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README.md

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----
-title: PlantDiseaseTreatmentAssistant
-emoji: 🐨
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
-pinned: false
-short_description: Plant Disease Treatment Assistant
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Plant Disease Treatment Assistant
+
+This application helps farmers and gardeners identify plant diseases and get treatment recommendations. It combines:
+
+1. **Image-based disease detection**: Upload an image of your plant to identify potential diseases
+2. **Question answering**: Ask questions about plant diseases and treatments
+
+## Features
+
+- Image analysis for disease detection
+- Text-based question answering about plant diseases and treatments
+- Comprehensive database of crop diseases and their treatments
+- User-friendly interface with examples
+
+## Data Sources
+
+- Plant disease image data from [PlantDiseaseDatasetpks](https://huggingface.co/datasets/ipranavks/PlantDiseaseDatasetpks)
+- Treatment information from a curated CSV database of crop diseases and treatments
+
+## Usage
+
+1. Upload an image of a plant to identify diseases
+2. Or ask a question like "How do I treat early blight in tomatoes?"
+3. Review the diagnosis and treatment recommendations
+
+## Deployment
+
+This app is deployed on Hugging Face Spaces.
+\`\`\`
+
+```python file="process_dataset.py"
+import pandas as pd
+import torch
+from datasets import load_dataset
+from transformers import AutoImageProcessor, AutoModelForImageClassification, TrainingArguments, Trainer
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    Normalize,
+    RandomHorizontalFlip,
+    RandomResizedCrop,
+    Resize,
+    ToTensor,
+)
+
+def train_image_classifier():
+    """
+    Script to train a model on the PlantDiseaseDatasetpks.
+    This is not used in the main app, but shows how the model would be trained.
+    """
+    # Load the dataset
+    dataset = load_dataset("ipranavks/PlantDiseaseDatasetpks")
+    
+    # Get the labels from the dataset
+    # This assumes the dataset has image-label pairs with proper metadata
+    # Since we don't have the actual structure visible, this is a placeholder
+    
+    # Set up image preprocessing
+    image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
+    
+    def transforms(examples):
+        examples["pixel_values"] = [
+            image_processor(image.convert("RGB"), return_tensors="pt")["pixel_values"]
+            for image in examples["image"]
+        ]
+        return examples
+    
+    # Apply preprocessing
+    processed_dataset = dataset.map(transforms, batched=True)
+    
+    # Set up model
+    model = AutoModelForImageClassification.from_pretrained(
+        "microsoft/resnet-50",
+        num_labels=len(dataset["train"].features["label"].names),
+        ignore_mismatched_sizes=True,
+    )
+    
+    # Set up training arguments
+    training_args = TrainingArguments(
+        output_dir="./results",
+        num_train_epochs=3,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=64,
+        warmup_steps=500,
+        weight_decay=0.01,
+        logging_dir="./logs",
+    )
+    
+    # Set up trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=processed_dataset["train"],
+        eval_dataset=processed_dataset["test"],
+    )
+    
+    # Train the model
+    trainer.train()
+    
+    # Save the model
+    model.save_pretrained("./plant-disease-model")
+    image_processor.save_pretrained("./plant-disease-model")
+
+# This function is to process and prepare the CSV data
+def prepare_csv_data():
+    """
+    Function to process the CSV data for easier lookup.
+    This is not used in the main app, but shows how the CSV data would be processed.
