for best results. "
-            f"Consult local agricultural experts for specific guidance."
-        )
-        used_model = "Fallback"
-    
-    print(f"\n✅ Generated using: {used_model}")
-    print("=" * 60 + "\n")
-    
-    return response_text
-
-
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    if request.method == 'POST':
-        # Retrieve form data
-        temperature = float(request.form['temperature'])
-        humidity = float(request.form['humidity'])
-        moisture = float(request.form['moisture'])
-        soil_type = request.form['soil_type']
-        crop_type = request.form['crop_type']
-        nitrogen = float(request.form['nitrogen'])
-        potassium = float(request.form['potassium'])
-        phosphorous = float(request.form['phosphorous'])
-
-        # Encode categorical data
-        soil_type_encoded = soil_type_encodings.get(soil_type, -1)
-        crop_type_encoded = crop_type_encodings.get(crop_type, -1)
-
-        # Create a DataFrame for the input
-        user_input = pd.DataFrame({
-            'Temperature': [temperature],
-            'Humidity': [humidity],
-            'Moisture': [moisture],
-            'Nitrogen': [nitrogen],
-            'Potassium': [potassium],
-            'Phosphorous': [phosphorous],
-            'Soil Type': [soil_type_encoded],
-            'Crop Type': [crop_type_encoded]
-        })
-
-        # Predict Fertilizer Name
-        pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]
-        pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]
-
-        # Predict Fertilizer Quantity
-        pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]
-        
-        # Generate AI suggestions with fallback system
-        pred_info = generate_ai_suggestions(pred_fertilizer_name)
-
-        return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,
-                               fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty, pred_info=pred_info)
-    return render_template('index.html', prediction=False)
-
-
-if __name__ == '__main__':
-    print("\n" + "=" * 60)
-    print("🚀 Starting Fertilizer Recommendation App")
-    print("=" * 60)
-    print(f"📊 Configuration:")
-    print(f"  - Gemini API: {'✓ Configured' if GEMINI_API_KEY else '✗ Not configured'}")
-    print(f"  - NVIDIA API: {'✓ Configured' if NVIDIA_API_KEY else '✗ Not configured'}")
-    print(f"  - Gemini Models: {len(GEMINI_MODELS)}")
-    print(f"  - NVIDIA Models: {len(NVIDIA_MODELS)}")
-    print("=" * 60 + "\n")
-    app.run(port=7860, host='0.0.0.0')
+from flask import Flask, render_template, request
+import joblib
+import pandas as pd
+import google.generativeai as genai
+from openai import OpenAI
+import os
+import time
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+app = Flask(__name__)
+
+# Load the trained Random Forest models
+rf_ferti_name = joblib.load('rf_ferti_name.pkl')
+rf_ferti_value = joblib.load('rf_ferti_value.pkl')
+
+# Manually define the encodings based on the provided dictionaries
+soil_type_encodings = {'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4}
+crop_type_encodings = {'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,
+                       'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,
+                       'Tobacco': 10, 'Wheat': 11}
+fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,
+                             '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,
+                             'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,
+                             'Potassium chloride/Muriate of potash (MOP)': 13, 'Potassium sulfate/Sulfate of potash (SOP)': 14,
+                             'Rock phosphate (RP)': 15, 'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,
+                             'Urea': 18, 'Zinc sulfate': 19}
+
+# --- ENHANCED LLM CONFIGURATION ---
+GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
+NVIDIA_API_KEY = os.getenv('NVIDIA_API_KEY')
+
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+
+# Model configurations with retry settings
+GEMINI_MODELS = [
+    {"name": "gemini-2.0-flash-exp", "max_retries": 2, "timeout": 30, "description": "Latest experimental"},
+    {"name": "gemini-1.5-pro-latest", "max_retries": 2, "timeout": 45, "description": "Most capable"},
+    {"name": "gemini-1.5-flash", "max_retries": 3, "timeout": 20, "description": "Fast and reliable"},
+    {"name": "gemini-1.5-flash-8b", "max_retries": 3, "timeout": 15, "description": "Lightweight"},
+]
+
+NVIDIA_MODELS = [
+    {"name": "meta/llama-3.2-90b-vision-instruct", "max_retries": 2, "timeout": 40, "description": "High capability"},
+    {"name": "meta/llama-3.2-11b-vision-instruct", "max_retries": 2, "timeout": 30, "description": "Balanced"},
+]
+
+
+def retry_with_backoff(func, max_retries=3, initial_delay=1):
+    """Retry a function with exponential backoff."""
+    for attempt in range(max_retries):
+        try:
+            return func()
+        except Exception as e:
+            if attempt == max_retries - 1:
+                raise
+            delay = initial_delay * (2 ** attempt)
+            print(f"  >> Retry {attempt + 1}/{max_retries} after {delay}s (Error: {type(e).__name__})")
+            time.sleep(delay)
+
+
+def generate_with_gemini(prompt, model_config):
+    """Generate text using a specific Gemini model with retry logic."""
+    model_name = model_config["name"]
+    max_retries = model_config.get("max_retries", 2)
+    
+    def _attempt():
+        print(f"  >> Attempting Gemini: {model_name}")
+        model = genai.GenerativeModel(model_name)
+        response = model.generate_content(prompt)
+        
+        if not response or not response.text:
+            raise ValueError("Empty response from model")
+        
+        return response.text
+    
+    try:
+        return retry_with_backoff(_attempt, max_retries=max_retries)
+    except Exception as e:
+        print(f"  >> FAILED {model_name}: {type(e).__name__}")
+        return None
+
+
+def generate_with_nvidia(prompt, model_config):
+    """Generate text using NVIDIA API with retry logic."""
