Update app.py

app.py

@@ -1,112 +1,11 @@
-import gradio as gr
-from tensorflow.keras.utils import img_to_array,load_img
-from keras.models import load_model
-import numpy as np
+import requests
 
-# Load the pre-trained model from the local path
-model_path = 'peach.h5'
-model = load_model(model_path)  # Load the model here
+API_URL = "https://api-inference.huggingface.co/models/your-username/your-model-name"
+headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_TOKEN"}
 
-def predict_disease(image_file, model, all_labels):
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
-    try:
-        # Load and preprocess the image
-        img = load_img(image_file, target_size=(224, 224))  # Use load_img from tensorflow.keras.utils
-        img_array = img_to_array(img)
-        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-        img_array = img_array / 255.0  # Normalize the image
-
-        # Predict the class
-        predictions = model.predict(img_array)  # Use the loaded model here
-        predicted_class = np.argmax(predictions[0])
-
-        # Get the predicted class label
-        predicted_label = all_labels[predicted_class]
-
-        # Print the predicted label to the console
-
-        if predicted_label=='Peach Healthy':
-            predicted_label = predicted_label = """<h3 align="center">Peach Healthy</h3><br><br>
-          <center>No need use Pesticides</center>"""
-        elif predicted_label=='Peach Bacterial Spot':
-            predicted_label = """
-            <style>
-                      li{
-                        font-size: 15px;
-                        margin-left: 90px;
-                        margin-top: 15px;
-                        margin-bottom: 15px;
-                      }
-                      h4{
-                        font-size: 17px;
-                        margin-top: 15px;
-                      }
-                      h4:hover{
-                        cursor: pointer;
-                      }
-
-                      h3:hover{
-                        cursor: pointer;
-                        color: blue;
-                        transform: scale(1.3);
-                      }
-                      .note{
-                        text-align: center;
-                        font-size: 16px;
-                      }
-                      p{
-                        font-size: 13px;
-                        text-align: center;
-                      }
-
-                </style>
-                <h3><center><b>Peach Bacterial Spot</b></center></h3>
-                <h4>PESTICIDES TO BE USED:</h4>
-                <ul>
-                    <li>1. Copper oxychloride (Kocide)</li>
-                    <li>2. Streptomycin (Streptomycin sulfate)</li>
-                    <li>3. Tetracycline (Agrimycin)</li>
-                    <li>4. Oxytetracycline (Terramycin)</li>
-
-
-                </ul>
-                <p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
-                <p>Be sure to follow local regulations and guidelines for application</p>
-
-
-            """
-
-
-
-        else:
-          predicted_label = """<h3 align="center">Choose Correct image</h3><br><br>
-          """
-
-        return predicted_label
-
-
-    except Exception as e:
-        print(f"Error: {e}")
-        return None
-
-# List of class labels
-all_labels = [
-    'Peach Healthy',
-    'Peach Bacterial Spot'
-]
-
-# Define the Gradio interface
-def gradio_predict(image_file):
-    return predict_disease(image_file, model, all_labels)  # Pass the model to the function
-
-# Create a Gradio interface
-gr_interface = gr.Interface(
-    fn=gradio_predict,  # Function to call for predictions
-    inputs=gr.Image(type="filepath"),  # Upload image as file path
-    outputs="html",  # Output will be the class label as text
-    title="Peach Disease Predictor",
-    description="Upload an image of a plant to predict the disease.",
-)
-
-# Launch the Gradio app
-gr_interface.launch()
+data = query({"inputs": "your input text"})
+print(data)