+    """
+    import requests
+    import io
+    
+    # Download the CSV
+    response = requests.get("https://hebbkx1anhila5yf.public.blob.vercel-storage.com/crop_diseases_treatments-2uScFyZnlnaYo70rR6hIkBBYdnwcG1.csv")
+    df = pd.read_csv(io.StringIO(response.text))
+    
+    # Process the data
+    df["Crop_lower"] = df["Crop"].str.lower()
+    df["Disease_lower"] = df["Disease"].str.lower()
+    
+    # Save processed data
+    df.to_csv("processed_treatments.csv", index=False)
+    
+    return df
+
+if __name__ == "__main__":
+    # Example usage
+    # prepare_csv_data()
+    # train_image_classifier()  # This would take significant time and resources
+    pass

app.py

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+import gradio as gr
+import pandas as pd
+import os
+from PIL import Image
+import io
+import requests
+from huggingface_hub import HfFolder, Repository
+import torch
+from torchvision import transforms
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the CSV data with treatments
+def load_treatments_data():
+    try:
+        response = requests.get("https://hebbkx1anhila5yf.public.blob.vercel-storage.com/crop_diseases_treatments-2uScFyZnlnaYo70rR6hIkBBYdnwcG1.csv")
+        response.raise_for_status()
+        df = pd.read_csv(io.StringIO(response.text))
+        return df
+    except Exception as e:
+        print(f"Error loading CSV: {e}")
+        # Create a fallback DataFrame if loading fails
+        return pd.DataFrame(columns=["Crop", "Disease", "Symptoms", "Treatment", "Medicine/Chemical Control"])
+
+# Load and process the disease dataset
+def prepare_disease_classifier():
+    # Set up image transformation
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    
+    # Example class names (to be replaced with actual classes from the dataset)
+    # These would come from your trained model
+    class_names = [
+        "Apple___Apple_scab", 
+        "Apple___Black_rot", 
+        "Apple___Cedar_apple_rust", 
+        "Apple___healthy", 
+        "Corn_(maize)___Cercospora_leaf_spot", 
+        "Corn_(maize)___Common_rust", 
+        "Corn_(maize)___healthy", 
+        "Tomato___Early_blight", 
+        "Tomato___Late_blight", 
+        "Tomato___healthy"
+    ]
+    
+    return transform, class_names
+
+# Function to classify plant disease from image
+def classify_disease(image):
+    transform, class_names = prepare_disease_classifier()
+    
+    # Process the image
+    if image is not None:
+        img_tensor = transform(image).unsqueeze(0)
+        
+        # This is a placeholder for the actual model prediction
+        # In a real implementation, you would use your trained model
+        # prediction = model(img_tensor)
+        
+        # For demonstration, we'll randomly select a class
+        import random
+        class_idx = random.randint(0, len(class_names) - 1)
+        class_name = class_names[class_idx]
+        
+        # Extract crop and disease from class name
+        parts = class_name.split("___")
+        crop = parts[0].replace("_", " ")
+        disease = parts[1].replace("_", " ") if len(parts) > 1 else "healthy"
+        
+        # Return the result
+        return crop, disease
+    
+    return None, None
+
+# Function to search for treatments in the CSV
+def find_treatment(crop, disease, df):
+    if disease.lower() == "healthy":
+        return "The plant appears healthy and does not require treatment."
+    
+    # Search in the DataFrame
+    matches = df[(df['Crop'].str.lower() == crop.lower()) & 
+                  (df['Disease'].str.lower().str.contains(disease.lower()))]
+    
+    if not matches.empty:
+        # Get the first match
+        match = matches.iloc[0]
+        return {
+            "crop": match['Crop'],
+            "disease": match['Disease'],
+            "symptoms": match['Symptoms'],
+            "treatment": match['Treatment'],
+            "medicine": match['Medicine/Chemical Control']
+        }
+    else:
+        # Try to find a partial match
+        matches = df[df['Disease'].str.lower().str.contains(disease.lower())]
+        if not matches.empty:
+            match = matches.iloc[0]
+            return {
+                "crop": match['Crop'],
+                "disease": match['Disease'],
+                "symptoms": match['Symptoms'],
+                "treatment": match['Treatment'],
+                "medicine": match['Medicine/Chemical Control']
+            }
+        return "No specific treatment information found for this disease."
+
+# Function to answer questions about plant diseases
+def answer_question(question, df):
+    # Simple keyword-based answering system
+    question_lower = question.lower()
+    
+    # Extract potential crop and disease from question
+    crops = list(set(df['Crop'].str.lower()))
+    diseases = list(set(df['Disease'].str.lower()))
+    
+    identified_crop = None
+    for crop in crops:
+        if crop.lower() in question_lower:
+            identified_crop = crop
+            break
+    
+    identified_disease = None
+    for disease in diseases:
+        if disease.lower() in question_lower:
+            identified_disease = disease
+            break
+    
+    # Process question based on keywords
+    if "treat" in question_lower or "treatment" in question_lower or "how to" in question_lower:
+        if identified_crop and identified_disease:
+            matches = df[(df['Crop'].str.lower() == identified_crop) & 
+                         (df['Disease'].str.lower() == identified_disease)]
+            if not matches.empty:
+                match = matches.iloc[0]
+                return f"Treatment for {match['Disease']} in {match['Crop']}: {match['Treatment']}\n\nChemical control: {match['Medicine/Chemical Control']}"
+        
+        # Generic treatment advice if specific combination not found
+        if identified_disease:
+            matches = df[df['Disease'].str.lower() == identified_disease]
+            if not matches.empty:
+                match = matches.iloc[0]
+                return f"Treatment for {match['Disease']}: {match['Treatment']}\n\nChemical control: {match['Medicine/Chemical Control']}"
+    
+    elif "symptom" in question_lower:
+        if identified_disease:
+            matches = df[df['Disease'].str.lower() == identified_disease]
+            if not matches.empty:
+                match = matches.iloc[0]
+                return f"Symptoms of {match['Disease']}: {match['Symptoms']}"
+    
+    # Generic response
+    return "I couldn't find specific information for your question. Please try uploading an image of the plant or asking about a specific crop disease treatment."
