Update app.py

app.py

@@ -2,31 +2,15 @@ import os
 from pathlib import Path
 import gradio as gr
 import numpy as np
-from sentence_transformers import SentenceTransformer
+import torch
 import faiss
-from openai import OpenAI, OpenAIError
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-# ----------------------------
-# NVIDIA API Key (set via Space Variables & Secrets)
-# ----------------------------
-NV_API_KEY = os.environ.get("NV_API_KEY")
-if not NV_API_KEY:
-    raise RuntimeError(
-        "🔒 NV_API_KEY not set. "
-        "In your Hugging Face Space, go to Settings → Variables & Secrets and create a variable named NV_API_KEY with your NVIDIA key."
-    )
 
-# NVIDIA-compatible OpenAI client
-client = OpenAI(
-    base_url="https://integrate.api.nvidia.com/v1",
-    api_key=NV_API_KEY
-)
-CHAT_MODEL = "meta/llama3-8b-instruct"
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from huggingface_hub import login
 
 # ----------------------------
-# Application configuration
+# Configurações da aplicação
 # ----------------------------
 APP_TITLE = "CVchat – Ronaldo Menezes"
 INTRO = (
@@ -47,80 +31,93 @@ SUGGESTION_QUESTIONS = [
     "Certificações?",
 ]
 
+# Caminhos dos arquivos de índice
 INDEX_FILE  = "r_docs.index"
 CHUNKS_FILE = "r_chunks.npy"
 PDF_PATH    = "CV-Ronaldo_Menezes_2025_06.pdf"
 
-# verify index files exist
+# Verificação dos arquivos
 if not Path(INDEX_FILE).exists() or not Path(CHUNKS_FILE).exists():
-    raise FileNotFoundError(
-        "Index not found. Please run first:\n  python build_index.py"
-    )
+    raise FileNotFoundError("Index not found. Por favor, execute: python build_index.py")
 
-# load FAISS index and chunks
+# Carrega o índice FAISS e os trechos
 index  = faiss.read_index(INDEX_FILE)
 chunks = np.load(CHUNKS_FILE, allow_pickle=True)
 
-# sentence-transformer embedding model
+# Modelo de embeddings
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 
+# ----------------------------
+# Carregamento do modelo LLM local
+# ----------------------------
+hf_token = os.getenv("HF_TOKEN")
+if hf_token is None:
+    raise ValueError("Token Hugging Face não encontrado. Defina como segredo 'HF_TOKEN' nos Settings do Space.")
+
+# Autenticação
+login(token=hf_token)
+
+MODEL_NAME = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=hf_token)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",
+    token=hf_token
+)
+llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# ----------------------------
+# Recuperação de contexto com FAISS
+# ----------------------------
 def retrieve_context(query: str, k: int = 4) -> str:
-    q_emb = embedding_model.encode([query], convert_to_numpy=True)
+    q_emb = embedding_model.encode([query], convert_to_numpy=True, show_progress_bar=False)
     _, I = index.search(q_emb, k)
     return "\n---\n".join(chunks[i] for i in I[0])
 
-# maintain dialog history
 dialog_history: list[tuple[str, str]] = []
 
+# ----------------------------
+# Função do chatbot
+# ----------------------------
 def chatbot(user_input, temperature, top_p, max_tokens):
     global dialog_history
     if not user_input:
         return dialog_history, ""
 
