Experiment branch commit

total_app.py

@@ -0,0 +1,118 @@
+# %% API call1
+
+import time
+import json
+import os
+from datetime import datetime
+import pandas as pd
+from openai import OpenAI
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# === CONFIGURATION ===
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL")
+MODEL_NAME = "GPT-OSS-120B"
+INPUT_CSV = "/home/shahin/Lab/Doktorarbeit/Barcelona/Data/MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_unique.csv"
+EDSS_INSTRUCTIONS_PATH = "/home/shahin/Lab/Doktorarbeit/Barcelona/attach/Komplett.txt"
+
+client = OpenAI(
+    api_key=OPENAI_API_KEY,
+    base_url=OPENAI_BASE_URL
+)
+
+with open(EDSS_INSTRUCTIONS_PATH, 'r') as f:
+    EDSS_INSTRUCTIONS = f.read().strip()
+
+# === UPDATED RUN INFERENCE WITH RETRY LOGIC ===
+def run_inference(patient_text, max_retries=3):
+    prompt = f'''Du bist ein medizinischer Assistent... (rest of your prompt)''' # Kept same as your original
+
+    # Instructions omitted for brevity, but keep your full prompt here
+
+    attempts = 0
+    while attempts < max_retries:
+        start_time = time.time()
+        attempts += 1
+
+        try:
+            response = client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "You extract EDSS scores..."},
+                    {"role": "user", "content": prompt}
+                ],
+                model=MODEL_NAME,
+                max_tokens=2048,
+                temperature=0.0,
+                response_format={"type": "json_object"}
+            )
+
+            content = response.choices[0].message.content
+
+            # Check if content is empty or None
+            if not content or content.strip() == "" or content.strip() == "{}":
+                print(f"   [Attempt {attempts}] Warning: Received empty response. Retrying...")
+                time.sleep(1) # Short pause before retrying
+                continue
+
+            # Parse the JSON response
+            parsed = json.loads(content)
+            inference_time = time.time() - start_time
+
+            return {
+                "success": True,
+                "result": parsed,
+                "inference_time_sec": inference_time,
+                "attempts": attempts
+            }
+
+        except Exception as e:
+            print(f"   [Attempt {attempts}] Error: {e}")
+            if attempts < max_retries:
+                time.sleep(2) # Wait longer on actual connection errors
+            else:
+                return {
+                    "success": False,
+                    "error": f"Failed after {max_retries} attempts: {str(e)}",
+                    "inference_time_sec": -1,
+                    "attempts": attempts
+                }
+
+    return {"success": False, "error": "Unknown failure", "attempts": attempts}
+
+# === BUILD PATIENT TEXT ===
+def build_patient_text(row):
+    summary = str(row.get("T_Zusammenfassung", "")) if pd.notna(row.get("T_Zusammenfassung")) else ""
+    diagnoses = str(row.get("Diagnosen", "")) if pd.notna(row.get("Diagnosen")) else ""
+    clinical = str(row.get("T_KlinBef", "")) if pd.notna(row.get("T_KlinBef")) else ""
+    findings = str(row.get("T_Befunde", "")) if pd.notna(row.get("T_Befunde")) else ""
+    return "\n".join([summary, diagnoses, clinical, findings]).strip()
+
+if __name__ == "__main__":
+    df = pd.read_csv(INPUT_CSV, sep=';')
+    results = []
+
+    for idx, row in df.iterrows():
+        print(f"Processing row {idx + 1}/{len(df)}")
+        patient_text = build_patient_text(row)
+
+        # Calling the updated inference function
+        result = run_inference(patient_text, max_retries=3)
+
+        result["unique_id"] = row.get("unique_id", f"row_{idx}")
+        result["MedDatum"] = row.get("MedDatum", None)
+
+        results.append(result)
+        # Optional: only print success to keep console clean
+        if result["success"]:
+            print(f"   Success on attempt {result.get('attempts')}")
+
+    output_json = INPUT_CSV.replace(".csv", "_results_total.json")
+    with open(output_json, 'w', encoding='utf-8') as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    print(f"Results saved to {output_json}")
+
+
+