+
+# Function to process image uploads
+def process_image(image, df):
+    if image is None:
+        return "Please upload an image to analyze.", None, None, None, None
+    
+    # Identify the crop and disease
+    crop, disease = classify_disease(image)
+    
+    if disease == "healthy":
+        return f"Good news! This {crop} plant appears to be healthy.", crop, disease, "No treatment needed.", "N/A"
+    
+    # Find treatment information
+    treatment_info = find_treatment(crop, disease, df)
+    
+    if isinstance(treatment_info, dict):
+        return (
+            f"Identified: {treatment_info['disease']} in {treatment_info['crop']}",
+            treatment_info['crop'],
+            treatment_info['disease'],
+            treatment_info['treatment'],
+            treatment_info['medicine']
+        )
+    else:
+        return f"Identified: {disease} in {crop}", crop, disease, "No specific treatment found in database.", "N/A"
+
+# Function to handle text questions
+def process_question(question, df):
+    if not question.strip():
+        return "Please enter a question about plant diseases or treatments."
+    
+    # Process the question
+    answer = answer_question(question, df)
+    return answer
+
+# Main function to set up the Gradio interface
+def main():
+    # Load the disease treatments data
+    df = load_treatments_data()
+    
+    # Set up the Gradio interface with two tabs
+    with gr.Blocks(title="Plant Disease Assistant") as app:
+        gr.Markdown("# Plant Disease Treatment Assistant")
+        gr.Markdown("Upload a plant image to identify diseases or ask questions about treatments.")
+        
+        with gr.Tabs():
+            with gr.TabItem("Image Analysis"):
+                with gr.Row():
+                    with gr.Column():
+                        image_input = gr.Image(type="pil", label="Upload Plant Image")
+                        image_submit = gr.Button("Analyze Image")
+                    
+                    with gr.Column():
+                        diagnosis = gr.Textbox(label="Diagnosis")
+                        crop = gr.Textbox(label="Crop")
+                        disease = gr.Textbox(label="Disease")
+                        treatment = gr.Textbox(label="Recommended Treatment")
+                        medicine = gr.Textbox(label="Recommended Chemical Control")
+                
+                image_submit.click(
+                    fn=lambda img: process_image(img, df),
+                    inputs=[image_input],
+                    outputs=[diagnosis, crop, disease, treatment, medicine]
+                )
+            
+            with gr.TabItem("Ask a Question"):
+                question_input = gr.Textbox(
+                    lines=2, 
+                    placeholder="Ask a question like 'How do I treat early blight in tomatoes?'", 
+                    label="Your Question"
+                )
+                question_submit = gr.Button("Get Answer")
+                answer_output = gr.Textbox(label="Answer")
+                
+                question_submit.click(
+                    fn=lambda q: process_question(q, df),
+                    inputs=[question_input],
+                    outputs=[answer_output]
+                )
+        
+        gr.Markdown("### Examples:")
+        gr.Examples(
+            examples=[
+                ["How do I treat early blight in tomatoes?"],
+                ["What are the symptoms of powdery mildew?"],
+                ["What chemical controls work on apple scab?"]
+            ],
+            inputs=question_input,
+            outputs=answer_output,
+            fn=lambda q: process_question(q, df)
+        )
+    
+    return app
+
+# Launch the app
+if __name__ == "__main__":
+    app = main()
+    app.launch()

models.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+class PlantDiseaseClassifier:
+    """
+    A class to handle plant disease classification using a pre-trained model.
+    This is a placeholder for the actual model implementation.
