Update app.py

app.py

@@ -1,91 +1,69 @@
-import os
-import openai
 import gradio as gr
-from PIL import Image
-import io
-import base64
-
-# Set your OpenAI API key here or use environment variables
-openai.api_key = "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"
+from azure.ai.inference import ChatCompletionsClient
+from azure.ai.inference.models import (
+    SystemMessage,
+    UserMessage,
+    TextContentItem,
+    ImageContentItem,
+    ImageUrl,
+    ImageDetailLevel,
+)
+from azure.core.credentials import AzureKeyCredential
 
-def detect_disease_and_get_remedies(image):
-    """
-    Detects leaf disease from the uploaded image and provides remedies.
+# Azure API credentials
+token = "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"
+endpoint = "https://models.inference.ai.azure.com"
+model_name = "Llama-3.2-90B-Vision-Instruct"
 
-    Args:
-        image (PIL.Image): Uploaded leaf image.
+# Initialize the ChatCompletionsClient
+client = ChatCompletionsClient(
+    endpoint=endpoint,
+    credential=AzureKeyCredential(token),
+)
 
-    Returns:
-        Tuple[str, str]: Detected disease and corresponding remedies.
-    """
+# Define the function to handle the image and get predictions
+def analyze_leaf_disease(image):
     try:
-        # Convert PIL Image to bytes
-        buffered = io.BytesIO()
-        image.save(buffered, format="JPEG")
-        img_bytes = buffered.getvalue()
-
-        # Encode image to base64
-        img_base64 = base64.b64encode(img_bytes).decode('utf-8')
-
-        # Create a system prompt to guide the model
-        system_prompt = "You are an agricultural expert specializing in plant diseases."
-
-        # Step 1: Detect Disease
-        disease_prompt = (
-            "Analyze the following image of a plant leaf and identify any diseases present. "
-            "Provide only the name of the disease without additional explanation.\n\n"
-            "Image (base64 encoded): {}\n\nDisease:".format(img_base64)
-        )
-
-        # Send the image to OpenAI's ChatCompletion API for disease detection
-        response = openai.ChatCompletion.create(
-            model="gpt-4-vision",  # Assuming GPT-4 with vision capabilities
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": disease_prompt}
-            ],
-            temperature=0
-        )
-
-        disease = response.choices[0].message['content'].strip()
+        # Convert the uploaded image to a compatible format (e.g., save it locally)
+        image.save("uploaded_image.jpg")
 
-        if not disease or disease.lower() in ["no disease detected", "healthy"]:
-            return "No Disease Detected", "The leaf appears to be healthy. Maintain regular plant care to prevent future issues."
-
-        # Step 2: Get Remedies for the Detected Disease
-        remedy_prompt = (
-            "Provide detailed remedies and treatment options for the following plant leaf disease: {}. "
-            "Include both chemical and organic treatment methods if applicable.".format(disease)
-        )
-
-        remedy_response = openai.ChatCompletion.create(
-            model="gpt-4",
+        # Prepare and send the request to the Azure API
+        response = client.complete(
             messages=[
-                {"role": "system", "content": "You are a knowledgeable agricultural advisor."},
-                {"role": "user", "content": remedy_prompt}
+                SystemMessage(
+                    content="You are a helpful assistant that describes leaf disease in detail."
+                ),
+                UserMessage(
+                    content=[
+                        TextContentItem(text="What's the leaf disease in this image?"),
+                        ImageContentItem(
+                            image_url=ImageUrl.load(
+                                image_file="uploaded_image.jpg",
+                                image_format="jpg",
+                                detail=ImageDetailLevel.LOW,
+                            )
+                        ),
+                    ],
+                ),
             ],
-            temperature=0.7,
-            max_tokens=500
+            model=model_name,
         )
 
-        remedies = remedy_response.choices[0].message['content'].strip()
-
-        return disease, remedies
+        # Extract and return the response content
+        return response.choices[0].message.content
 
     except Exception as e:
-        return "Error", f"An error occurred: {str(e)}"
+        return f"An error occurred: {e}"
 
 # Define the Gradio interface
-iface = gr.Interface(
-    fn=detect_disease_and_get_remedies,
-    inputs=gr.Image(type="pil", label="Upload Leaf Image"),
-    outputs=[
-        gr.Textbox(label="Detected Disease"),
-        gr.Textbox(label="Remedies")
-    ],
+interface = gr.Interface(
+    fn=analyze_leaf_disease,
+    inputs=gr.Image(type="file", label="Upload an Image of a Leaf"),
+    outputs="text",
     title="Leaf Disease Detector",
-    description="Upload an image of a leaf, and the system will detect any diseases present and provide remedies."
+    description="Upload an image of a leaf, and this tool will identify the disease affecting it.",
 )
 
+# Launch the interface
 if __name__ == "__main__":
-    iface.launch()
+    interface.launch()