Update app.py

app.py

@@ -1,175 +1,182 @@
-import os
-from dotenv import load_dotenv
-import google.generativeai as genai
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from pydantic import BaseModel
-from typing import Optional
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import json
-import pickle
-import pandas as pd
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Configure Google Gemini API
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-gemini_model = genai.GenerativeModel("gemini-1.5-flash")
-
-app = FastAPI()
-
-# Load models for plant disease prediction
-plant_disease_model = tf.keras.models.load_model('plant_disease_model.h5')
-class_indices = json.load(open('class_indices.json'))
-class_indices = {int(k): v for k, v in class_indices.items()}  # Ensure keys are int
-
-# Load models for crop recommendation
-with open("model_useing_Location_Seasion_Area.pkl", "rb") as f:
-    location_model = pickle.load(f)
-
-with open("model_useing_npk.pkl", "rb") as f:
-    npk_model = pickle.load(f)
-
-# Load unique values from the JSON file
-with open("unique_values.json", "r") as f:
-    unique_values = json.load(f)
-
-class CropRequest(BaseModel):
-    state: Optional[str] = None
-    district: Optional[str] = None
-    season: Optional[str] = None
-    area: Optional[float] = None
-    N: Optional[float] = None
-    P: Optional[float] = None
-    K: Optional[float] = None
-    temperature: Optional[float] = None
-    humidity: Optional[float] = None
-    ph: Optional[float] = None
-    rainfall: Optional[float] = None
-
-def fetch_gemini_advice(crop, disease):
-    """Fetches step-by-step cure instructions for the given crop and disease using Google Gemini API."""
-    if disease.lower() == "healthy":
-        return "The plant is healthy. No action needed."
-    
-    base_prompt = (
-        f"Provide only step-by-step instructions to cure {disease} in {crop}. "
-        "Do not include any introduction or description, just list the steps clearly and concisely."
-    )
-    response = gemini_model.generate_content(base_prompt)
-    return response.text if response else "No cure information found."
-
-def parse_prediction(prediction):
-    """Separates the crop name and disease name from the prediction."""
-    if "___" in prediction:
-        crop, disease = prediction.split("___")
-    else:
-        crop, disease = prediction, "Unknown"
-    return crop, disease
-
-# Image Preprocessing
-def load_and_preprocess_image(image_path, target_size=(224, 224)):
-    img = Image.open(image_path)
-    img = img.resize(target_size)
-    img_array = np.array(img)
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    img_array = img_array.astype('float32') / 255.  # Normalize
-    return img_array
-
-# Function to Convert Image to JPG
-def convert_to_jpg(image_path: str) -> str:
-    """
-    Converts any image to JPG format and returns the path to the converted image.
-    """
-    # Open the image
-    img = Image.open(image_path)
-
-    # Ensure the image is in RGB mode (important for non-RGB images like PNG)
-    img = img.convert("RGB")
-
-    # Define the new file path (adding .jpg extension)
-    new_image_path = os.path.splitext(image_path)[0] + ".jpg"
-
-    # Save the image as JPG
-    img.save(new_image_path, "JPEG")
-
-    # Return the path of the new image
-    return new_image_path
-
-# Prediction Function for Plant Disease
-def predict_image_class(image_path):
-    preprocessed_img = load_and_preprocess_image(image_path)
-    predictions = plant_disease_model.predict(preprocessed_img)
-    predicted_class_index = np.argmax(predictions, axis=1)[0]
-    return class_indices[predicted_class_index]
-
-@app.post('/predict_disease')
-async def predict_disease(file: UploadFile = File(...)):
-    # Save the uploaded file to /tmp directory
-    file_path = os.path.join('/tmp', file.filename)
-    with open(file_path, 'wb') as buffer:
-        buffer.write(await file.read())
-
-    # Convert the image to JPG if it's not already in JPG format
-    if not file.filename.lower().endswith(".jpg"):
-        file_path = convert_to_jpg(file_path)
-
-    # Predict the class
-    prediction = predict_image_class(file_path)
-    crop, disease = parse_prediction(prediction)
-
-    # Get cure steps from Google Gemini
-    cure_steps = fetch_gemini_advice(crop, disease)
-
-    # Remove the temporary file
-    os.remove(file_path)
-
-    return {
-        "crop": crop,