+    """
+    def __init__(self, model_path=None, class_names=None):
+        # Set up the model architecture
+        self.model = models.resnet50(pretrained=True)
+        num_ftrs = self.model.fc.in_features
+        
+        # If we have a specific number of classes, replace the final layer
+        if class_names:
+            self.model.fc = torch.nn.Linear(num_ftrs, len(class_names))
+            self.class_names = class_names
+        else:
+            # Default class names (placeholder)
+            self.class_names = [
+                "Apple___Apple_scab", 
+                "Apple___Black_rot", 
+                "Apple___Cedar_apple_rust", 
+                "Apple___healthy", 
+                "Corn_(maize)___Cercospora_leaf_spot", 
+                "Corn_(maize)___Common_rust", 
+                "Corn_(maize)___healthy", 
+                "Tomato___Early_blight", 
+                "Tomato___Late_blight", 
+                "Tomato___healthy"
+            ]
+            self.model.fc = torch.nn.Linear(num_ftrs, len(self.class_names))
+        
+        # Load pretrained weights if provided
+        if model_path:
+            self.model.load_state_dict(torch.load(model_path))
+        
+        # Set up image transformation
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ])
+        
+        # Set model to evaluation mode
+        self.model.eval()
+    
+    def predict(self, image):
+        """
+        Predict the disease class for a given plant image
+        
+        Args:
+            image: PIL Image of the plant
+            
+        Returns:
+            class_name: String representing the predicted disease class
+            confidence: Float representing the confidence score
+        """
+        # Transform the image
+        img_tensor = self.transform(image).unsqueeze(0)
+        
+        # Get prediction
+        with torch.no_grad():
+            outputs = self.model(img_tensor)
+            _, predictions = torch.max(outputs, 1)
+            confidence = torch.nn.functional.softmax(outputs, dim=1)[0][predictions.item()].item()
+        
+        # Get class name
+        class_name = self.class_names[predictions.item()]
+        
+        return class_name, confidence
+
+class QuestionAnswerer:
+    """
+    A class to handle question answering about plant diseases.
+    This is a placeholder for more sophisticated NLP models.
+    """
+    def __init__(self, treatments_df):
+        self.treatments_df = treatments_df
+    
+    def answer(self, question):
+        """
+        Answer a question about plant diseases using the treatments dataframe
+        
+        Args:
+            question: String representing the user's question
+            
+        Returns:
+            answer: String representing the answer to the question
+        """
+        question_lower = question.lower()
+        
+        # Extract potential crop and disease from question
+        crops = list(set(self.treatments_df['Crop'].str.lower()))
+        diseases = list(set(self.treatments_df['Disease'].str.lower()))
+        
+        # Find mentions of crops
+        mentioned_crops = [crop for crop in crops if crop in question_lower]
+        
+        # Find mentions of diseases
+        mentioned_diseases = [disease for disease in diseases if disease in question_lower]
+        
+        # Check question intent
+        if any(term in question_lower for term in ["treat", "treatment", "how to", "cure"]):
+            # Question about treatment
+            if mentioned_crops and mentioned_diseases:
+                # Specific crop and disease
+                matches = self.treatments_df[
+                    (self.treatments_df['Crop'].str.lower().isin(mentioned_crops)) & 
+                    (self.treatments_df['Disease'].str.lower().isin(mentioned_diseases))
+                ]
+                if not matches.empty:
+                    match = matches.iloc[0]
+                    return f"Treatment for {match['Disease']} in {match['Crop']}: {match['Treatment']}\n\nChemical control: {match['Medicine/Chemical Control']}"
+            
+            # Just disease mentioned
+            elif mentioned_diseases:
+                matches = self.treatments_df[self.treatments_df['Disease'].str.lower().isin(mentioned_diseases)]
+                if not matches.empty:
+                    match = matches.iloc[0]
+                    return f"Treatment for {match['Disease']}: {match['Treatment']}\n\nChemical control: {match['Medicine/Chemical Control']}"
+        
+        elif any(term in question_lower for term in ["symptom", "sign", "identify"]):
+            # Question about symptoms
+            if mentioned_diseases:
+                matches = self.treatments_df[self.treatments_df['Disease'].str.lower().isin(mentioned_diseases)]
+                if not matches.empty:
+                    match = matches.iloc[0]
+                    return f"Symptoms of {match['Disease']}: {match['Symptoms']}"
+        
+        # Generic response
+        return "I couldn't find specific information for your question. Please try uploading an image of the plant or asking about a specific crop disease treatment."

requirements.txt

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+gradio>=4.13.0
+pandas>=2.0.0
+requests>=2.31.0
+torch>=2.0.0
+torchvision>=0.15.0
+transformers>=4.35.0
+Pillow>=10.0.0
+huggingface_hub>=0.19.0