Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+from groq import Groq
+from dotenv import load_dotenv
+
+# Load environment variables from the .env file
+load_dotenv()
+
+# Securely get the GROQ API key from environment variables
+groq_api_key = os.getenv("GROQ_API_KEY")
+if not groq_api_key:
+    raise ValueError("GROQ_API_KEY environment variable not set.")
+
+# Initialize the client with the API key
+client = Groq(api_key=groq_api_key)
+
+# Expanded dictionary with treatments for various diseases
+disease_treatments = {
+    "Grape with Black Rot": "Prune affected areas, avoid water on leaves, use fungicide if severe.",
+    "Potato with Early Blight": "Apply fungicides, avoid overhead watering, rotate crops yearly.",
+    "Tomato with Early Blight": "Remove infected leaves, use copper-based fungicide, maintain good airflow.",
+    "Apple with Scab": "Remove fallen leaves, prune trees, apply fungicide in early spring.",
+    "Wheat with Leaf Rust": "Apply resistant varieties, use fungicides, remove weeds.",
+    "Cucumber with Downy Mildew": "Use resistant varieties, ensure good air circulation, apply fungicide.",
+    "Rose with Powdery Mildew": "Use sulfur or potassium bicarbonate sprays, prune affected areas, avoid overhead watering.",
+    "Strawberry with Gray Mold": "Remove infected fruits, improve ventilation, avoid wetting the fruit when watering.",
+    "Peach with Leaf Curl": "Apply a fungicide in late fall or early spring, remove affected leaves.",
+    "Banana with Panama Disease": "Use disease-resistant varieties, ensure soil drainage, avoid overwatering.",
+    "Tomato with Septoria Leaf Spot": "Use resistant varieties, remove infected leaves, apply fungicide.",
+    "Corn with Smut": "Remove infected ears, use disease-free seed, rotate crops.",
+    "Carrot with Root Rot": "Ensure well-draining soil, avoid excessive watering, use crop rotation.",
+    "Onion with Downy Mildew": "Use fungicides, ensure adequate spacing, avoid overhead watering.",
+    "Potato with Late Blight": "Apply copper-based fungicides, remove affected foliage, practice crop rotation.",
+    "Citrus with Greening Disease": "Remove infected trees, control leafhopper population, plant disease-free trees.",
+    "Lettuce with Downy Mildew": "Ensure good air circulation, avoid overhead watering, apply fungicides.",
+    "Pepper with Bacterial Spot": "Use resistant varieties, apply copper-based bactericides, practice crop rotation.",
+    "Eggplant with Verticillium Wilt": "Use resistant varieties, solarize soil before planting, avoid soil disturbance.",
+    "Cotton with Boll Rot": "Improve drainage, remove infected bolls, apply fungicides if necessary.",
+    "Soybean with Soybean Rust": "Use fungicides, rotate crops, use resistant varieties if available.",
+    "Rice with Sheath Blight": "Reduce nitrogen application, maintain proper water levels, apply fungicides.",
+    "Sunflower with Downy Mildew": "Use resistant varieties, avoid waterlogging, apply fungicides.",
+    "Barley with Net Blotch": "Use resistant varieties, remove crop residues, apply fungicides.",
+    "Oat with Crown Rust": "Use resistant varieties, apply fungicides, avoid high nitrogen levels.",
+    "Sugarcane with Red Rot": "Use disease-free cuttings, control weeds, apply fungicides if necessary.",
+    "Pine with Pine Wilt": "Remove and destroy infected trees, control beetle population, avoid planting susceptible species.",
+    "Avocado with Anthracnose": "Prune infected branches, use copper-based fungicides, avoid wet foliage.",
+    "Papaya with Papaya Ringspot Virus": "Use virus-resistant varieties, remove infected plants, control aphid population.",
+    "Mango with Powdery Mildew": "Use sulfur-based fungicides, remove affected parts, avoid overhead watering.",
+    "Peanut with Leaf Spot": "Use resistant varieties, apply fungicides, rotate crops to reduce infection risk.",
+    "Chili with Anthracnose": "Apply copper fungicides, remove infected fruits, avoid overhead irrigation.",
+    "Garlic with White Rot": "Remove infected plants, improve soil drainage, practice crop rotation."
+}
+
+# Streamlit title and description
+st.title("🌿 Plant Disease Detection 🌿")
+st.write("Upload an image of a plant leaf, and the app will detect the disease and suggest a treatment.")
