Create app.py

app.py

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+import gradio as gr
+import spaces
+import subprocess
+import os
+import shutil
+import string
+import random
+import glob
+from pypdf import PdfReader
+from sentence_transformers import SentenceTransformer
+
+# Configurações do modelo
+MODEL_NAME = os.environ.get("MODEL", "Snowflake/snowflake-arctic-embed-m")
+CHUNK_SIZE = int(os.environ.get("CHUNK_SIZE", 128))
+DEFAULT_MAX_CHARACTERS = int(os.environ.get("DEFAULT_MAX_CHARACTERS", 258))
+
+# Carregue o modelo de linguagem
+model = SentenceTransformer(MODEL_NAME)
+
+# Função para incorporar consultas e documentos
+@spaces.GPU
+def embed(queries, chunks):
+    query_embeddings = model.encode(queries, prompt_name="query")
+    document_embeddings = model.encode(chunks)
+
+    scores = query_embeddings @ document_embeddings.T
+    results = {}
+    for query, query_scores in zip(queries, scores):
+        chunk_idxs = [i for i in range(len(chunks))]
+        results[query] = list(zip(chunk_idxs, query_scores))
+
+    return results
+
+# Função para extrair texto de arquivos PDF
+def extract_text_from_pdf(reader):
+    full_text = ""
+    for idx, page in enumerate(reader.pages):
+        text = page.extract_text()
+        if len(text) > 0:
+            full_text += f"---- Página {idx} ----\n" + page.extract_text() + "\n\n"
+
+    return full_text.strip()
+
+# Função para converter arquivos em texto
+def convert(filename):
+    plain_text_filetypes = [
+        ".txt",
+        ".csv",
+        ".tsv",
+        ".md",
+        ".yaml",
+        ".toml",
+        ".json",
+        ".json5",
+        ".jsonc",
+    ]
+
+    if any(filename.endswith(ft) for ft in plain_text_filetypes):
+        with open(filename, "r") as f:
+            return f.read()
+
+    if filename.endswith(".pdf"):
+        return extract_text_from_pdf(PdfReader(filename))
+
+    raise ValueError(f"Tipo de arquivo não suportado: {filename}")
+
+# Função para dividir texto em pedaços
+def chunk_to_length(text, max_length=512):
+    chunks = []
+    while len(text) > max_length:
+        chunks.append(text[:max_length])
+        text = text[max_length:]
+    chunks.append(text)
+    return chunks
+
+# Função para prever pedaços relevantes
+@spaces.GPU
+def predict(query, max_characters):
+    query_embedding = model.encode(query, prompt_name="query")
+
+    all_chunks = []
+    for filename, doc in docs.items():
+        similarities = doc["embeddings"] @ query_embedding.T
+        all_chunks.extend([(filename, chunk, sim) for chunk, sim in zip(doc["chunks"], similarities)])
+
+    all_chunks.sort(key=lambda x: x[2], reverse=True)
+
+    relevant_chunks = {}
+    total_chars = 0
+    for filename, chunk, _ in all_chunks:
+        if total_chars + len(chunk) <= max_characters:
+            if filename not in relevant_chunks:
+                relevant_chunks[filename] = []
+            relevant_chunks[filename].append(chunk)
+            total_chars += len(chunk)
+        else:
+            break
+
+    return {"relevant_chunks": relevant_chunks}
+
+# Carregue os documentos
+docs = {}
+for filename in glob.glob("src/*"):
+    if filename.endswith("add_your_files_here"):
+        continue
+
+    converted_doc = convert(filename)
+    chunks = chunk_to_length(converted_doc, CHUNK_SIZE)
+    embeddings = model.encode(chunks)
+
+    docs[filename] = {
+        "chunks": chunks,
+        "embeddings": embeddings,
+    }
+
+# Crie a interface da ferramenta
+gr.Interface(
+    predict,
+    inputs=[
+        gr.Textbox(label="Consulta feita sobre os documentos"),
+        gr.Number(label="Máximo de caracteres de saída", value=DEFAULT_MAX_CHARACTERS),
+    ],
+    outputs=[gr.Dict(label="Pedaços relevantes")],
+    title="Demonstração do modelo de ferramenta da comunidade ",
+    description='''"Para usar o no HuggingChat com seus próprios documentos
+                , comece clonando este espaço, adicione seus documentos à pasta `src` e então crie uma ferramenta comunitária com este espaço!"
+                ,'''
+).launch()