+    if not NVIDIA_API_KEY:
+        return None
+    
+    model_name = model_config["name"]
+    max_retries = model_config.get("max_retries", 2)
+    
+    def _attempt():
+        print(f"  >> Attempting NVIDIA: {model_name}")
+        client = OpenAI(
+            base_url="https://integrate.api.nvidia.com/v1",
+            api_key=NVIDIA_API_KEY
+        )
+        
+        completion = client.chat.completions.create(
+            model=model_name,
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=500,
+            temperature=0.7
+        )
+        
+        response_text = completion.choices[0].message.content
+        if not response_text:
+            raise ValueError("Empty response from NVIDIA")
+        
+        return response_text
+    
+    try:
+        return retry_with_backoff(_attempt, max_retries=max_retries)
+    except Exception as e:
+        print(f"  >> FAILED NVIDIA {model_name}: {type(e).__name__}")
+        return None
+
+
+def generate_ai_suggestions(pred_fertilizer_name):
+    """Generate AI suggestions with enhanced fallback system."""
+    print("\n" + "=" * 60)
+    print(f"🌱 GENERATING AI SUGGESTIONS FOR: {pred_fertilizer_name}")
+    print("=" * 60)
+    
+    prompt = (
+        f"For {pred_fertilizer_name} fertilizer, generate 3-4 Short Informative sentences each on a new line. Content should not be very big max to max 4 sentence thats all okay"
+        f"Text should be justified and should not contain any special characters."
+    )
+    
+    response_text = None
+    used_model = "None"
+    
+    # PHASE 1: Try Gemini models
+    if GEMINI_API_KEY:
+        print("\n--- PHASE 1: Trying Gemini Models ---")
+        for idx, model_config in enumerate(GEMINI_MODELS, 1):
+            print(f"[{idx}/{len(GEMINI_MODELS)}] Testing {model_config['name']}...")
+            response_text = generate_with_gemini(prompt, model_config)
+            
+            if response_text:
+                used_model = f"Gemini-{model_config['name']}"
+                print(f"  ✓ SUCCESS with {used_model}")
+                break
+    
+    # PHASE 2: Try NVIDIA models (fallback)
+    if not response_text and NVIDIA_API_KEY:
+        print("\n--- PHASE 2: Trying NVIDIA Models (Fallback) ---")
+        for idx, model_config in enumerate(NVIDIA_MODELS, 1):
+            print(f"[{idx}/{len(NVIDIA_MODELS)}] Testing {model_config['name']}...")
+            response_text = generate_with_nvidia(prompt, model_config)
+            
+            if response_text:
+                used_model = f"NVIDIA-{model_config['name']}"
+                print(f"  ✓ SUCCESS with {used_model}")
+                break
+    
+    # PHASE 3: Final fallback
+    if not response_text:
+        print("\n❌ All LLM providers failed. Using fallback text.")
+        response_text = (
+            f"{pred_fertilizer_name} is a commonly used fertilizer in agriculture. "
+            f"It provides essential nutrients to crops. "
+            f"Follow recommended dosage for best results. "
+            f"Consult local agricultural experts for specific guidance."
+        )
+        used_model = "Fallback"
+    
+    print(f"\n✅ Generated using: {used_model}")
+    print("=" * 60 + "\n")
+    
+    return response_text
+
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        # Retrieve form data
+        temperature = float(request.form['temperature'])
+        humidity = float(request.form['humidity'])
+        moisture = float(request.form['moisture'])
+        soil_type = request.form['soil_type']
+        crop_type = request.form['crop_type']
+        nitrogen = float(request.form['nitrogen'])
+        potassium = float(request.form['potassium'])
+        phosphorous = float(request.form['phosphorous'])
+
+        # Encode categorical data
+        soil_type_encoded = soil_type_encodings.get(soil_type, -1)
+        crop_type_encoded = crop_type_encodings.get(crop_type, -1)
+
+        # Create a DataFrame for the input
+        user_input = pd.DataFrame({
+            'Temperature': [temperature],
+            'Humidity': [humidity],
+            'Moisture': [moisture],
+            'Nitrogen': [nitrogen],
+            'Potassium': [potassium],
+            'Phosphorous': [phosphorous],
+            'Soil Type': [soil_type_encoded],
+            'Crop Type': [crop_type_encoded]
+        })
+
+        # Predict Fertilizer Name
+        pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]
+        pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]
+
+        # Predict Fertilizer Quantity
+        pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]
+        
+        # Generate AI suggestions with fallback system
+        pred_info = generate_ai_suggestions(pred_fertilizer_name)
+
+        return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,
+                               fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty, pred_info=pred_info)
+    return render_template('index.html', prediction=False)
+
+
+if __name__ == '__main__':
+    print("\n" + "=" * 60)
+    print("🚀 Starting Fertilizer Recommendation App")
+    print("=" * 60)
+    print(f"📊 Configuration:")
+    print(f"  - Gemini API: {'✓ Configured' if GEMINI_API_KEY else '✗ Not configured'}")
+    print(f"  - NVIDIA API: {'✓ Configured' if NVIDIA_API_KEY else '✗ Not configured'}")
+    print(f"  - Gemini Models: {len(GEMINI_MODELS)}")
+    print(f"  - NVIDIA Models: {len(NVIDIA_MODELS)}")
+    print("=" * 60 + "\n")
+    app.run(port=7860, host='0.0.0.0')