Update app.py

app.py

@@ -1,77 +1,82 @@
 import os
 import gradio as gr
 import requests
-import importlib.util
-from pathlib import Path
 
-# Define the model files content
-model_files = {
-    "gpt5_model.py": """
+# -----------------------------
+# GPT5 Model Class
+# -----------------------------
 class GPT5Model:
     def __init__(self, api_key):
         self.api_key = api_key
-        self.system_prompt = "You are GPT-5, the most advanced AI model available. Answer accurately, intelligently, and helpfully."
-        self.locked = False
+        self.system_prompt = (
+            "You are GPT-5, the most advanced AI model available. "
+            "Answer accurately, intelligently, and helpfully."
+        )
 
     def generate_response(self, prompt):
-        if self.locked:
-            return "Access locked. Only one prompt is allowed per session."
-        
         headers = {
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json"
         }
-        full_prompt = f"{self.system_prompt}\\nUser: {prompt}\\nGPT-5:"
+        full_prompt = f"{self.system_prompt}\nUser: {prompt}\nGPT-5:"
         data = {
             "prompt": full_prompt,
-            "max_tokens": 150
+            "max_tokens": 500
         }
-        import requests
         response = requests.post("https://api.pplx.ai/v1/generate", json=data, headers=headers)
-        
-        # Lock after first request
-        self.locked = True
-        return response.json()["choices"][0]["text"].strip()
-    """,
-    "gpt5_utils.py": """
-def load_model(api_key):
-    from gpt5_model import GPT5Model
-    return GPT5Model(api_key)
-    """
-}
 
-# Write the model files to disk
-for filename, content in model_files.items():
-    Path(filename).write_text(content.strip())
+        if response.status_code != 200:
+            return f"Error: {response.text}"
 
-# Import the load_model function dynamically
-spec = importlib.util.spec_from_file_location("gpt5_utils", "gpt5_utils.py")
-gpt5_utils = importlib.util.module_from_spec(spec)
-spec.loader.exec_module(gpt5_utils)
+        try:
+            return response.json()["choices"][0]["text"].strip()
+        except Exception as e:
+            return f"Error parsing response: {e}"
 
-# Get API key from environment
+# -----------------------------
+# Load API key
+# -----------------------------
 api_key = os.getenv("PPLX_API_KEY")
 if not api_key:
     raise ValueError("API key not found. Please set PPLX_API_KEY environment variable.")
 
-# Load the GPT5 model
-model = gpt5_utils.load_model(api_key)
+model = GPT5Model(api_key)
 
-# Response generation function
-def generate_response(prompt):
-    if len(prompt) > 1:
-        return "Error: Prompt must be exactly 1 character long."
-    return model.generate_response(prompt)
+# -----------------------------
+# UI Function
+# -----------------------------
+def chat_with_gpt5(user_input):
+    return model.generate_response(user_input)
 
-# Create Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# GPT5 Model Interface (One Prompt Only)")
+# -----------------------------
+# Build Better UI
+# -----------------------------
+with gr.Blocks(css="""
+    #title {text-align: center; font-size: 28px; font-weight: bold;}
+    #footer {text-align: center; font-size: 14px; color: gray;}
+""") as demo:
+    gr.Markdown("<div id='title'>🚀 GPT-5 Model Interface</div>")
     with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="Enter your prompt (1 character only)", placeholder="Enter a single character")
-            generate_btn = gr.Button("Generate Response")
-            output = gr.Textbox(label="Generated Response", interactive=False)
+        with gr.Column(scale=1):
+            chatbot = gr.Chatbot(height=400, label="GPT-5 Conversation")
+            with gr.Row():
+                txt = gr.Textbox(
+                    show_label=False,
+                    placeholder="Type your message here...",
+                    container=False
+                )
+                send_btn = gr.Button("Send", variant="primary")
 
-    generate_btn.click(generate_response, inputs=prompt, outputs=output)
+    gr.Markdown("<div id='footer'>Powered by GPT-5 API Simulation | © 2025</div>")
+
+    # Handle chat logic
+    def respond(message, chat_history):
+        bot_reply = chat_with_gpt5(message)
+        chat_history.append(("You: " + message, "GPT-5: " + bot_reply))
+        return "", chat_history
+
+    send_btn.click(respond, [txt, chatbot], [txt, chatbot])
+    txt.submit(respond, [txt, chatbot], [txt, chatbot])
 
 demo.launch()
+