Update app.py

app.py

@@ -1,13 +1,127 @@
 
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-testo= """6.2_ KNIFE CHANGE     When the software indicates a knife change (see figure 63), follow these steps:   1. Press the emergency button to turn off the motors.   2. Press the cover lift button on the cutting head (figure 63b).   3. Rotate the plate to access the two screws comfortably that secure the knife to the knife holder.   4. Use a 2.5 mm hex key to remove the 2 screws from the knife holder (fig. 64) by turning counterclock‐ wise.  Figure 63—knife change notice    Figure 63b —buttons that raise and lower the cover  Cutting head cover  opening button   6 —  63      NOTICE  The knife must pass through all its guides. Repeat the insertion process into the guide sleeve if  the knife appears to be forced or is not aligned correctly.  5. Detach the knife from the knife holder (fig. 65) by moving the knife downward.   6. Rotate the plate to a position that makes it easy to remove the knife: place the cutting edge towards  the beam.   7. Pull the knife upwards from the guide sleeve, being careful not to cut yourself (fig. 66). The knife is  sharp!  WARNING   The knife is sharp: exercise extreme caution and avoid touching the sharp edge. It is recom‐ mended to use specialized gloves for maintenance operations.   NOTICE  The knife cannot be secured if inserted the wrong way, as the screw holes would not align. Pay  attention to the insertion direction of the knife: the noncutting edge must face towards the  plate support.   8. Insert the new knife from the top to the bottom, ensuring that you align the guides located inside the  guide sleeve and on the plate support.   9. Position the knife so that the holes on its upper part align with the holes on the knife holder.   10. Tighten the two securing screws using the 2.5 mm hex key.   Figure 65—knife removal  Figure 66—knife extraction   6 —  64       11. Lower the cutting head cover by using the dedicated button on the operator panel and release the pre‐ viously pressed emergency button.   12. Start the machine's motors using the appropriate switch on the computer console (see  4.2).   13. Open the VCUT program and navigate to the PARAMETERSKNIFE section. Click on the "NEW KNIFE"  button to restore the values related to the knife size and consumption (figure 67).   NOTICE  Failure to confirm the knife change in the VCUT program can lead to inaccuracies in cutting. The  knife dimensions entered in the VCUT program must always match the actual knife dimensions.   PARAMETERS   button KNIFE button   Figure 67—setting “new knife ” in the VCUT software   6 —  65
- """
-query = "how to change the knife ?"  
-messages=[
-
-    {"role": "system", "content": "Sei un esperto tecnico specializzato in macchine da taglio. Rispondi in italiano alla domanda con chiarezza, fornendo dettagli tecnici accurati e, se possibile, esempi pratici. Usa un tono professionale ma accessibile."},
-    {"role": "user", "content": f"Testo: '{testo}'\n\nDomanda: {query}"},
+
+
+
+client = OpenAI(api_key=api_key)
+# Configura MarianMT per la traduzione multilingua
+model_name = "Helsinki-NLP/opus-mt-it-en"
+translator_tokenizer = MarianTokenizer.from_pretrained(model_name)
+translator_model = MarianMTModel.from_pretrained(model_name)
+
+# Percorsi dei file
+faiss_index_path = "manual_embeddings.index"  # Percorso del file FAISS
+chunk_file_path = "manual_chunks.txt"         # Percorso del file con i chunk
+
+# Carica l'indice FAISS
+index = faiss.read_index(faiss_index_path)
+
+# Carica i chunk dal file
+with open(chunk_file_path, "r", encoding="utf-8") as f:
+    chunks = f.read().split("\n\n")  # Ogni chunk è separato da due newline
+
+# Funzione per tradurre testo
+def translate_text(text, source_lang="it", target_lang="en"):
+    
+    if source_lang == target_lang:
+        return text  # Nessuna traduzione necessaria
+
+    model_name = f"Helsinki-NLP/opus-mt-{source_lang}-{target_lang}"
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    translated = model.generate(**inputs)
+    return tokenizer.decode(translated[0], skip_special_tokens=True)
+
+# Funzione per generare l'embedding della query usando OpenAI
+def get_query_embedding(query, model="text-embedding-ada-002"):
+    response = client.embeddings.create(model=model, input=[query])
+    embedding = response.data[0].embedding
+    return np.array(embedding)  # Restituisce l'embedding come array NumPy
+
+
+# Funzione per cercare nel database FAISS
+def search_in_faiss(query_embedding, k=3):
+    query_embedding = np.expand_dims(query_embedding, axis=0)  # Assicura una matrice 2D
+    distances, indices = index.search(query_embedding, k)
+    return distances, indices
+
+# Funzione per recuperare i chunk associati agli indici di FAISS
+def get_chunks_from_indices(indices):
+    retrieved_chunks = []
+    for i in indices[0]:
+        if 0 <= i < len(chunks):
+            retrieved_chunks.append(chunks[i])
+        else:
+            retrieved_chunks.append(f"[Chunk non trovato per indice {i}]")
+    return retrieved_chunks
+
+# Funzione per rispondere alla domanda usando GPT-3
+def generate_response(query, context, max_tokens=550):
+    try:
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": (
+                    "Sei un tecnico specializzato in macchine tessili da taglio. Rispondi in italiano alla domanda in modo chiaro, "
+                    "usando un tono professionale ma accessibile. Se il testo non contiene informazioni sufficienti per rispondere, dillo in modo esplicito."
+                )},
+                {"role": "user", "content": (
+                    f"Domanda: {query}\n"
+                    f"Testo: {context}\n"
+                    "Rispondi in modo chiaro e operativo per un tecnico che deve svolgere la mansione."
+                )}
+            ],
+            max_tokens=max_tokens,
+            temperature=0.5,
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"Errore durante la generazione della risposta: {e}")
+        return "Errore nell'elaborazione della risposta."
+
+# Funzione principale per ricerca e risposta
+def search_and_answer(query, source_lang="it", target_lang="en"):
+    # Traduci la query in inglese
+    translated_query = translate_text(query, source_lang, target_lang)
+
+    # Ottieni l'embedding della query
+    query_embedding = get_query_embedding(translated_query)
+
+    # Esegui la ricerca in FAISS
+    distances, indices = search_in_faiss(query_embedding)
+
+    # Recupera i chunk associati agli indici
+    retrieved_chunks = get_chunks_from_indices(indices)
+    context = "\n".join(retrieved_chunks)
+
+    # Ottieni la risposta da GPT-3
+    response = generate_response(query, context)
+
+    # Traduci la risposta nella lingua originale
+    #translated_response = translate_text(response, target_lang, source_lang)
+
+    return context, response  #translated_response
+
+# Interfaccia Gradio
+examples = [
+    ["Come cambio la lama della macchina?"],
+    ["Quali sono le precauzioni di sicurezza per usare la macchina?"],
+    ["Chi contattare in caso di problemi?"]
 ]
-pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
-pipe(messages)
+iface = gr.Interface(
+    fn=search_and_answer,
+    inputs=gr.Textbox(lines=2, placeholder="Inserisci la tua domanda qui..."),
+    outputs=[
+        gr.Textbox(label="Risultati della Ricerca"),
+        gr.Textbox(label="Risposta Generata")
+    ],
+    examples=examples,
+    title="Sistema RAG con Traduzione Multilingua",
+    description=(
+        "Inserisci una domanda in qualsiasi lingua supportata. Il sistema tradurrà la query, "
+        "eseguirà la ricerca e genererà una risposta nella lingua originale."
+    )
+)
+
+# Avvia l'app
+iface.launch()