Update app.py

app.py

@@ -3,19 +3,12 @@ from PIL import Image
 import torch
 from transformers import AutoModelForImageClassification, AutoFeatureExtractor
 from groq import Groq
-from dotenv import load_dotenv
-import os
 
-# Load environment variables from the .env file
-load_dotenv()
-
-# Securely get the GROQ API key from environment variables
-groq_api_key = os.getenv("GROQ_API_KEY")
-if not groq_api_key:
+# Securely load GROQ API key
+grog_api_key = "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"
+if not grog_api_key:
     raise ValueError("GROQ_API_KEY environment variable not set.")
-
-# Initialize the client with the API key
-client = Groq(api_key=groq_api_key)
+client = Groq(api_key=grog_api_key)
 
 # Expanded dictionary with treatments for various diseases
 disease_treatments = {
@@ -93,7 +86,7 @@ if uploaded_file is not None:
     top_indices = top_indices[0].tolist()
 
     # Define a confidence threshold
-    confidence_threshold = 0.7
+    confidence_threshold = 0.5
     class_labels = model.config.id2label
     predicted_disease = "Unknown Disease"
     predicted_confidence = top_probs[0]
@@ -149,7 +142,6 @@ if uploaded_file is not None:
                     "content": (
                         "You are a plant disease analysis assistant. Your task is to provide a comprehensive, actionable diagnosis report."
                         "The report should include the predicted disease, its symptoms, recommended treatments, and prevention tips. "
-                         
                         "Ensure the report is actionable and easy to understand for non-experts in agriculture."
                     )
                 },
@@ -160,7 +152,7 @@ if uploaded_file is not None:
             ],
             model="mixtral-8x7b-32768",  # Adjust this to the model you're using
             temperature=0.7,  # Adjust to balance creativity and precision
-            max_tokens=5000  # Limit to ensure the output is not cut off
+            max_tokens=1500  # Limit to ensure the output is not cut off
         )
 
         # Display the generated report in the Streamlit app