Update app.py

app.py

@@ -1,185 +1,59 @@
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-import os
-import tempfile
-from PIL import Image
 from transformers import pipeline
-import logging
-
-# Setup logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+from PIL import Image
+import base64
+import io
+import json
 
 # Load model
-model = pipeline("image-classification", model="linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification", top_k=5)
-
-# Mapping, treatments, and fertilizers
-DISEASE_MAPPING = {
-    "Tomato___Late_blight": "Late Blight",
-    "Tomato___Early_blight": "Early Blight",
-    "Tomato___healthy": "Healthy"
-}
-TREATMENTS = {
-    "Late Blight": "Apply chlorothalonil or mancozeb fungicides. Remove infected material.",
-    "Early Blight": "Use copper fungicide and remove lower leaves.",
-    "Healthy": "No treatment needed."
-}
-FERTILIZERS = {
-    "Late Blight": "Low-nitrogen (5-10-10).",
-    "Early Blight": "Balanced with calcium (10-10-10 + Ca).",
-    "Healthy": "Standard NPK (10-10-10)."
-}
-
-def preprocess_image(image):
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image.astype('uint8'), 'RGB')
-    return image.resize((224, 224), Image.LANCZOS)
-
-def generate_confidence_plot(preds):
-    labels = [p['label'] for p in preds]
-    scores = [p['score'] * 100 for p in preds]
-    plt.figure(figsize=(8, 5))
-    bars = plt.barh(labels, scores, color=['#66bb6a' if 'healthy' in l.lower() else '#ef5350' for l in labels])
-    plt.xlabel("Confidence (%)")
-    plt.xlim(0, 100)
-    plt.gca().invert_yaxis()
-    for bar in bars:
-        plt.text(bar.get_width() - 5, bar.get_y() + bar.get_height()/2,
-                 f"{bar.get_width():.1f}%", va='center', ha='right', color='white')
-    path = os.path.join(tempfile.gettempdir(), f"plot_{int(time.time())}.png")
-    plt.tight_layout()
-    plt.savefig(path)
-    plt.close()
-    return path
-
-def generate_analysis(disease, confidence):
-    if disease == "Healthy":
-        return "🌱 Your plant looks healthy! No signs of disease detected."
-    if confidence < 0.6:
-        return f"⚠️ Possible signs of {disease.lower()} detected. Monitor closely."
-    elif confidence < 0.8:
-        return f"🟡 Likely {disease.lower()} detected. Preventive treatment advised."
-    else:
-        return f"🔴 Clear signs of {disease.lower()} detected. Treat immediately."
-
-def predict(image, plant_type, location, format):
-    processed = preprocess_image(image)
-    preds = model(processed)
-    top = preds[0]
-    disease = DISEASE_MAPPING.get(top['label'], top['label'])
-    analysis = generate_analysis(disease, top['score'])
-    treatment = TREATMENTS.get(disease, "Consult expert.")
-    fertilizer = FERTILIZERS.get(disease, "Use balanced NPK.")
-    plot_path = generate_confidence_plot(preds)
-
-    table = "| Rank | Disease | Confidence |\n|------|---------|------------|\n"
-    for i, p in enumerate(preds):
-        name = DISEASE_MAPPING.get(p['label'], p['label'])
-        table += f"| {i+1} | {name} | {p['score']*100:.1f}% |\n"
-
-    report = f"""
-### 🪴 Plant Type: {plant_type}
-📍 **Location**: {location or 'Not specified'}  
-🧪 **Diagnosis**: {disease} ({top['score']*100:.1f}%)  
-🕒 **Time**: {time.strftime('%Y-%m-%d %H:%M:%S')}
-
----
-
-### 🔍 AI Analysis  
-{analysis}
-
-### 💊 Treatment Recommendation  
-{treatment}
-
-### 🌱 Fertilizer Suggestion  
-{fertilizer}
-
----
-
-### 📊 Top Predictions  
-{table}
-"""
-    report_file = os.path.join(tempfile.gettempdir(), f"report_{int(time.time())}.txt")
-    with open(report_file, "w") as f:
-        f.write(report)
-
-    return report, plot_path, report_file
-
-# ───────────────────── STYLING ─────────────────────
-
-css = """
-@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&display=swap');
-
-body {
-    font-family: 'Poppins', sans-serif;
-    background: linear-gradient(to right, #e6f2e6, #cce0cc);
