Update app.py

app.py

@@ -1,11 +1,18 @@
 import gradio as gr
-import requests
 import os
-import json
 import google.generativeai as genai
+import logging
+
+# Configure Logging
+logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # Load environment variables
-genai.configure(api_key=os.environ["geminiapikey"])
+try:
+    genai.configure(api_key=os.environ["geminiapikey"])
+except KeyError:
+    logging.error("Error: 'geminiapikey' environment variable not found.")
+    exit(1)
+
 read_key = os.environ.get('HF_TOKEN', None)
 
 custom_css = """
@@ -15,14 +22,14 @@ custom_css = """
     background: #202020;
     padding: 20px;
     color: white;
-    border: 1 px solid white;
+    border: 1px solid white;
 }
 """
 
 def predict(prompt):
     # Create the model
     generation_config = {
-        "temperature": 0.3,
+        "temperature": 0.7,
         "top_p": 0.95,
         "top_k": 40,
         "max_output_tokens": 2048,
@@ -31,31 +38,35 @@ def predict(prompt):
 
     model = genai.GenerativeModel(
         model_name="gemini-1.5-pro",
-        #model_name="gemini-2.0-flash-exp",
         generation_config=generation_config,
     )
 
-    chat_session = model.start_chat(
-        history=[
-        ]
-    )
-    
-    response = chat_session.send_message(prompt, tools='google_search_retrieval')
-    #response = model.generate_content(contents=prompt, tools='google_search_retrieval')
-    return response.text
+    try:
+        contents_to_send = [genai.Content(parts=[prompt])]
+
+        response = model.generate_content(contents=contents_to_send, tools='google_search_retrieval')
+
+        if response and response.text:
+          return response.text
+        else:
+          logging.error(f"Unexpected response: {response}")
+          return "Error: Could not extract text from the response."
+    except Exception as e:
+      logging.error(f"An error occurred: {e}")
+      return f"An error occurred: {e}"
+
 
 # Create the Gradio interface
 with gr.Blocks(css=custom_css) as demo:
     with gr.Row():
-        details_output = gr.Markdown(label="answer", elem_id="md")        
-        #details_output = gr.Textbox(label="Ausgabe", value = f"\n\n\n\n")  
+        details_output = gr.Markdown(label="answer", elem_id="md")
+    with gr.Row():
+        ort_input = gr.Textbox(label="prompt", placeholder="ask anything...")
     with gr.Row():
-        ort_input = gr.Textbox(label="prompt", placeholder="ask anything...")      
-    with gr.Row():         
-        button = gr.Button("Senden")    
+        button = gr.Button("Senden")
 
     # Connect the button to the function
-    button.click(fn=predict, inputs=ort_input, outputs=details_output)   
+    button.click(fn=predict, inputs=ort_input, outputs=details_output)
 
 # Launch the Gradio application
 demo.launch()