Create app.py

app.py

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+# Python script, Unicode text, UTF-8 text executable, with CRLF line terminators
+import requests
+import json
+from pprint import pprint
+import gradio as gr
+
+API_KEY = "2b109sYMpWyL50uehM9kUesDMO"  # Your API_KEY here
+PROJECT = "all";  # try specific floras: "weurope", "canada"
+api_endpoint = f"https://my-api.plantnet.org/v2/identify/{PROJECT}?api-key={API_KEY}&lang=zh"
+
+def identify_plant(image_paths, organs):
+    files = []
+    for image_path in image_paths:
+        image_data = open(image_path, 'rb')
+        files.append(('images', (image_path, image_data)))
+
+    data = {'organs': organs}
+    req = requests.Request('POST', url=api_endpoint, files=files, data=data)
+    prepared = req.prepare()
+
+    s = requests.Session()
+    response = s.send(prepared)
+    json_result = json.loads(response.text)
+
+    # Close the opened files
+    for _, (_, image_data) in files:
+        image_data.close()
+
+    return response.status_code, json_result
+
+
+def gradio_interface(files, organs):
+    image_paths = [file.name for file in files]
+    print(image_paths)
+    status_code, json_result = identify_plant(image_paths, organs)
+    
+    return json_result.get("bestMatch",None), json_result
+with gr.Blocks() as demo:
+    file_output = gr.File()
+    upload_button = gr.UploadButton("Click to Upload a File", file_types=["image", "video"], file_count="multiple")
+    organs_input = gr.CheckboxGroup(choices=["flower", "leaf", "fruit", "bark", "habit"])
+    best_match_text = gr.Textbox()
+    json_text = gr.JSON()
+    upload_button.upload(gradio_interface, inputs=[upload_button,organs_input], outputs = [best_match_text,json_text])
+
+#gr.Interface(fn=gradio_interface, inputs=[image_input, organs_input], outputs=outputs).launch()
+demo.launch(share=True)
+
+