Update app.py

app.py

@@ -198,16 +198,39 @@ def extract_pdf_note(file_path: str) -> str:
         return ""
 
 def call_gemini_with_prompt(content_text: str, note_text: str, question: str, model_choice: str, temperature: float, top_p: float):
-    """Gửi bảng + note vào Gemini"""
+    """Gửi bảng + note vào Gemini (ưu tiên prompt tùy chỉnh nếu có)"""
     api_key = os.environ.get("GOOGLE_API_KEY", DEFAULT_API_KEY)
     genai.configure(api_key=api_key)
+
     model = genai.GenerativeModel(
         model_name=INTERNAL_MODEL_MAP.get(model_choice, "gemini-2.5-flash"),
-        generation_config={"temperature": temperature, "top_p": top_p}
+        generation_config={
+            "temperature": float(temperature),
+            "top_p": float(top_p)
+        }
     )
-    prompt = f"{PROMPT_FREIGHT_JSON}\n\nBelow is the extracted CSV data:\n{content_text}\n\nBelow are the notes:\n{note_text}\n\n{question or ''}"
+
+    # Nếu user không nhập câu hỏi riêng, dùng prompt chuẩn FREIGHT_JSON
+    base_prompt = question.strip() if question and question.strip() else PROMPT_FREIGHT_JSON
+
+    prompt = f"""
+                {base_prompt}
+                
+                Below is the extracted CSV data:
+                {content_text}
+                
+                Below are the notes extracted from the PDF (e.g. Valid From, Origin, Remark, Package Type rules):
+                {note_text}
+                
+                Please analyze all data and generate the JSON output following the schema above.
+                """
+
+    print("🧠 Sending prompt to Gemini...")
     response = model.generate_content(prompt)
-    return getattr(response, "text", str(response))
+    result_text = getattr(response, "text", str(response))
+
+    return result_text
+
 
 # ================== MAIN ROUTER ==================
 def run_process(file, question, model_choice, temperature, top_p, external_api_url):