Upload app.py

app.py

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+import streamlit as st
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+import torch
+import uuid
+import os
+import langid
+
+st.title("RAG PDF Q&A με DeepSeek-7B και Paraphrasing (μόνο Αγγλικά)")
+
+st.sidebar.header("Ανέβασε PDF")
+pdf = st.sidebar.file_uploader("Επίλεξε PDF", type="pdf")
+
+if pdf:
+    temp_filename = f"temp_{uuid.uuid4()}.pdf"
+    with open(temp_filename, "wb") as f:
+        f.write(pdf.read())
+
+    loader = PyPDFLoader(temp_filename)
+    pages = loader.load()
+
+    st.sidebar.success(f"Το έγγραφο έχει {len(pages)} σελίδες.")
+
+    # ---- Φόρτωση DeepSeek ----
+    @st.cache_resource
+    def load_llm():
+        MODEL_ID = "deepseek-ai/deepseek-llm-7b-chat"
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            MODEL_ID,
+            trust_remote_code=True
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            quantization_config=bnb_config,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        return tokenizer, model
+
+    tokenizer, model = load_llm()
+
+    # ---- Paraphrasing για Αγγλικά ----
+    @st.cache_resource
+    def load_en_paraphraser():
+        return pipeline("text2text-generation", model="ramsrigouthamg/t5_paraphraser")
+
+    paraphraser_en = load_en_paraphraser()
+
+    translation_pipeline = pipeline("translation", model="Helsinki-NLP/opus-mt-en-el")
+
+    total_words = sum(len(page.page_content.split()) for page in pages)
+    avg_words_per_page = total_words / len(pages)
+
+    st.sidebar.info(f"Μέσος όρος λέξεων ανά σελίδα: {int(avg_words_per_page)}")
+
+    proposed_chunk_size = 1000
+    proposed_overlap = 300
+
+    user_chunk_size = st.sidebar.number_input("Επίλεξε Chunk size", value=proposed_chunk_size, step=50)
+    user_overlap = st.sidebar.number_input("Επίλεξε Overlap", value=proposed_overlap, step=50)
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        separators=["\n\n", "\n", ". ", "! ", "; ", "? ", "  ", " "],
+        chunk_size=user_chunk_size,
+        chunk_overlap=user_overlap
+    )
+
+    docs = text_splitter.split_documents(pages)
+
+    # ---- Προσθήκη custom ids ----
+    for idx, doc in enumerate(docs):
+        doc.metadata["custom_id"] = idx
+
+    st.success(f"Επεξεργάστηκαν {len(docs)} chunks.")
+
+    embedding_function = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )
+
+    # ---- Ανέβηκε νέο PDF; Καθάρισε vectordb ----
+    if st.session_state.get("loaded_filename") != temp_filename:
+        st.session_state.vectordb = None
+        st.session_state.retriever = None
+        st.session_state.docs = None
+        st.session_state.loaded_filename = temp_filename
+
+    if not st.session_state.get("vectordb"):
+        vectordb = Chroma(
+            collection_name=f"rag_pdf_collection_{uuid.uuid4()}",
+            embedding_function=embedding_function
+        )
+        vectordb.add_documents(docs)
+        retriever = vectordb.as_retriever(
+            search_kwargs={"k": 6}
+        )
+        st.session_state.vectordb = vectordb
+        st.session_state.retriever = retriever
+        st.session_state.docs = docs
+    else:
+        retriever = st.session_state.retriever
+        docs = st.session_state.docs
+
+    st.sidebar.success("Έτοιμο το retriever.")
+
+    # ---- Rephrasing ----
+    def rephrase_question(original_question, lang):
+        variations = [original_question]
+        paraphrase_prompt = f"paraphrase: {original_question} </s>"
+
+        try:
+            if lang == "el":
+                # Δεν υπάρχει διαθέσιμο paraphrasing για ελληνικά προς το παρόν
+                rephrases = []
+            else:
+                output = paraphraser_en(paraphrase_prompt, max_length=64, num_return_sequences=3, do_sample=True)
+                rephrases = list({o['generated_text'].strip() for o in output})
+            variations.extend(rephrases)
+        except Exception as e:
+            st.sidebar.warning("Το paraphrasing απέτυχε. Χρησιμοποιούμε μόνο την αρχική ερώτηση.")
+
+        return variations
+
+    def generate_answer(question):
+        detected_lang = langid.classify(question)[0]
+        if detected_lang == "el":
+            lang_instruction = "Απάντησε στα Ελληνικά."
+            fallback_response = "Δεν γνωρίζω."
+        else:
+            lang_instruction = "Answer in English."
+            fallback_response = "I do not know."
+
+        variations = rephrase_question(question, detected_lang)
+
+        st.sidebar.info(f"Εναλλακτικές ερωτήσεις: {variations}")
+
+        all_docs = []
+        for var in variations:
+            docs_found = retriever.get_relevant_documents(var)
+            all_docs.extend(docs_found)
+
+        unique_docs = list({doc.page_content: doc for doc in all_docs}.values())
+        context = "\n\n".join([doc.page_content for doc in unique_docs])
+
+        chunk_ids = []
+        for doc in unique_docs:
+            if "custom_id" in doc.metadata:
+                chunk_ids.append(doc.metadata["custom_id"])
+
+        prompt = f"""
+{lang_instruction}
+Χρησιμοποίησε ΜΟΝΟ τα συμφραζόμενα.
+Αν δεν υπάρχει απάντηση στα συμφραζόμενα, πες ρητά: {fallback_response}
+Συμφραζόμενα:
+{context}
+Ερώτηση: {question}
+Απάντηση:"""
+
+        inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+        output = model.generate(
+            **inputs,
+            max_new_tokens=250,
+            temperature=0.2,
+            repetition_penalty=1.2,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+        full_answer = tokenizer.decode(output[0], skip_special_tokens=True)
+
+        if "Απάντηση:" in full_answer:
+            clean_answer = full_answer.split("Απάντηση:")[-1].strip()
+        else:
+            clean_answer = full_answer.strip()
+
+        if clean_answer == "" or any(
+            bad in clean_answer.lower()
+            for bad in ["απάντησε", "συμφραζόμενα", question.lower()]
+        ):
+            clean_answer = fallback_response
+
+        if detected_lang == "el" and clean_answer != fallback_response:
+            if sum(c.isalpha() and c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' for c in clean_answer) > len(clean_answer) * 0.4:
+                translation = translation_pipeline(clean_answer)[0]['translation_text']
+                clean_answer = translation
+
+        return clean_answer, chunk_ids
+
+    question = st.text_input("Γράψε την ερώτησή σου:")
+    if question:
+        with st.spinner("Ανάκτηση και παραγωγή απάντησης..."):
+            answer, chunk_ids = generate_answer(question)
+            st.markdown("**Απάντηση:**")
+            st.success(answer)
+            st.info(f"Χρησιμοποιήθηκαν τα chunks με ID: {chunk_ids}")
+
+    os.remove(temp_filename)
+
+else:
+    st.info("Περιμένω να ανεβάσεις ένα PDF.")