init!

app.py

@@ -1,64 +1,117 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+import faiss
+import numpy as np
+from rank_bm25 import BM25Okapi
+from transformers import AutoTokenizer, AutoModel
+from litellm import completion
+import os
+import torch
+from sentence_transformers import CrossEncoder
 
+# --- 1. Завантаження документів ---
+def load_documents(file_paths):
+    documents = []
+    for path in file_paths:
+        with open(path, 'r', encoding='utf-8') as file:
+            documents.append(file.read().strip())
+    return documents
 
+# --- 2. Індексування документів ---
+class DocumentIndexer:
+    def __init__(self, documents):
+        self.documents = documents
+        self.bm25 = BM25Okapi([doc.split() for doc in documents])
+        self.tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+        self.model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+        self.index = self.create_faiss_index()
+
+    def create_faiss_index(self):
+        embeddings = self.embed_documents(self.documents)
+        dimension = embeddings.shape[1]
+        index = faiss.IndexFlatL2(dimension)
+        index.add(embeddings)
+        return index
+
+    def embed_documents(self, docs):
+        tokens = self.tokenizer(docs, padding=True, truncation=True, return_tensors="pt")
+        with torch.no_grad():
+            embeddings = self.model(**tokens).last_hidden_state.mean(dim=1).numpy()
+        return embeddings
+
+    def search_bm25(self, query, top_k=5):
+        query_terms = query.split()
+        scores = self.bm25.get_scores(query_terms)
+        top_indices = np.argsort(scores)[::-1][:top_k]
+        return [self.documents[i] for i in top_indices]
+
+    def search_semantic(self, query, top_k=5):
+        query_embedding = self.embed_documents([query])
+        distances, indices = self.index.search(query_embedding, top_k)
+        return [self.documents[i] for i in indices[0]]
+
+# --- 3. Ререйкер ---
+class Reranker:
+    def __init__(self, model_name="cross-encoder/ms-marco-TinyBERT-L-6"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = CrossEncoder(model_name)
+
+    def rank(self, query, documents):
+        pairs = [(query, doc) for doc in documents]
+        scores = self.model.predict(pairs)
+        ranked_docs = [documents[i] for i in np.argsort(scores)[::-1]]
+        return ranked_docs
+
+# --- 4. Генерація відповіді ---
+class QAChatbot:
+    def __init__(self, indexer, reranker):
+        self.indexer = indexer
+        self.reranker = reranker
+
+    def generate_answer(self, query):
+        # 1. Шукаємо релевантні документи
+        bm25_results = self.indexer.search_bm25(query)
+        semantic_results = self.indexer.search_semantic(query)
+        combined_results = list(set(bm25_results + semantic_results))
+
+        # 2. Ранжуємо документи
+        ranked_docs = self.reranker.rank(query, combined_results)
+
+        # 3. Генеруємо відповідь
+        context = "\n".join(ranked_docs[:3])  # Використовуємо топ-3 документи
+        response = completion(
+            model="groq/llama3-8b-8192",
+            messages=[
+                {
+                    "role": "system",
+                    "content": PROMPT
+                },
+                {
+                    "role": "user",
+                    "content": f"Context: {context}\n\nQuestion: {query}\nAnswer:",
+                }
+            ],
+        )
+        return response
+
+# --- 5. Створення Gradio інтерфейсу ---
+def chatbot_interface(query):
+    file_paths = ["company.txt", "Base.txt"]  # Вкажіть ваші файли
+    documents = load_documents(file_paths)
+    
+    # Налаштовуємо індексер та ререйкер
+    indexer = DocumentIndexer(documents)
+    reranker = Reranker()
+
+    # Запускаємо чат-бота
+    chatbot = QAChatbot(indexer, reranker)
+    answer = chatbot.generate_answer(query)
+    return answer["choices"][0]["message"]["content"]
+
+# Створення інтерфейсу Gradio
+iface = gr.Interface(fn=chatbot_interface, inputs="text", outputs="text", 
+                     live=True, title="Чат-бот для ритейл-компанії", 
+                     description="Запитуйте мене про товари і я допоможу вам вибрати найкраще!")
+
+# Запуск інтерфейсу
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()