Update demo_script.py

demo_script.py

@@ -1,154 +1,154 @@
-import csv
-import os
-import sys
-import base64
-import requests
-import logging
-from pathlib import Path
-
-# ==================== CONFIGURAZIONE ====================
-
-API_KEY = "ChiaveAPI" 
-MODEL_NAME = "gpt-4o"
-IMAGE_DIR = Path("images")
-CSV_PATH = Path("Dataset_Art-0725.csv")
-OUTPUT_CSV = Path("risposte_output.csv")
-
-CATEGORIE = {
-    "AR": "art_recognition",
-    "CR": "chronological_reasoning",
-    "CS": "contextual_summary",
-    "VR": "vision_reading",
-    "VB": "vision_basic",
-    "VL": "vision_logic",
-    "VRS": "vision_reasoning",
-    "IG" : "img_gen"
-}
-
-# ==================== LOGGING ====================
-
-logging.basicConfig(
-    filename='task_log.txt',
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s'
-)
-
-# ==================== FUNZIONI ====================
-
-def encode_image(image_path: Path) -> str:
-    with open(image_path, "rb") as img:
-        return base64.b64encode(img.read()).decode("utf-8")
-
-def validate_image_paths(row):
-    missing = []
-    for col in ['immagine_1_path', 'immagine_2_path']:
-        if row[col].strip():
-            path = IMAGE_DIR / row[col].strip()
-            if not path.exists():
-                missing.append(path)
-    return missing
-
-def invia_task(prompt, image_paths):
-    content = [{"type": "text", "text": prompt}]
-    for img_path in image_paths:
-        if img_path.exists():
-            b64 = encode_image(img_path)
-            content.append({
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{b64}"
-                }
-            })
-
-    payload = {
-        "model": MODEL_NAME,
-        "messages": [
-            {
-                "role": "system",
-                "content": (
-                    "Sei uno storico dell’arte specializzato in analisi iconografiche e storico-stilistiche. "
-                    "Rispondi sempre in italiano, in stile accademico, formale e neutrale. "
-                    "Analizza secondo i criteri storico-artistici e rispondi in modo rigoroso e preciso."
-                )
-            },
-            {"role": "user", "content": content}
-        ],
-        "max_tokens": 1000
-    }
-
-    response = requests.post(
-        "https://api.openai.com/v1/chat/completions",
-        headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
-        json=payload
-    )
-
-    if response.status_code == 200:
-        return response.json()["choices"][0]["message"]["content"]
-    else:
-        raise Exception(f"Errore API: {response.status_code} - {response.text}")
-
-# ==================== MAIN ====================
-
-def main():
-    if len(sys.argv) < 2:
-        print("Uso: python test_01.py AR CR ...")
-        sys.exit(1)
-
-    categorie_input = sys.argv[1:]
-    categorie_mapped = {CATEGORIE[c] for c in categorie_input if c in CATEGORIE}
-
-    if not categorie_mapped:
-        print("Nessuna categoria valida fornita.")
-        sys.exit(1)
-
-    results = []
-
-    with open(CSV_PATH, encoding='utf-8') as f:
-        reader = csv.DictReader(f)
-        for idx, row in enumerate(reader, 1):
-            if row["categoria"] not in categorie_mapped:
-                continue
-
-            image_paths = [IMAGE_DIR / row["immagine_1_path"].strip()]
-            if row["immagine_2_path"].strip():
-                image_paths.append(IMAGE_DIR / row["immagine_2_path"].strip())
-
-            missing = validate_image_paths(row)
-            if missing:
-                msg = f"[TASK {idx}] Immagini mancanti: {[str(m) for m in missing]}"
-                logging.warning(msg)
-                print(msg)
-                continue
-
-            # Costruzione del prompt
-            base_prompt = row["prompt"].strip() + " " + row["instructions"].strip()
-            opzioni = [row.get("opzione_1", ""), row.get("opzione_2", ""), row.get("opzione_3", "")]
-            opzioni = [opt.strip() for opt in opzioni if opt.strip()]
-            if opzioni:
-                base_prompt += "\n\nOpzioni disponibili:\n" + "\n".join(f"- {opt}" for opt in opzioni)
-
-            try:
-                result = invia_task(base_prompt, image_paths)
-                logging.info(f"[TASK {idx}] Completato con successo.")
-                results.append({
-                    "task_id": idx,
-                    "risposta": result,
-                    "prompt_inviato": base_prompt
-                })
-            except Exception as e:
-                logging.error(f"[TASK {idx}] Errore durante l'invio: {e}")
-                print(f"[TASK {idx}] Errore - vedi log")
-
-    # Scrittura risultati su CSV
-    if results:
-        with open(OUTPUT_CSV, mode='w', encoding='utf-8', newline='') as out_csv:
-            writer = csv.DictWriter(out_csv, fieldnames=["task_id", "risposta", "prompt_inviato"])
-            writer.writeheader()
-            for r in results:
-                writer.writerow(r)
-
-    print(f"Esecuzione completata. Risposte salvate in {OUTPUT_CSV}.")