+
+# File upload option
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "bmp", "gif", "tiff", "webp"])
+
+if uploaded_file is not None:
+    # Open the image using PIL and display it
+    image = Image.open(uploaded_file)
+    image = image.convert("RGB")
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+    # Initialize the feature extractor and model
+    extractor = AutoFeatureExtractor.from_pretrained("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification")
+    model = AutoModelForImageClassification.from_pretrained("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification")
+
+    # Preprocess the image
+    inputs = extractor(images=image, return_tensors="pt")
+
+    # Get the model's raw prediction (logits)
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    # Temperature scaling for logits (lowering temperature to adjust confidence)
+    temperature = 0.5
+    logits = logits / temperature
+
+    # Convert logits to probabilities using softmax
+    softmax = torch.nn.Softmax(dim=1)
+    probabilities = softmax(logits)
+
+    # Get the top prediction
+    top_k = 1
+    top_probs, top_indices = torch.topk(probabilities, top_k, dim=1)
+    top_probs = top_probs[0].tolist()
+    top_indices = top_indices[0].tolist()
+
+    # Define a confidence threshold
+    confidence_threshold = 0.5
+    class_labels = model.config.id2label
+    predicted_disease = "Unknown Disease"
+    predicted_confidence = top_probs[0]
+
+    # Handle low-confidence predictions
+    if predicted_confidence >= confidence_threshold:
+        predicted_disease = class_labels.get(top_indices[0], "Unknown Disease")
+    else:
+        predicted_disease = "Unknown Disease"
+        st.warning("The model could not confidently identify a disease. Please try again with a clearer image.")
+
+    # Fetch treatment from the dictionary
+    treatment = disease_treatments.get(predicted_disease, "No treatment information available.")
+
+    # Display prediction and treatment
+    st.write(f"**Predicted Disease:** {predicted_disease}")
+    st.write(f"**Confidence Level:** {predicted_confidence:.2f}")
+    
+
+    # Generate detailed report (using prompts)
+    symptoms = "leaf discoloration, spots, mold, wilting"  # This can be dynamically generated based on the image features
+    diagnosis_prompt = f"Identify the disease or pest affecting a plant with the following symptoms: {symptoms}."
+    treatment_prompt = f"Suggest treatments for a plant disease with symptoms like {symptoms}. Include organic and chemical treatment options if available."
+    preventive_prompt = f"Provide preventive measures for plant diseases with symptoms similar to {symptoms}."
+
+    # Combine the prompts into the chat context
+    chat_context = f"""
+    ### 🌱 Plant Disease Diagnosis Report 🌱
+
+    #### 🌟 Predicted Disease
+    - **Disease**: {predicted_disease}
+    - **Confidence Level**: {predicted_confidence:.2f}
+
+    #### 📝 Disease Diagnosis and Symptoms
+    - **Diagnosis**: {diagnosis_prompt}
+
+    #### 💊 Treatment Recommendations
+    - **Treatment**: {treatment_prompt}
+
+    #### 🛡️ Preventive Measures
+    - **Prevention**: {preventive_prompt}
+
+    #### 🔚 Conclusion
+    - **Next Steps**: Consult a local expert for further advice.
+    """
+
+    # Generate report using Groq (or any other service)
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[ 
+                {
+                    "role": "system",
+                    "content": (
+                        "You are a plant disease analysis assistant. Your task is to provide a comprehensive, actionable diagnosis report."
+                        "The report should include the predicted disease, its symptoms, recommended treatments, and prevention tips. "
+                        "Ensure the report is actionable and easy to understand for non-experts in agriculture."
+                    )
+                },
+                {
+                    "role": "user",
+                    "content": chat_context
+                }
+            ],
+            model="mixtral-8x7b-32768",  # Adjust this to the model you're using
+            temperature=0.7,  # Adjust to balance creativity and precision
+            max_tokens=1500  # Limit to ensure the output is not cut off
+        )
+
+        # Display the generated report in the Streamlit app
+        st.markdown("---")
+        # Display the full HTML report generated by Groq
+        st.markdown(chat_completion.choices[0].message.content, unsafe_allow_html=True)
+
+    except Exception as e:
+        # If there's an error with the Groq API call, display an error message
+        st.error(f"Error generating report: {str(e)}")