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app.py

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+import os
+import json
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import streamlit as st
+import matplotlib.pyplot as plt
+from groq import Groq
+
+# Set page configuration
+st.set_page_config(page_title="Plant Disease Classifier", page_icon="🌿", layout="wide")
+
+# Get the directory of the current file
+working_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Use os.path.join for cross-platform compatibility
+model_path = os.path.join(working_dir, 'plant_disease_prediction_model.h5')
+class_indices_path = os.path.join(working_dir, 'class_indices.json')
+
+# Load the pre-trained model
+model = tf.keras.models.load_model(model_path)
+
+# Loading the class names
+with open(class_indices_path, 'r') as f:
+    class_indices = json.load(f)
+
+# Function to Load and Preprocess the Image using Pillow
+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)
+    img_array = img_array.astype('float32') / 255.0
+    return img_array
+
+# Function to Predict the Class of an Image
+def predict_image_class(model, image_path, class_indices):
+    preprocessed_img = load_and_preprocess_image(image_path)
+    predictions = model.predict(preprocessed_img)
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    predicted_class_name = class_indices[str(predicted_class_index)]
+    return predicted_class_name  # Only returning the name of the disease
+
+# Function to Get Solution from LLM
+def get_solution_from_llm(disease_name):
+    client = Groq(api_key="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")
+    prompt = f"The plant disease detected is {disease_name}. Provide detailed steps to mitigate the disease and improve plant health."
+    
+    completion = client.chat.completions.create(
+        model="llama-3.1-70b-versatile",
+        messages=[{"role": "system", "content": "You are a helpful assistant."},
+                  {"role": "user", "content": prompt}],
+        temperature=1,
+        max_tokens=1024,
+        top_p=1,
+        stream=False,
+        stop=None,
+    )
+    
+    solution = completion.choices[0].message.content
+    return solution
+
+# Sidebar Content
+st.sidebar.title("🌿 About the App")
+st.sidebar.write("This app uses AI to classify plant diseases and provide solutions.")
+st.sidebar.subheader("How It Works:")
+st.sidebar.write("""
+1. Upload a clear image of a plant leaf.
+2. The model predicts the disease using a pre-trained AI.
+3. You get detailed mitigation solutions powered by LLMs.
+""")
+st.sidebar.markdown("**Technologies Used:**")
+st.sidebar.write("- TensorFlow\n- PIL\n- Groq API\n- Streamlit")
+st.sidebar.markdown("**Project By:** Harman")
+
+# Main Page
+st.markdown("<h1 style='text-align: center; color: green;'>🌱 Plant Disease Classifier</h1>", unsafe_allow_html=True)
+
+uploaded_image = st.file_uploader("Upload a leaf image (jpg, jpeg, png)", type=["jpg", "jpeg", "png"])
+
+if uploaded_image:
+    # Display uploaded image (resized for smaller display)
+    image = Image.open(uploaded_image)
+    col1, col2 = st.columns(2, gap="large")
+
+    with col1:
+        st.markdown("<h3 style='text-align: center;'>Uploaded Image</h3>", unsafe_allow_html=True)
+        resized_img = image.resize((200, 200))  # Resized to 200x200 for smaller display
+        st.image(resized_img, use_column_width=True)
+
+    with col2:
+        st.markdown("<h3 style='text-align: center;'>Prediction</h3>", unsafe_allow_html=True)
+        if st.button("Classify"):
+            with st.spinner("Analyzing the image..."):
+                prediction = predict_image_class(model, uploaded_image, class_indices)  # Only name of the disease
+                st.success(f"**Disease Detected:** {prediction}")
+
+                st.markdown("---")
+                st.markdown("<h3>Solution for the Problem:</h3>", unsafe_allow_html=True)
+                solution = get_solution_from_llm(prediction)
+                st.write(solution)
+                
+                # Toggle Solution Visibility
+                with st.expander("Want to learn more? Click here!"):
+                    st.markdown(solution)
+                
+                # Download Button
+                st.download_button(
+                    label="Download Solution as Text",
+                    data=solution,
+                    file_name=f"{prediction}_solution.txt",
+                    mime="text/plain"
+                )
+else:
+    st.warning("Please upload an image to proceed.")
+
+# Footer
+st.markdown("<hr>", unsafe_allow_html=True)
+st.markdown(
+    "<p style='text-align: center; font-size: 14px;'>Made with ❤️ by Harman</p>",
+    unsafe_allow_html=True
+)

class_indices.json

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+{"0": "Apple___Apple_scab", "1": "Apple___Black_rot", "2": "Apple___Cedar_apple_rust", "3": "Apple___healthy", "4": "Blueberry___healthy", "5": "Cherry_(including_sour)___Powdery_mildew", "6": "Cherry_(including_sour)___healthy", "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "8": "Corn_(maize)___Common_rust_", "9": "Corn_(maize)___Northern_Leaf_Blight", "10": "Corn_(maize)___healthy", "11": "Grape___Black_rot", "12": "Grape___Esca_(Black_Measles)", "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "14": "Grape___healthy", "15": "Orange___Haunglongbing_(Citrus_greening)", "16": "Peach___Bacterial_spot", "17": "Peach___healthy", "18": "Pepper,_bell___Bacterial_spot", "19": "Pepper,_bell___healthy", "20": "Potato___Early_blight", "21": "Potato___Late_blight", "22": "Potato___healthy", "23": "Raspberry___healthy", "24": "Soybean___healthy", "25": "Squash___Powdery_mildew", "26": "Strawberry___Leaf_scorch", "27": "Strawberry___healthy", "28": "Tomato___Bacterial_spot", "29": "Tomato___Early_blight", "30": "Tomato___Late_blight", "31": "Tomato___Leaf_Mold", "32": "Tomato___Septoria_leaf_spot", "33": "Tomato___Spider_mites Two-spotted_spider_mite", "34": "Tomato___Target_Spot", "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "36": "Tomato___Tomato_mosaic_virus", "37": "Tomato___healthy"}

plant_disease_prediction_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0d196c3af57c9db66072e031922fedef1a0980f2ff5d859ec203f72a31f0646
+size 573706416

requirements.txt

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+streamlit
+kaggle
+pandas
+scikit-learn
+tensorflow
+groq
+matplotlib