-    background-image: url('https://images.unsplash.com/photo-1501004318641-b39e6451bec6?auto=format&fit=crop&w=1500&q=80');
-    background-size: cover;
-    background-attachment: fixed;
-    margin: 0;
-    padding: 0;
-}
-.gradio-container {
-    max-width: 1000px;
-    margin: 40px auto;
-    background: rgba(255, 255, 255, 0.92);
-    padding: 40px;
-    border-radius: 20px;
-    box-shadow: 0 8px 32px rgba(0, 50, 0, 0.3);
-}
-h1, h2 {
-    color: #2e7d32;
-    text-align: center;
-}
-button {
-    font-weight: 600;
-    background-color: #4caf50 !important;
-    color: white !important;
-    border-radius: 10px !important;
-}
-button:hover {
-    background-color: #388e3c !important;
-}
-textarea, input, select {
-    border-radius: 10px !important;
-    padding: 8px;
-}
-.output-markdown {
-    background-color: rgba(255,255,255,0.95);
-    padding: 20px;
-    border-radius: 15px;
-    margin-top: 20px;
-    box-shadow: 0 4px 20px rgba(0,0,0,0.1);
-}
-"""
-
-# ���──────────────────── UI ─────────────────────
-
-with gr.Blocks(css=css) as app:
-    gr.Markdown("# 🌿 Plant Disease Detector")
-    gr.Markdown("Upload a plant leaf photo to diagnose disease and get treatment + fertilizer advice.")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="numpy", label="📷 Upload Leaf Image")
-            plant_input = gr.Text(label="🌱 Plant Type", placeholder="e.g., Tomato")
-            location_input = gr.Text(label="📍 Location", placeholder="e.g., Greenhouse A")
-            format_input = gr.Radio(["TXT"], label="📄 Report Format", value="TXT")
-            submit = gr.Button("🔬 Analyze")
-
-        with gr.Column(scale=2):
-            report_output = gr.Markdown(elem_classes=["output-markdown"])
-            plot_output = gr.Image(label="📊 Confidence Chart")
-            file_output = gr.File(label="📥 Download Diagnosis")
-
-    submit.click(
-        fn=predict,
-        inputs=[image_input, plant_input, location_input, format_input],
-        outputs=[report_output, plot_output, file_output]
-    )
-
-# ───────────────────── RUN ─────────────────────
-
+model = pipeline("image-classification", model="linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification", top_k=1)
+
+# Load mapping data
+def load_map(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return dict(line.strip().split(":", 1) for line in f if ":" in line)
+
+disease_map = load_map("diseases.txt")
+treatment_map = load_map("treatments.txt")
+fertilizer_map = load_map("fertilizers.txt")
+
+with open("critical_diseases.txt", "r") as f:
+    critical_diseases = set(line.strip() for line in f)
+
+def predict_disease(base64_img):
+    # Decode base64
+    try:
+        img_bytes = base64.b64decode(base64_img)
+        image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+    except Exception as e:
+        return {"error": f"Invalid image: {str(e)}"}
+
+    # Predict
+    try:
+        result = model(image)[0]
+        label = result["label"]
+        confidence = float(result["score"])
+        disease = disease_map.get(label, label)
+        treatment = treatment_map.get(disease, "Consult an expert for treatment.")
+        fertilizer = fertilizer_map.get(disease, "Use a general NPK 10-10-10 fertilizer.")
+
+        output = {
+            "disease_prediction": disease,
+            "confidence": round(confidence, 4),
+            "suggested_treatment": treatment,
+            "fertilizer_recommendation": fertilizer
+        }
+
+        # Alerts (for integration)
+        if confidence < 0.6 or disease in critical_diseases:
+            output["alert"] = True
+
+        return output
+    except Exception as e:
+        return {"error": f"Prediction failed: {str(e)}"}
+
+# Test hook
 if __name__ == "__main__":
-    app.launch()
+    with open("example_image.b64", "r") as f:
+        base64_img = f.read()
+    response = predict_disease(base64_img)
+    print(json.dumps(response, indent=2))