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aggiunta possibilità di usare anche openai

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This commit is contained in:
Daniele Viti
2025-12-03 18:54:22 +01:00
parent 53f1b25a68
commit 4fdb0540ca
3 changed files with 67 additions and 287 deletions
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1
.env.example
View File

@@ -1,2 +1,3 @@
LLM_PROVIDER=openai
OPENAI_API_KEY=xxxxxx
GOOGLE_API_KEY=xxxxxx

97
process_files_gemini.py → process_files.py
View File

@@ -8,6 +8,7 @@ from docx.oxml.table import CT_Tbl
from docx.table import _Cell, Table
from docx.text.paragraph import Paragraph
import google.generativeai as genai
import openai
from dotenv import load_dotenv
import time
@@ -140,7 +141,9 @@ def parse_markdown_table(text):
def step1_process_pages():
"""Step 1: Processa pagine -> Markdown -> CSV (Algoritmico)"""
print("\n--- INIZIO STEP 1: Word -> Markdown -> CSV (Gemini) ---")
llm_provider = os.getenv("LLM_PROVIDER", "gemini").lower()
print(f"\n--- INIZIO STEP 1: Word -> Markdown -> CSV ({llm_provider.upper()}) ---")
word_files = glob.glob(os.path.join(INPUT_DIR, "*.docx"))
if not word_files:
@@ -163,29 +166,47 @@ def step1_process_pages():
- NON inventare ID, se li trovi usali altrimenti lascia vuoto.
"""
# Configurazione Modello
# Utilizziamo gemini-flash-latest come richiesto (o la versione più vicina disponibile)
generation_config = {
"temperature": 0.1,
}
# Configurazione Safety Settings
safety_settings = {
genai.types.HarmCategory.HARM_CATEGORY_HARASSMENT: genai.types.HarmBlockThreshold.BLOCK_NONE,
genai.types.HarmCategory.HARM_CATEGORY_HATE_SPEECH: genai.types.HarmBlockThreshold.BLOCK_NONE,
genai.types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: genai.types.HarmBlockThreshold.BLOCK_NONE,
genai.types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: genai.types.HarmBlockThreshold.BLOCK_NONE,
}
model = None
openai_client = None
try:
model = genai.GenerativeModel(
model_name="gemini-flash-latest",
system_instruction=system_prompt,
generation_config=generation_config,
safety_settings=safety_settings
)
except Exception as e:
print(f"Errore inizializzazione modello Gemini: {e}")
if llm_provider == "gemini":
# Configurazione Modello Gemini
# Utilizziamo gemini-flash-latest come richiesto (o la versione più vicina disponibile)
generation_config = {
"temperature": 0.1,
}
# Configurazione Safety Settings
safety_settings = {
genai.types.HarmCategory.HARM_CATEGORY_HARASSMENT: genai.types.HarmBlockThreshold.BLOCK_NONE,
genai.types.HarmCategory.HARM_CATEGORY_HATE_SPEECH: genai.types.HarmBlockThreshold.BLOCK_NONE,
genai.types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: genai.types.HarmBlockThreshold.BLOCK_NONE,
genai.types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: genai.types.HarmBlockThreshold.BLOCK_NONE,
}
try:
model = genai.GenerativeModel(
model_name="gemini-flash-latest",
system_instruction=system_prompt,
generation_config=generation_config,
safety_settings=safety_settings
)
except Exception as e:
print(f"Errore inizializzazione modello Gemini: {e}")
return
elif llm_provider == "openai":
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
print("Errore: OPENAI_API_KEY non trovata nel file .env")
return
try:
openai_client = openai.OpenAI(api_key=api_key)
except Exception as e:
print(f"Errore inizializzazione client OpenAI: {e}")
return
else:
print(f"Provider {llm_provider} non supportato. Usa 'gemini' o 'openai'.")
return
for file_path in word_files:
@@ -208,14 +229,28 @@ def step1_process_pages():
user_prompt = f"Frammento {chunk_count}:\n\n{chunk}"
try:
# Chiamata a Gemini
response = model.generate_content(user_prompt)
try:
content = response.text.strip()
except ValueError:
print(f" -> Warning: response.text non disponibile. Finish reason: {response.candidates[0].finish_reason if response.candidates else 'N/A'}")
content = ""
content = ""
if llm_provider == "gemini":
# Chiamata a Gemini
response = model.generate_content(user_prompt)
try:
content = response.text.strip()
except ValueError:
print(f" -> Warning: response.text non disponibile. Finish reason: {response.candidates[0].finish_reason if response.candidates else 'N/A'}")
content = ""
elif llm_provider == "openai":
# Chiamata a OpenAI
response = openai_client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.1
)
content = response.choices[0].message.content.strip()
# Pulisci markdown fences
if content.startswith("```markdown"):

