Update app.py

app.py

@@ -1,34 +1,52 @@
                                                                            
 import os
-
 import subprocess
-
 import time
-
 import gradio as gr
-
 import numpy as np
-
 import tensorflow as tf
-
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import h5py     
+import google.generativeai as genai
+
+genai.configure(api_key="AIzaSyAzv4VAm5hsr-Lek3ARywH6wcY6zEGVhSw")
+
+# Set up generation configuration
+generation_config = {
+    "temperature": 1,
+    "top_p": 0.95,
+    "top_k": 0,
+    "max_output_tokens": 8192,
+}
+
+# Safety settings to filter harmful content
+safety_settings = [
+    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+]
 
-import h5py                                                                                                                              
-# Set the environment variable for the Ollama model
-
-OLLAMA_MODEL = 'dolphin-mistral:v2.8'
-
-os.environ['OLLAMA_MODEL'] = OLLAMA_MODEL
-
-
-
-# Start Ollama as a background process
-
-command = "nohup ollama serve &"
-
-process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+# Set system message for the model
+system_instruction = (
+    "You are Dr. Bot, a medical assistant specializing in breast cancer. "
+    "Your role is to provide accurate information about breast cancer types, symptoms, "
+    "screening guidelines, treatment options, and supportive resources. "
+    "Offer compassionate support and respond to patient inquiries with empathy and evidence-based information."
+)
 
-time.sleep(5)  # Wait for the server to start
+# Load Gemini model
+model = genai.GenerativeModel(
+    model_name="gemini-1.5-pro-latest",
+    generation_config=generation_config,
+    system_instruction=system_instruction,
+    safety_settings=safety_settings
+)
+# Function to interact with the Gemini model
+def query_model(input_text):
+    convo = model.start_chat(history=[{"role": "user", "parts": [input_text]}])
+    response = convo.send_message("YOUR_USER_INPUT")
+    return convo.last.text
 
 f = h5py.File("best_model_2.h5", mode="r+")
 model_config_string = f.attrs.get("model_config")