issue fixes

.env

@@ -3,4 +3,4 @@ TRANSFORMERS_CACHE=/tmp/huggingface
 XDG_CACHE_HOME=/tmp
 TORCH_HOME=/tmp/torch
 WHISPER_CACHE=/tmp/whisper
-UPLOAD_DIR=/tmp/uploads
+UPLOAD_DIR=/tmp/uploads

ai_med_extract/api/routes.py

@@ -505,10 +505,11 @@ def register_routes(app, agents):
             except Exception as e:
                 return jsonify({"error": f"Summarizer model load failed: {str(e)}"}), 500
 
-            # Use a Windows-friendly temp directory
+            # Use platform-agnostic temp directory
             import uuid
             from werkzeug.utils import secure_filename
-            temp_dir = os.path.join(os.environ.get('TEMP', 'C:\\Temp'), 'audio_uploads')
+            import tempfile
+            temp_dir = os.path.join(tempfile.gettempdir(), 'audio_uploads')
             os.makedirs(temp_dir, exist_ok=True)
             temp_filename = f"{uuid.uuid4()}_{secure_filename(audio_file.filename)}"
             temp_path = os.path.join(temp_dir, temp_filename)
@@ -736,158 +737,3 @@ def register_routes(app, agents):
     @app.route("/")
     def home():
         return "Medical Data Extraction API is running!", 200
-
-    @app.route("/extract_medical_data_questions", methods=["POST"])
-    def extract_medical_data_questions():
-        data = request.json
-        qa_model_name = data.get("qa_model_name")
-        qa_model_type = data.get("qa_model_type")
-        if "extracted_data" not in data:
-            return jsonify({"error": "Missing 'extracted_data' in request"}), 400
-        if not qa_model_name or not qa_model_type:
-            return jsonify({"error": "Missing 'qa_model_name' or 'qa_model_type'"}), 400
-        try:
-            qa_pipeline = get_qa_pipeline(qa_model_type, qa_model_name)
-        except Exception as e:
-            return jsonify({"error": f"Could not load model: {str(e)}"}), 500
-        questions = {
-            "Patient Name": "What is the patient's name?",
-            "Age": "What is the patient's age?",
-            "Gender": "What is the patient's gender?",
-            "Date of Birth": "What is the patient's date of birth?",
-            "Patient ID": "What is the patient ID?",
-            "Reason for Visit": "What is the reason for the patient's visit?",
-            "Physician": "Who is the physician in charge of the patient?",
-            "Test Date": "What is the test date?",
-            "Hemoglobin": "What is the patient's hemoglobin level?",
-            "Blood Glucose (Fasting)": "What is the patient's fasting blood glucose level?",
-            "Total Cholesterol": "What is the total cholesterol level?",
-            "LDL Cholesterol": "What is the LDL cholesterol level?",
-            "HDL Cholesterol": "What is the HDL cholesterol level?",
-            "Serum Creatinine": "What is the serum creatinine level?",
-            "Vitamin D (25-OH)": "What is the patient's Vitamin D level?",
-            "Height": "What is the patient's height?",
-            "Weight": "What is the patient's weight?",
-            "Blood Pressure (Systolic)": "What is the patient's systolic blood pressure?",
-            "Blood Pressure (Diastolic)": "What is the patient's diastolic blood pressure?",
-            "Recommendations": "What are the recommendations based on the test results?",
-        }
-        structured_response = {"extracted_data": []}
-        for file_data in data["extracted_data"]:
-            filename = file_data.get("file", "unknown_file")
-            context = file_data.get("extracted_text", "").strip()
-            if not context:
-                structured_response["extracted_data"].append(
-                    {
-                        "file": filename,
-                        "medical_terms": "No data extracted",
-                        "categorized_data": [],
-                    }
-                )
-                continue
-            extracted_info = {}
-            for key, question in questions.items():
-                try:
-                    result = run_qa_pipeline(qa_pipeline, question, context)
-                    extracted_info[key] = clean_result(
-                        result.get("answer", "Not Available")
-                    )
-                except Exception:
-                    extracted_info[key] = "Error extracting"
-            categorized_data = [
-                {
-                    "name": "Patient Information",
-                    "fields": [
-                        {
-                            "label": "Patient Name",
-                            "value": extracted_info.get("Patient Name", ""),
-                        },
-                        {
-                            "label": "Date of Birth",
-                            "value": extracted_info.get("Date of Birth", ""),
-                        },
-                        {"label": "Gender", "value": extracted_info.get("Gender", "")},
-                        {
-                            "label": "Patient ID",
-                            "value": extracted_info.get("Patient ID", ""),
-                        },
-                    ],
-                },
-                {
-                    "name": "Vitals",
-                    "fields": [
-                        {"label": "Height", "value": extracted_info.get("Height", "")},
-                        {"label": "Weight", "value": extracted_info.get("Weight", "")},
-                        {
-                            "label": "Blood Pressure",
-                            "value": f"{extracted_info.get('Blood Pressure (Systolic)', '')}/{extracted_info.get('Blood Pressure (Diastolic)', '')} mmHg",
-                        },
-                        {
-                            "label": "Hemoglobin",
-                            "value": extracted_info.get("Hemoglobin", ""),
-                        },
-                        {
-                            "label": "Serum Creatinine",
-                            "value": extracted_info.get("Serum Creatinine", ""),
-                        },
-                    ],
-                },
-                {
-                    "name": "Lab Results",
-                    "fields": [
-                        {
-                            "label": "Blood Glucose (Fasting)",
-                            "value": extracted_info.get("Blood Glucose (Fasting)", ""),
-                        },
-                        {
-                            "label": "Total Cholesterol",
-                            "value": extracted_info.get("Total Cholesterol", ""),
-                        },
-                        {
-                            "label": "LDL Cholesterol",
-                            "value": extracted_info.get("LDL Cholesterol", ""),
-                        },
-                        {
-                            "label": "HDL Cholesterol",
-                            "value": extracted_info.get("HDL Cholesterol", ""),
-                        },
-                        {
-                            "label": "Vitamin D (25-OH)",
-                            "value": extracted_info.get("Vitamin D (25-OH)", ""),
-                        },
-                    ],
-                },
-                {
-                    "name": "Medical Notes",
-                    "fields": [
-                        {
-                            "label": "Reason for Visit",
-                            "value": extracted_info.get("Reason for Visit", ""),
-                        },
-                        {
-                            "label": "Physician",
-                            "value": extracted_info.get("Physician", ""),
-                        },
-                        {
-                            "label": "Test Date",
-                            "value": extracted_info.get("Test Date", ""),
-                        },
-                        {
-                            "label": "Recommendations",
-                            "value": extracted_info.get("Recommendations", ""),
-                        },
-                    ],
-                },
-            ]
-            structured_response["extracted_data"].append(
-                {
-                    "file": filename,
-                    "medical_terms": extracted_info,
-                    "categorized_data": categorized_data,
-                }
-            )
-        return jsonify(structured_response), 200
-
-    @app.route("/")
-    def home():
-        return "Medical Data Extraction API is running!", 200