256
process_files_openai.py
View File

@@ -1,256 +0,0 @@
import os
import json
import glob
import pandas as pd
from docx import Document
from docx.document import Document as _Document
from docx.oxml.text.paragraph import CT_P
from docx.oxml.table import CT_Tbl
from docx.table import _Cell, Table
from docx.text.paragraph import Paragraph
from openai import OpenAI
from dotenv import load_dotenv
# Carica le variabili d'ambiente
load_dotenv()
# Configurazione directory
INPUT_DIR = './input'
OUTPUT_DIR = './output'
TEMPLATE_FILE = './template/use_case_template.csv'
# Assicurati che le directory esistano
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Inizializza client OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
def iter_block_items(parent):
"""
Yield each paragraph and table child within *parent*, in document order.
Each returned value is an instance of either Table or Paragraph.
"""
if isinstance(parent, _Document):
parent_elm = parent.element.body
elif isinstance(parent, _Cell):
parent_elm = parent._tc
else:
raise ValueError("something's not right")
for child in parent_elm.iterchildren():
if isinstance(child, CT_P):
yield Paragraph(child, parent)
elif isinstance(child, CT_Tbl):
yield Table(child, parent)
def read_docx_chunks(file_path, chunk_size=4000):
"""
Legge il file .docx e restituisce un generatore di chunk di testo.
Mantiene l'ordine di paragrafi e tabelle.
chunk_size: numero approssimativo di caratteri per chunk.
"""
try:
doc = Document(file_path)
current_chunk = []
current_length = 0
for block in iter_block_items(doc):
text = ""
if isinstance(block, Paragraph):
text = block.text.strip()
if text:
text += "\n"
elif isinstance(block, Table):
# Converti tabella in testo (markdown-like o pipe separated)
for row in block.rows:
row_data = [cell.text.strip() for cell in row.cells]
text += "| " + " | ".join(row_data) + " |\n"
text += "\n"
if text:
current_chunk.append(text)
current_length += len(text)
# Se superiamo la dimensione del chunk, yieldiamo
if current_length >= chunk_size:
yield "".join(current_chunk)
current_chunk = []
current_length = 0
# Yield dell'ultimo chunk se presente
if current_chunk:
yield "".join(current_chunk)
except Exception as e:
print(f"Errore lettura {file_path}: {e}")
yield None
def parse_markdown_table(text):
"""
Analizza un testo contenente una tabella Markdown e restituisce un DataFrame pandas.
"""
lines = text.split('\n')
table_lines = [line.strip() for line in lines if line.strip().startswith('|')]
if len(table_lines) < 2:
return None
# Trova la riga separatrice (es. |---|---|)
separator_index = -1
for i, line in enumerate(table_lines):
# Rimuovi pipe e spazi per controllare se contiene solo trattini/due punti
content = line.replace('|', '').replace(':', '').replace('-', '').strip()
if not content: # Se vuota dopo aver rimosso i caratteri separatori, è una riga separatrice
separator_index = i
break
if separator_index <= 0:
return None
def split_row(row_str):
# Split semplice per pipe
parts = row_str.split('|')
# Rimuovi primo e ultimo elemento se vuoti (dovuti ai pipe esterni)
if row_str.startswith('|'): parts.pop(0)
if row_str.endswith('|'): parts.pop(-1)
return [p.strip() for p in parts]
headers = split_row(table_lines[separator_index - 1])
data_rows = table_lines[separator_index + 1:]
data = []
for row in data_rows:
cols = split_row(row)
# Allinea colonne
if len(cols) == len(headers):
data.append(cols)
elif len(cols) < len(headers):
data.append(cols + [''] * (len(headers) - len(cols)))
else:
data.append(cols[:len(headers)])
if not data:
return None