-
-if __name__ == "__main__":
-    main()
-
+import csv
+import os
+import sys
+import base64
+import requests
+import logging
+from pathlib import Path
+
+# ==================== CONFIGURAZIONE ====================
+
+API_KEY = "ChiaveAPI" 
+MODEL_NAME = "gpt-4o"
+IMAGE_DIR = Path("images")
+CSV_PATH = Path("ArtVision-0725.csv")
+OUTPUT_CSV = Path("risposte_output.csv")
+
+CATEGORIE = {
+    "AR": "art_recognition",
+    "CR": "chronological_reasoning",
+    "CS": "contextual_summary",
+    "VR": "vision_reading",
+    "VB": "vision_basic",
+    "VL": "vision_logic",
+    "VRS": "vision_reasoning",
+    "IG" : "img_gen"
+}
+
+# ==================== LOGGING ====================
+
+logging.basicConfig(
+    filename='task_log.txt',
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+# ==================== FUNZIONI ====================
+
+def encode_image(image_path: Path) -> str:
+    with open(image_path, "rb") as img:
+        return base64.b64encode(img.read()).decode("utf-8")
+
+def validate_image_paths(row):
+    missing = []
+    for col in ['immagine_1_path', 'immagine_2_path']:
+        if row[col].strip():
+            path = IMAGE_DIR / row[col].strip()
+            if not path.exists():
+                missing.append(path)
+    return missing
+
+def invia_task(prompt, image_paths):
+    content = [{"type": "text", "text": prompt}]
+    for img_path in image_paths:
+        if img_path.exists():
+            b64 = encode_image(img_path)
+            content.append({
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{b64}"
+                }
+            })
+
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [
+            {
+                "role": "system",
+                "content": (
+                    "Sei uno storico dell’arte specializzato in analisi iconografiche e storico-stilistiche. "
+                    "Rispondi sempre in italiano, in stile accademico, formale e neutrale. "
+                    "Analizza secondo i criteri storico-artistici e rispondi in modo rigoroso e preciso."
+                )
+            },
+            {"role": "user", "content": content}
+        ],
+        "max_tokens": 1000
+    }
+
+    response = requests.post(
+        "https://api.openai.com/v1/chat/completions",
+        headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
+        json=payload
+    )
+
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        raise Exception(f"Errore API: {response.status_code} - {response.text}")
+
+# ==================== MAIN ====================
+
+def main():
+    if len(sys.argv) < 2:
+        print("Uso: python test_01.py AR CR ...")
+        sys.exit(1)
+
+    categorie_input = sys.argv[1:]
+    categorie_mapped = {CATEGORIE[c] for c in categorie_input if c in CATEGORIE}
+
+    if not categorie_mapped:
+        print("Nessuna categoria valida fornita.")
+        sys.exit(1)
+
+    results = []
+
+    with open(CSV_PATH, encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for idx, row in enumerate(reader, 1):
+            if row["categoria"] not in categorie_mapped:
+                continue
+
+            image_paths = [IMAGE_DIR / row["immagine_1_path"].strip()]
+            if row["immagine_2_path"].strip():
+                image_paths.append(IMAGE_DIR / row["immagine_2_path"].strip())
+
+            missing = validate_image_paths(row)
+            if missing:
+                msg = f"[TASK {idx}] Immagini mancanti: {[str(m) for m in missing]}"
+                logging.warning(msg)
+                print(msg)
+                continue
+
+            # Costruzione del prompt
+            base_prompt = row["prompt"].strip() + " " + row["instructions"].strip()
+            opzioni = [row.get("opzione_1", ""), row.get("opzione_2", ""), row.get("opzione_3", "")]
+            opzioni = [opt.strip() for opt in opzioni if opt.strip()]
+            if opzioni:
+                base_prompt += "\n\nOpzioni disponibili:\n" + "\n".join(f"- {opt}" for opt in opzioni)
+
+            try:
+                result = invia_task(base_prompt, image_paths)
+                logging.info(f"[TASK {idx}] Completato con successo.")
+                results.append({
+                    "task_id": idx,
+                    "risposta": result,
+                    "prompt_inviato": base_prompt
+                })
+            except Exception as e:
+                logging.error(f"[TASK {idx}] Errore durante l'invio: {e}")
+                print(f"[TASK {idx}] Errore - vedi log")
+
+    # Scrittura risultati su CSV
+    if results:
+        with open(OUTPUT_CSV, mode='w', encoding='utf-8', newline='') as out_csv:
+            writer = csv.DictWriter(out_csv, fieldnames=["task_id", "risposta", "prompt_inviato"])
+            writer.writeheader()
+            for r in results:
+                writer.writerow(r)
+
+    print(f"Esecuzione completata. Risposte salvate in {OUTPUT_CSV}.")
+
+if __name__ == "__main__":
+    main()
+