Update app.py

app.py

@@ -1,21 +1,11 @@
 import gradio as gr
+import requests
 import os
+import json
 import google.generativeai as genai
-import logging
-import time
-import backoff
-import google.ai.generativelanguage as glm
-
-# Configure Logging
-logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # Load environment variables
-try:
-    genai.configure(api_key=os.environ["geminiapikey"])
-except KeyError:
-    logging.error("Error: 'geminiapikey' environment variable not found.")
-    exit(1)
-
+genai.configure(api_key=os.environ["geminiapikey"])
 read_key = os.environ.get('HF_TOKEN', None)
 
 custom_css = """
@@ -25,14 +15,14 @@ custom_css = """
     background: #202020;
     padding: 20px;
     color: white;
-    border: 1px solid white;
+    border: 1 px solid white;
 }
 """
 
 def predict(prompt):
     # Create the model
     generation_config = {
-        "temperature": 0.7,
+        "temperature": 0.3,
         "top_p": 0.95,
         "top_k": 40,
         "max_output_tokens": 2048,
@@ -40,36 +30,32 @@ def predict(prompt):
     }
 
     model = genai.GenerativeModel(
-        model_name="gemini-1.5-pro",
+        #model_name="gemini-1.5-pro",
+        model_name="gemini-2.0-flash-exp",
         generation_config=generation_config,
     )
 
-    # Create the tools configuration
-
-    tools_config = glm.ToolConfig(
-        function_declarations=[],
-        search_queries=[prompt],
+    chat_session = model.start_chat(
+        history=[
+        ]
     )
-
-    response = model.generate_content(
-        contents=[prompt],  # Directly pass the prompt
-        tools=[tools_config]
-    )
-
-    if response and response.text:
-        return response.text
+    
+    response = chat_session.send_message(prompt)
+    #response = model.generate_content(contents=prompt, tools='google_search_retrieval')
+    return response.text
 
 # Create the Gradio interface
 with gr.Blocks(css=custom_css) as demo:
     with gr.Row():
-        details_output = gr.Markdown(label="answer", elem_id="md")
-    with gr.Row():
-        ort_input = gr.Textbox(label="prompt", placeholder="ask anything...")
+        details_output = gr.Markdown(label="answer", elem_id="md")        
+        #details_output = gr.Textbox(label="Ausgabe", value = f"\n\n\n\n")  
     with gr.Row():
-        button = gr.Button("Senden")
+        ort_input = gr.Textbox(label="prompt", placeholder="ask anything...")      
+    with gr.Row():         
+        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()