Upload 3 files

app.py

@@ -6,7 +6,7 @@ import pickle
 from langchain_huggingface import HuggingFaceEmbeddings
 
 # ====================== CONFIG ======================
-repo_id = "robertolofaro/books-model"
+repo_id = "robertolofaro/articles-model"
 
 BACKENDS = {
     "Fast Mode (No RAG)": None,
@@ -20,7 +20,7 @@ FAISS_PATH = "faiss_index_hnsw"
 QDRANT_PATH = "qdrant_db"
 QDRANT_COLLECTION = "articles"
 
-# ====================== LOAD METADATA FOR BOOK LIST ======================
+# ====================== LOAD METADATA FOR ARTICLE LIST ======================
 def load_articles_list():
     try:
         with open("metadata.pkl", "rb") as f:
@@ -30,7 +30,7 @@ def load_articles_list():
     except:
         return ["All categories"]
 
-ARTICLE_LIST = load_article_list()
+ARTICLE_LIST = load_articles_list()
 
 # ====================== LOAD LLM ======================
 model_path = hf_hub_download(
@@ -89,7 +89,7 @@ When a user asks a question, your goal is to provide a structured response based
 
 
 # ====================== GENERATION FUNCTION ======================
-def generate_response(message, history, rag_mode, book_filter, max_tokens, temperature, top_p, repeat_penalty):
+def generate_response(message, history, rag_mode, article_filter, max_tokens, temperature, top_p, repeat_penalty):
     full_prompt = f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
 
     for msg in history[-4:]:
@@ -144,8 +144,8 @@ with gr.Blocks(title="Article Q&A model") as demo:
             value="Fast Mode (No RAG)",
             label="Mode"
         )
-        book_filter = gr.Dropdown(
-            choices=BOOK_LIST,
+        article_filter = gr.Dropdown(
+            choices=ARTICLE_LIST,
             value="All categories",
             label="Focus on category"
         )
@@ -158,7 +158,7 @@ with gr.Blocks(title="Article Q&A model") as demo:
 
     gr.ChatInterface(
         fn=generate_response,
-        additional_inputs=[rag_mode, book_filter, max_tokens, temperature, top_p, repeat_penalty],
+        additional_inputs=[rag_mode, article_filter, max_tokens, temperature, top_p, repeat_penalty],
         examples=[
             ["What is the potential for Italy?"],
             ["What is the potential for Turin?"]

packages.txt

@@ -3,4 +3,3 @@ cmake
 pkg-config
 libopenblas-dev
 libopenblas0-pthread
-langchain_huggingface

requirements.txt

@@ -2,3 +2,7 @@ gradio
 huggingface_hub
 llama-cpp-python @ https://huggingface.co/robertolofaro/libraries_prebuilt/resolve/main/llama_cpp_python-0.3.23-py3-none-linux_x86_64.whl
 langchain_huggingface
+langchain-community
+chromadb
+faiss-cpu
+qdrant-client