-    # retrieve context
     context = retrieve_context(user_input)
-    system_msg = {
-        "role": "system",
-        "content": (
-            "You are an assistant specialized in R packages. "
-            "Use only the context below to answer. If you don't know, say so.\n\n"
-            f"=== Retrieved Context ===\n{context}\n\n"
-        )
-    }
-
-    # build messages
-    messages = [system_msg]
-    for u, a in dialog_history:
-        messages.append({"role": "user", "content": u})
-        messages.append({"role": "assistant", "content": a})
-    messages.append({"role": "user", "content": user_input})
-
-    assistant_reply = ""
+
+    prompt = (
+        f"### System:\nVocê é um assistente especializado em pacotes R. "
+        f"Use somente o contexto abaixo para responder. Se não souber, diga isso.\n\n"
+        f"=== Retrieved Context ===\n{context}\n\n"
+        f"### User:\n{user_input}\n\n### Assistant:\n"
+    )
+
     try:
-        stream = client.chat.completions.create(
-            model=CHAT_MODEL,
-            messages=messages,
+        result = llm_pipeline(
+            prompt[:model.config.max_position_embeddings],
+            max_new_tokens=max_tokens,
             temperature=temperature,
             top_p=top_p,
-            max_tokens=max_tokens,
-            stream=True
-        )
-        for chunk in stream:
-            delta = chunk.choices[0].delta
-            if hasattr(delta, "content") and delta.content:
-                assistant_reply += delta.content
-    except OpenAIError as e:
-        assistant_reply = f"⚠️ API Error: {e.__class__.__name__}: {e}"
+            do_sample=True
+        )[0]
+        assistant_reply = result.get('generated_text') or result.get('text') or "⚠️ Resposta não gerada."
+        assistant_reply = assistant_reply.split("### Assistant:")[-1].strip()
+    except Exception as e:
+        assistant_reply = f"⚠️ Erro local: {e}"
 
     dialog_history.append((user_input, assistant_reply))
     return dialog_history, ""
 
-def clear_history():
+def clear_all():
     global dialog_history
     dialog_history = []
-    return [], ""
+    return [], "", 0.6, 0.95, 512
 
-# Custom CSS
+# ----------------------------
+# Interface Gradio
+# ----------------------------
 custom_css = r"""
 :root {
   --primary: #4a90e2;
@@ -144,23 +141,25 @@ with gr.Blocks(title=APP_TITLE, css=custom_css, theme=gr.themes.Base()) as demo:
     gr.Markdown(INTRO)
 
     with gr.Row():
-        # main chat column
         with gr.Column(scale=3):
-            chatbot_ui = gr.Chatbot(type="tuples", elem_id="chat-window")
+            chatbot_ui = gr.Chatbot(type="tuples", elem_id="chat-window", render_markdown=True)
             with gr.Row(elem_id="input-area"):
                 txt = gr.Textbox(placeholder="Digite sua pergunta…", lines=2, elem_id="user-input")
                 btn = gr.Button("Enviar", elem_id="send-button")
-            #btn.click(chatbot, [txt, gr.Slider(0,1,0.6), gr.Slider(0,1,0.95), gr.Slider(64,2048,512)], [chatbot_ui, txt])
-            txt.submit(chatbot, [txt, gr.Slider(0,1,0.6), gr.Slider(0,1,0.95), gr.Slider(64,2048,512)], [chatbot_ui, txt])
-            gr.Button("Limpar").click(clear_history, [], [chatbot_ui, txt])
+            temp = gr.Slider(0, 1, 0.6, label="Temperatura")
+            topp = gr.Slider(0, 1, 0.95, label="Top-p")
+            maxtok = gr.Slider(64, 1024, 512, label="Tokens Máximos")
+
+            btn.click(chatbot, [txt, temp, topp, maxtok], [chatbot_ui, txt])
+            txt.submit(chatbot, [txt, temp, topp, maxtok], [chatbot_ui, txt])
+            gr.Button("Limpar").click(clear_all, [], [chatbot_ui, txt, temp, topp, maxtok])
 
-        # sidebar: PDF & suggestions
         with gr.Column(scale=1, elem_classes="sidebar"):
             if Path(PDF_PATH).exists():
                 gr.Markdown(f"[📄 Baixar CV em PDF](/file={PDF_PATH})")
             gr.Markdown("### Sugestões de Perguntas")
             for q in SUGGESTION_QUESTIONS:
-                gr.Button(q).click(lambda suggestion=q: suggestion, outputs=[txt])
+                gr.Button(q).click(lambda q=q: (q, *chatbot(q, 0.6, 0.95, 512)), outputs=[txt, chatbot_ui, txt])
             gr.Markdown("---")
             gr.Markdown("### Dicas de Exploração do PDF")
             gr.Markdown("• Use palavras-chave como 'Process Mining' ou 'GIS' para ir direto à seção relevante.")
@@ -168,4 +167,3 @@ with gr.Blocks(title=APP_TITLE, css=custom_css, theme=gr.themes.Base()) as demo:
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
-