Update app.py

app.py

@@ -19,11 +19,7 @@ warnings.filterwarnings('ignore')
 def get_env_or_secret(key_name: str, default: str = None):
     """Helper to read from Streamlit secrets first, then env vars."""
     try:
-        # Try Streamlit secrets first
-        if hasattr(st, 'secrets') and key_name in st.secrets:
-            return st.secrets[key_name]
-        # Fall back to environment variables
-        return os.getenv(key_name, default)
+        return st.secrets.get(key_name, os.getenv(key_name, default))
     except Exception:
         return os.getenv(key_name, default)
 
@@ -46,17 +42,25 @@ def get_active_llm_provider():
 def get_llm_summary(prompt: str, context: str = "") -> str:
     """
     Robust LLM summary using AIML /responses endpoint.
+    Changes:
+    - Wraps the prompt inside `input: {"text": ...}` as some endpoints expect.
+    - Ensures `response_format` is set to force textual output.
+    - Keeps the previous temperature-fallback (remove if unsupported).
+    - Retries once with a larger token budget (without temperature) if no text returned.
     """
     # Build final prompt safely
     if context:
-        full_prompt = f"{context}\nUser Query: {prompt}"
+        full_prompt = f"{context}
+
+{prompt}"
     else:
         full_prompt = prompt
 
-    api_key = get_env_or_secret("AI_ML_API_KEY")
+    api_key = get_env_or_secret("AI_ML_API_KEY") or os.getenv("AI_ML_API_KEY")
     if not api_key:
         return (
-            "AI Analysis unavailable — AI_ML_API_KEY not configured.\n"
+            "AI Analysis unavailable — AI_ML_API_KEY not configured.
+"
             "Go to Settings → Secrets → Create secret 'AI_ML_API_KEY' with your AI/ML API key."
         )
 
@@ -76,10 +80,11 @@ def get_llm_summary(prompt: str, context: str = "") -> str:
 
     # initial payload (wrap input in dict and force text response_format)
     base_payload = {
-        "model": "openai/gpt-4",
+        "model": "openai/gpt-5-2025-08-07",
         "input": {"text": full_prompt},
         "max_output_tokens": 1024,
         "temperature": 0.0,
+        "reasoning_effort": "low",
         "response_format": {"type": "text"}
     }
 
@@ -140,7 +145,9 @@ def get_llm_summary(prompt: str, context: str = "") -> str:
                             if isinstance(c, dict) and c.get("text"):
                                 texts.append(c["text"].strip())
 
-        return "\n".join(texts).strip()
+        return "
+
+".join(texts).strip()
 
     out_text = extract_text_from_response(data)
     # If we received only reasoning without text, try an explicit retry that forces text
@@ -150,6 +157,7 @@ def get_llm_summary(prompt: str, context: str = "") -> str:
             "model": base_payload["model"],
             "input": {"text": full_prompt},
             "max_output_tokens": 2048,
+            "reasoning_effort": "low",
             "response_format": {"type": "text"}
         }
         data2, err2 = call_api(retry_payload)
@@ -723,4 +731,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()