Update app.py

app.py

@@ -1,17 +1,12 @@
 import os
+import json
 import gradio as gr
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import torch
 import numpy as np
-import requests
 import logging
 
-# Load Hugging Face API Key from Secrets
-HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
-if not HUGGINGFACE_API_KEY:
-    raise ValueError("❌ Missing Hugging Face API Key. Please set it in Hugging Face Secrets.")
-
 # Configure Logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
@@ -25,24 +20,23 @@ except Exception as e:
     logging.error(f"❌ Failed to load model: {str(e)}")
     raise RuntimeError("Failed to load the model. Please check the logs for details.")
 
-# Function to Get AI-Powered Treatment Suggestions
-def get_treatment_suggestions(disease_name):
-    url = "https://api-inference.huggingface.co/models/OpenAGI/agriculture-gpt"
-    headers = {"Authorization": f"Bearer {HUGGINGFACE_API_KEY}"}
-    data = {"inputs": f"What are the treatment options for {disease_name} in plants?"}
-
+# Load Treatment Data
+treatment_file = os.path.join(os.getcwd(), "treatments.json")
+if os.path.exists(treatment_file):
     try:
-        response = requests.post(url, headers=headers, json=data)
-        response_data = response.json()
+        with open(treatment_file, "r", encoding="utf-8") as f:
+            treatment_data = json.load(f)
+        logging.info("✅ Treatment data loaded successfully.")
+    except json.JSONDecodeError:
+        logging.error("❌ Invalid JSON format in treatments.json.")
+        treatment_data = {}
+else:
+    logging.warning("⚠️ treatments.json not found. Using empty treatment data.")
+    treatment_data = {}
 
-        if response.status_code == 200 and isinstance(response_data, list):
-            return response_data[0].get("generated_text", "No treatment information found.")
-        else:
-            logging.error(f"API Error {response.status_code}: {response_data}")
-            return f"API Error {response.status_code}: {response_data.get('error', 'Unknown error')}"
-    except Exception as e:
-        logging.error(f"Error fetching treatment suggestions: {str(e)}")
-        return "❌ Error retrieving treatment details."
+# Function to Get Treatment Suggestions
+def get_treatment_suggestions(disease_name):
+    return treatment_data.get(disease_name, "⚠️ No treatment suggestions available for this disease.")
 
 # Define Prediction Function
 def predict(image):
@@ -54,14 +48,14 @@ def predict(image):
         logits = outputs.logits
         predicted_class_idx = logits.argmax(-1).item()
         predicted_label = model.config.id2label[predicted_class_idx]
-
-        # Get AI-generated treatment suggestions
+        
+        # Get treatment suggestions
         treatment = get_treatment_suggestions(predicted_label)
-
+        
         return f"🌱 **Predicted Disease:** {predicted_label}\n💊 **Treatment:** {treatment}"
     except Exception as e:
-        logging.error(f"Prediction failed: {str(e)}")
-        return f"❌ Prediction failed: {str(e)}"
+        logging.error(f"❌ Prediction failed: {str(e)}")
+        return f"❌ **Error:** Unable to process image. Please try again."
 
 # Gradio Interface
 iface = gr.Interface(
@@ -69,7 +63,7 @@ iface = gr.Interface(
     inputs=gr.Image(type="numpy", label="Upload or capture plant image"),
     outputs=gr.Textbox(label="Result"),
     title="🌿 AI-Powered Plant Disease Detector",
-    description="📷 Upload a plant leaf image to detect diseases and get AI-powered treatment suggestions.",
+    description="📸 Upload a plant leaf image to detect diseases and receive treatment suggestions.",
     allow_flagging="never",
 )