Created hugging face space first time

app.py

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+from keras.models import load_model
+from fastapi import FastAPI
+import gradio as gr
+import requests
+import numpy as np
+from PIL import Image
+from tensorflow import keras
+from io import BytesIO
+import uvicorn
+from pyModel import Item
+
+
+
+app = FastAPI()
+
+
+
+@app.post('/post')
+def newPost(str):
+    input_image = url_to_img(str)
+    processed_image = process(input_image)
+    crop = crop_predict(processed_image)
+    disease = disease_predict(processed_image, crop)
+    return [crop,disease]
+  
+
+def url_to_img(image_url):
+
+    # Load the image from the URL
+    response = requests.get(image_url)
+    input_image = Image.open(BytesIO(response.content))
+    return input_image
+
+
+def process(input_image):
+
+    # Preprocess the image to match model's input dimensions
+    input_image = input_image.resize((224, 224))  # Adjust the dimensions
+    input_image = np.array(input_image) / 255.0  # Normalize pixel values to [0, 1]
+    processed_image = np.expand_dims(input_image, axis=0)  # Add batch dimension
+    return processed_image
+
+def crop_predict(processed_image):
+
+    # Load the trained model
+    model = keras.models.load_model('./crop_model.h5')
+    # Make predictions
+    predictions = model.predict(processed_image)
+    # Process the predictions (e.g., get class labels)
+    class_labels = ["Corn", "Potato", "Rice", "Wheat"] 
+    predicted_class = np.argmax(predictions, axis=1)
+    predicted_label = class_labels[predicted_class[0]]
+    return predicted_label
+
+def disease_predict(processed_image, crop):
+
+    if crop == "Potato":
+        # Load the trained model
+        model = keras.models.load_model('./potato_model.h5')
+        # Process the predictions (e.g., get class labels)
+        class_labels = ["Potato_Early_Blight", "Potato_Healthy", "Potato_Late_Blight"] 
+    
+    elif crop == "Corn":
+        # Load the trained model
+        model = keras.models.load_model('./corn_model.h5')
+        # Process the predictions (e.g., get class labels)
+        class_labels = ["Corn_Common_Rust", "Corn_Gray_Leaf_Spot", "Corn_Healthy", "Corn_Northern_Leaf_Blight"] 
+
+    elif crop == "Rice":
+        # Load the trained model
+        model = keras.models.load_model('./rice_model.h5')
+        # Process the predictions (e.g., get class labels)
+        class_labels = ["Rice_Brown_Spot", "Rice_Healthy", "Rice_Leaf_Blast", "Rice_Neck_Blast"] 
+
+    elif crop == "Wheat":
+        # Load the trained model
+        model = keras.models.load_model('./wheat_model.h5')
+        # Process the predictions (e.g., get class labels)
+        class_labels = ["Wheat_Brown_Rust", "Wheat_Healthy", "Wheat_Yellow_Rust"]
+
+    # Make predictions
+    predictions = model.predict(processed_image)
+    predicted_class = np.argmax(predictions, axis=1)
+    predicted_label = class_labels[predicted_class[0]]
+    return predicted_label
+
+
+# iface = gr.Interface(fn = newPost , inputs=['text'] , outputs=['text'])
+
+# iface.launch(share=True)

corn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11ae70a4bed9d5152d54c9321b3ea9a46e0222f89eed87c0f54482441a02cadb
+size 134085664

crop_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b2a84a50154840d784718f5bf94af1618195d97acafc25df4029c69e6a1ec1d
+size 134085664

potato_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e2cb1f05f7837d4e76601e2d61b77ecc4d41db1367a411bded4ee0800d07b87
+size 134083104

rice_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f5ecd26b7b9cf977877bcfb966df594af446877aa5f6b1300b40236081bce74
+size 134085664

wheat_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78befd2189d6fe820442f896d652e9bf48db70f0073196a2c6aff2cdfad1d02a
+size 134083104