Create app.py

app.py

@@ -0,0 +1,132 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+from llama_index.core import Document
+import pandas as pd
+import getpass
+import os
+from llama_index.core.retrievers import VectorIndexRetriever
+from llama_index.core import VectorStoreIndex
+from llama_index.core import QueryBundle
+from IPython.display import display, HTML
+from llama_index.core.postprocessor import LLMRerank
+import logging
+import sys
+from llama_index.core.memory import ChatMemoryBuffer
+from llama_index.llms.openai import OpenAI
+
+# Load the JSON file
+file_path = 'response_metropol.json'
+data = pd.read_json(file_path)
+data.head()
+documents = [Document(text=row['values'],metadata={"filename": row['file_name'], "description":row['file_description']},) for index, row in data.iterrows()]
+os.environ["OPENAI_API_KEY"] = "sk-proj-YoKkhPHBkCsTLPu155ddT3BlbkFJI434xaZyUK4YHUA7wwHL"
+#pd.set_option("display.max_colwidth", -1)
+# build index
+index = VectorStoreIndex.from_documents(documents)  
+
+def get_retrieved_nodes(
+    query_str, vector_top_k=10, reranker_top_n=5, with_reranker=False
+):
+    query_bundle = QueryBundle(query_str)
+    # configure retriever
+    retriever = VectorIndexRetriever(
+        index=index,
+        similarity_top_k=vector_top_k,
+    )
+    retrieved_nodes = retriever.retrieve(query_bundle)
+
+    if with_reranker:
+        # configure reranker
+        reranker = LLMRerank(
+            choice_batch_size=5,
+            top_n=reranker_top_n,
+        )
+        retrieved_nodes = reranker.postprocess_nodes(
+            retrieved_nodes, query_bundle
+        )
+
+    return retrieved_nodes
+
+
+def pretty_print(df):
+    return display(HTML(df.to_html().replace("\\n", "")))
+
+
+def visualize_retrieved_nodes(nodes) -> None:
+    result_dicts = []
+    for node in nodes:
+        result_dict = {"Score": node.score, "Text": node.node.get_text()}
+        result_dicts.append(result_dict)
+
+    pretty_print(pd.DataFrame(result_dicts))
+    
+new_nodes = get_retrieved_nodes(
+    "quel sont les agence qui sont adaptée aux personnes à mobilité réduite",
+    vector_top_k=10,
+    reranker_top_n=10,
+    with_reranker=True,
+)
+visualize_retrieved_nodes(new_nodes)
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+
+
+def get_all_text(new_nodes):
+    """
+    This function takes a list of nodes and returns a single string containing the text of each node, joined together.
+    
+    Args:
+    new_nodes (list): A list of nodes from which text is to be extracted.
+    
+    Returns:
+    str: A single string containing the text from each node, joined together.
+    """
+    texts = []
+    for node in new_nodes:
+        texts.append(node.get_text())
+    return ' '.join(texts)
+
+
+get_texts = get_all_text(new_nodes)
+print(get_texts)
+
+memory = ChatMemoryBuffer.from_defaults(token_limit=6500)
+
+chat_engine = index.as_chat_engine(
+    llm = OpenAI(temperature=0, model="gpt-4"),
+    chat_mode="context",
+    memory=memory,
+    system_prompt=(
+        "Assist public agents in providing responses to the residents and citizens of the metropolis in nice, guiding them to the appropriate services that best address their requests for assistance. This involves equipping public agents with the necessary tools and information to efficiently and effectively direct citizens to the services that can fulfill their needs.answer using french"
+    ),
+    similarity_top_k=10,
+    node_postprocessors=[
+        LLMRerank(
+            choice_batch_size=5,
+            top_n=5,
+        )
+    ],
+    response_mode="tree_summarize",
+)
+
+def process(input):  
+    response = chat_engine.stream_chat(input)
+    for token in response.response_gen:
+        print(token, end="")
+        return token
+
+iface = gr.chatInterface(
+    fn=process,
+    inputs="text",
+    outputs="text",
+    title="Métropole Signature Expert",
+    description="Provide a question related to signing authorities and get a response.",
+    examples=examples,
+    cache_examples=False,
+    
+)
+
+iface.launch() 
+