Update app.py

app.py

@@ -1,5 +1,4 @@
-from flask import Flask, render_template, request, jsonify
-import os
+import gradio as gr
 from azure.ai.inference import ChatCompletionsClient
 from azure.ai.inference.models import (
     SystemMessage,
@@ -10,14 +9,11 @@ from azure.ai.inference.models import (
     ImageDetailLevel,
 )
 from azure.core.credentials import AzureKeyCredential
-from googletrans import Translator
-
-app = Flask(__name__)
 
 # Azure API credentials
 token = "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"
 endpoint = "https://models.inference.ai.azure.com"
-model_name = "Llama-3.2-90B-Vision-Instruct"
+model_name = "gpt-4o"
 
 # Initialize the ChatCompletionsClient
 client = ChatCompletionsClient(
@@ -25,20 +21,18 @@ client = ChatCompletionsClient(
     credential=AzureKeyCredential(token),
 )
 
-translator = Translator()
-
-# Analyze leaf disease
-def analyze_leaf_disease(image_path):
+# Define the function to handle the image and get predictions
+def analyze_leaf_disease(image_path, leaf_type):
     try:
         # Prepare and send the request to the Azure API
         response = client.complete(
             messages=[
                 SystemMessage(
-                    content="You are a helpful assistant that describes leaf disease in detail."
+                    content=f"You are a subject matter expert that describes leaf disease in detail for {leaf_type} leaves."
                 ),
                 UserMessage(
                     content=[
-                        TextContentItem(text="What's the leaf disease in this image?"),
+                        TextContentItem(text="What's the name of the leaf disease in this image and what is the confidence score? What is the probable reason? What are the medicine or stops to prevent the disease"),
                         ImageContentItem(
                             image_url=ImageUrl.load(
                                 image_file=image_path,
@@ -58,42 +52,35 @@ def analyze_leaf_disease(image_path):
     except Exception as e:
         return f"An error occurred: {e}"
 
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/analyze', methods=['POST'])
-def analyze():
-    if 'image' not in request.files:
-        return jsonify({"error": "No file uploaded"}), 400
-
-    file = request.files['image']
-    leaf_type = request.form.get('leaf_type')
-
-    if file:
-        file_path = os.path.join('uploads', file.filename)
-        file.save(file_path)
-
-        # Analyze the image
-        result = analyze_leaf_disease(file_path)
-
-        # Remove the uploaded file
-        os.remove(file_path)
-
-        return jsonify({"result": result})
-
-@app.route('/translate', methods=['POST'])
-def translate():
-    data = request.get_json()
-    text = data.get('text')
+# Define the Gradio interface
+def handle_proceed(image_path, leaf_type):
+    # Display detecting status
+    detecting_status = "Detecting..."
+    result = analyze_leaf_disease(image_path, leaf_type)
+    # Clear detecting status after processing
+    return "", result
+
+with gr.Blocks() as interface:
+    with gr.Row():
+        gr.Markdown("""
+        # Leaf Disease Detector
+        Upload a leaf image, select the leaf type, and let the AI analyze the disease.
+        """)
+
+    with gr.Row():
+        image_input = gr.Image(type="filepath", label="Upload an Image or Take a Photo")
+        leaf_type = gr.Dropdown(
+            choices=["Tomato", "Tobacco", "Corn", "Paddy", "Maze", "Potato", "Wheat"],
+            label="Select Leaf Type",
+        )
+        proceed_button = gr.Button("Proceed")
 
-    if not text:
-        return jsonify({"error": "No text provided"}), 400
+    with gr.Row():
+        detecting_label = gr.Label("Detecting...", visible=False)
+        output_box = gr.Textbox(label="Results", placeholder="Results will appear here.")
 
-    translated = translator.translate(text, src='en', dest='bn')
-    return jsonify({"translated_text": translated.text})
+    # Update the detecting_label and result in outputs
+    proceed_button.click(handle_proceed, inputs=[image_input, leaf_type], outputs=[detecting_label, output_box])
 
-if __name__ == '__main__':
-    if not os.path.exists('uploads'):
-        os.makedirs('uploads')
-    app.run(debug=True)
+if __name__ == "__main__":
+    interface.launch()