-        "predicted_disease": disease,
-        "cure_steps": cure_steps
-    }
-
-# Helper function to get top recommendations from the model
-def get_top_recommendations(model, input_data, top_n=3):
-    if hasattr(model, "predict_proba"):
-        proba = model.predict_proba(input_data)
-        top_indices = np.argsort(proba[0])[-top_n:][::-1]
-        return [model.classes_[i] for i in top_indices]
-    return model.predict(input_data).tolist()
-
-@app.post("/predict_crop")
-def predict_crop(request: CropRequest):
-    location_recommendations = []
-    npk_recommendations = []
-
-    if request.state and request.district and request.season and request.area:
-        try:
-            input_data = pd.DataFrame([[request.state, request.district, request.season, np.log1p(request.area)]],
-                                      columns=['State_Name', 'District_Name', 'Season', 'Area'])
-            location_recommendations = get_top_recommendations(location_model, input_data, top_n=3)
-        except ValueError:
-            pass  # Handle invalid input
-
-    if request.N is not None and request.P is not None and request.K is not None and request.temperature is not None \
-            and request.humidity is not None and request.ph is not None and request.rainfall is not None:
-        try:
-            input_data = np.array([[request.N, request.P, request.K, request.temperature,
-                                    request.humidity, request.ph, request.rainfall]])
-            npk_recommendations = get_top_recommendations(npk_model, input_data, top_n=3)
-        except ValueError:
-            pass  # Handle invalid input
-
-    location_lower = {crop.lower(): crop for crop in location_recommendations}
-    common_crops_lower = set(location_lower.keys()) & set(crop.lower() for crop in npk_recommendations)
-    common_crops = [location_lower[crop] for crop in common_crops_lower]
-
-    location_recommendations = [crop for crop in location_recommendations if crop.lower() not in common_crops_lower]
-    npk_recommendations = [crop for crop in npk_recommendations if crop.lower() not in common_crops_lower]
-
-    merged_crops = common_crops + npk_recommendations + location_recommendations
-    final_recommendations = [crop.lower() for crop in merged_crops[:4]]
-
-    return {"recommendations": final_recommendations if final_recommendations else []}
+import os
+from dotenv import load_dotenv
+import google.generativeai as genai
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import json
+import pickle
+import pandas as pd
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure Google Gemini API
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+gemini_model = genai.GenerativeModel("gemini-1.5-flash")
+
+app = FastAPI()
+
+# Load models for plant disease prediction
+plant_disease_model = tf.keras.models.load_model('plant_disease_model.h5')
+class_indices = json.load(open('class_indices.json'))
+class_indices = {int(k): v for k, v in class_indices.items()}  # Ensure keys are int
+
+# Load models for crop recommendation
+with open("model_useing_Location_Seasion_Area.pkl", "rb") as f:
+    location_model = pickle.load(f)
+
+with open("model_useing_npk.pkl", "rb") as f:
+    npk_model = pickle.load(f)
+
+# Load unique values from the JSON file
+with open("unique_values.json", "r") as f:
+    unique_values = json.load(f)
+
+class CropRequest(BaseModel):
+    state: Optional[str] = None
+    district: Optional[str] = None
+    season: Optional[str] = None
+    area: Optional[float] = None
+    N: Optional[float] = None
+    P: Optional[float] = None
+    K: Optional[float] = None
+    temperature: Optional[float] = None
+    humidity: Optional[float] = None
+    ph: Optional[float] = None
+    rainfall: Optional[float] = None
+
+def fetch_gemini_advice(crop, disease):
+    """Fetches step-by-step cure instructions for the given crop and disease using Google Gemini API."""
+    if disease.lower() == "healthy":
+        return "The plant is healthy. No action needed."
+    
+    base_prompt = (
+        f"Provide only step-by-step instructions to cure {disease} in {crop}. "
+        "Do not include any introduction or description, just list the steps clearly and concisely."
+    )
+    response = gemini_model.generate_content(base_prompt)
+    return response.text if response else "No cure information found."
+
+def parse_prediction(prediction):
+    """Separates the crop name and disease name from the prediction."""