return pd.DataFrame(data, columns=headers)
def step1_process_pages():
"""Step 1: Processa pagine -> Markdown -> CSV (Algoritmico)"""
print("\n--- INIZIO STEP 1: Word -> Markdown -> CSV ---")
word_files = glob.glob(os.path.join(INPUT_DIR, "*.docx"))
if not word_files:
print("Nessun file .docx trovato in input.")
return
# Colonne attese per validazione (opzionale, ma utile per il prompt)
expected_columns = ["ID", "Descrizione", "Risultato Atteso", "Risultato Ottenuto", "Stato"]
for file_path in word_files:
filename = os.path.basename(file_path)
file_base_name = os.path.splitext(filename)[0]
print(f"Elaborazione: {filename}...")
# Directory output
output_subdir = os.path.join(OUTPUT_DIR, file_base_name)
os.makedirs(output_subdir, exist_ok=True)
chunk_count = 0
for chunk in read_docx_chunks(file_path):
if not chunk:
continue
chunk_count += 1
print(f" -> Elaborazione Pagina/Chunk {chunk_count}...")
system_prompt = f"""
Sei un esperto QA. Analizza il frammento di documento fornito.
Estrai TUTTI i dati relativi a Use Case, Test Case, Scenari e Verifiche senza rielaborare il testo, il testo di Use Case, Test Case, Scenari e Verifiche deve rimanere IDENTICO a quello letto.
Output richiesto:
- Genera una TABELLA MARKDOWN valida.
- La tabella DEVE avere ESATTAMENTE queste colonne: {', '.join(expected_columns)}.
- Se un dato non è presente, lascia la cella vuota.
- Se non trovi NESSUN dato rilevante, rispondi ESATTAMENTE con "NO_DATA".
- NON aggiungere altro testo prima o dopo la tabella.
"""
user_prompt = f"Frammento {chunk_count}:\n\n{chunk}"
try:
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.1
)
content = response.choices[0].message.content.strip()
# Pulisci markdown fences
if content.startswith("```markdown"):
content = content.replace("```markdown", "").replace("```", "")
elif content.startswith("```"):
content = content.replace("```", "")
content = content.strip()
if content and "NO_DATA" not in content:
# 1. Salva Markdown
md_path = os.path.join(output_subdir, f"chunk_{chunk_count}.md")
with open(md_path, 'w', encoding='utf-8') as f:
f.write(content)
# 2. Converti in CSV algoritmicamente
df = parse_markdown_table(content)
if df is not None and not df.empty:
csv_path = os.path.join(output_subdir, f"chunk_{chunk_count}.csv")
df.to_csv(csv_path, index=False, encoding='utf-8')
# print(f" -> Generato CSV: {csv_path}")
else:
print(f" -> Warning: Impossibile parsare tabella in chunk {chunk_count}")
else:
pass
except Exception as e:
print(f"Errore chunk {chunk_count} di {filename}: {e}")
def step2_aggregate_csvs():
"""Step 2: Aggrega i frammenti CSV in un unico file finale per documento."""
print("\n--- INIZIO STEP 2: Aggregazione CSV ---")
subdirs = [d for d in glob.glob(os.path.join(OUTPUT_DIR, "*")) if os.path.isdir(d)]
if not subdirs:
print("Nessuna cartella di frammenti trovata in output.")
return
for fragments_dir in subdirs:
doc_name = os.path.basename(fragments_dir)
print(f"Aggregazione per: {doc_name}...")
all_files = glob.glob(os.path.join(fragments_dir, "*.csv"))
try:
all_files.sort(key=lambda x: int(os.path.basename(x).split('_')[1].split('.')[0]))
except:
all_files.sort()
if not all_files:
print(f" -> Nessun frammento CSV trovato in {fragments_dir}")
continue
combined_df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)
output_csv_path = os.path.join(OUTPUT_DIR, f"{doc_name}.csv")
combined_df.to_csv(output_csv_path, index=False, encoding='utf-8')
print(f" -> Creato CSV finale: {output_csv_path}")
def main():
step1_process_pages()
step2_aggregate_csvs()
print("\nProcesso completato.")
if __name__ == "__main__":
main()