Update space

app.py

@@ -1,63 +1,35 @@
+import keras
+from keras.models import load_model
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import cv2
+
+my_model = load_model('Final_Chicken_disease_model.h5', compile=True)
+auth_model = load_model('auth_model.h5', compile=True)
+name_disease = {0: 'Coccidiosis', 1: 'Healthy', 2: 'New Castle Disease', 3: 'Salmonella'}
+result = {0: 'Critical', 1: 'No issue', 2: 'Critical', 3: 'Critical'}
+recommend = {0: 'Panadol', 1: 'You have no need of Medicine', 2: 'Percetamol', 3: 'Ponston'}
+
+
+def predict(image):
+    image_check = cv2.resize(image, (224, 224))
+    indx = auth_model.predict(image_check.reshape(1, 224, 224, 3)).argmax()
+    if indx == 0:
+        image = cv2.resize(image, (224, 224))
+        indx = my_model.predict(image.reshape(1, 224, 224, 3)).argmax()
+        name = name_disease.get(indx)
+        status = result.get(indx)
+        recom = recommend.get(indx)
+        return name, status, recom
+    else:
+        name = 'Unknown Image'
+        status = 'N/A'
+        recom = 'N/A'
+        return name, status, recom
+
+
+interface = gr.Interface(fn=predict, inputs=[gr.Image(label='upload Image')],
+                         outputs=[gr.components.Textbox(label="Disease Name"),
+                                  gr.components.Textbox(label="result"),
+                                  gr.components.Textbox(label='Medicine Recommend')],
+                         examples=[['disease.jpg'], ['ncd.jpg']])
+interface.launch(debug=True)

requirements.txt

@@ -1 +1,4 @@
-huggingface_hub==0.22.2
+huggingface_hub==0.22.2
+keras
+gradio
+cv2