+    if "___" in prediction:
+        crop, disease = prediction.split("___")
+    else:
+        crop, disease = prediction, "Unknown"
+    return crop, disease
+
+# Image Preprocessing
+def load_and_preprocess_image(image_path, target_size=(224, 224)):
+    img = Image.open(image_path)
+    img = img.resize(target_size)
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = img_array.astype('float32') / 255.  # Normalize
+    return img_array
+
+# Function to Convert Image to JPG
+def convert_to_jpg(image_path: str) -> str:
+    """
+    Converts any image to JPG format and returns the path to the converted image.
+    """
+    # Open the image
+    img = Image.open(image_path)
+
+    # Ensure the image is in RGB mode (important for non-RGB images like PNG)
+    img = img.convert("RGB")
+
+    # Define the new file path (adding .jpg extension)
+    new_image_path = os.path.splitext(image_path)[0] + ".jpg"
+
+    # Save the image as JPG
+    img.save(new_image_path, "JPEG")
+
+    # Return the path of the new image
+    return new_image_path
+
+# Prediction Function for Plant Disease
+def predict_image_class(image_path):
+    preprocessed_img = load_and_preprocess_image(image_path)
+    predictions = plant_disease_model.predict(preprocessed_img)
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    return class_indices[predicted_class_index]
+
+@app.post('/predict_disease')
+async def predict_disease(file: UploadFile = File(...)):
+    # Save the uploaded file to /tmp directory
+    file_path = os.path.join('/tmp', file.filename)
+    with open(file_path, 'wb') as buffer:
+        buffer.write(await file.read())
+
+    # Convert the image to JPG if it's not already in JPG format
+    if not file.filename.lower().endswith(".jpg"):
+        file_path = convert_to_jpg(file_path)
+
+    # Predict the class
+    prediction = predict_image_class(file_path)
+    crop, disease = parse_prediction(prediction)
+
+    # Get cure steps from Google Gemini
+    cure_steps = fetch_gemini_advice(crop, disease)
+
+    # Remove the temporary file
+    os.remove(file_path)
+
+    return {
+        "crop": crop,
+        "predicted_disease": disease,
+        "cure_steps": cure_steps
+    }
+
+# Helper function to get top recommendations from the model
+def get_top_recommendations(model, input_data, top_n=3):
+    if hasattr(model, "predict_proba"):
+        proba = model.predict_proba(input_data)
+        top_indices = np.argsort(proba[0])[-top_n:][::-1]
+        return [model.classes_[i] for i in top_indices]
+    return model.predict(input_data).tolist()
+
+@app.post("/predict_crop")
+def predict_crop(request: CropRequest):
+    location_recommendations = []
+    npk_recommendations = []
+
+    if request.state and request.district and request.season and request.area:
+        try:
+            input_data = pd.DataFrame([[request.state, request.district, request.season, np.log1p(request.area)]],
+                                      columns=['State_Name', 'District_Name', 'Season', 'Area'])
+            location_recommendations = get_top_recommendations(location_model, input_data, top_n=3)
+        except ValueError:
+            pass  # Handle invalid input
+
+    if request.N is not None and request.P is not None and request.K is not None and request.temperature is not None \
+            and request.humidity is not None and request.ph is not None and request.rainfall is not None:
+        try:
+            input_data = np.array([[request.N, request.P, request.K, request.temperature,
+                                    request.humidity, request.ph, request.rainfall]])
+            npk_recommendations = get_top_recommendations(npk_model, input_data, top_n=3)
+        except ValueError:
+            pass  # Handle invalid input
+
+    location_lower = {crop.lower(): crop for crop in location_recommendations}
+    common_crops_lower = set(location_lower.keys()) & set(crop.lower() for crop in npk_recommendations)
+    common_crops = [location_lower[crop] for crop in common_crops_lower]
+
+    location_recommendations = [crop for crop in location_recommendations if crop.lower() not in common_crops_lower]
+    npk_recommendations = [crop for crop in npk_recommendations if crop.lower() not in common_crops_lower]
+
+    merged_crops = common_crops + npk_recommendations + location_recommendations
+    final_recommendations = [crop.lower() for crop in merged_crops[:4]]
+
+    return {"recommendations": final_recommendations if final_recommendations else []}
+
+
+
+
+@app.get("/")
+def read_root():
+    return {"message": "API is running!"}