Update app.py

README.md

@@ -1,15 +1,17 @@
 ---
-title: Gradio Chatbot
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
+title: Avatar Bot
+emoji: 👀
+colorFrom: green
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 6.3.0
 app_file: app.py
 pinned: false
 hf_oauth: true
 hf_oauth_scopes:
- - inference-api
+- inference-api
+short_description: a bot that answer questions about professional projets
+license: mit
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -1,70 +1,93 @@
+
+
+from utils import _set_env
+
+
+_set_env("OPENAI_API_KEY")
+
+
+from utils import *
+
+def create_graph():
+    from langgraph.graph import StateGraph, START, END
+    from langgraph.prebuilt import ToolNode, tools_condition
+
+
+    ## ADD TRACKING
+    response_model = init_chat_model("gpt-4o", temperature=0)
+    grader_model = init_chat_model("gpt-4o", temperature=0)
+
+
+    workflow = StateGraph(MessagesState)
+
+    # Define the nodes we will cycle between
+    workflow.add_node(generate_query_or_respond)
+    workflow.add_node("retrieve", ToolNode([retriever_tool]))
+    workflow.add_node(rewrite_question)
+    workflow.add_node(generate_answer)
+
+    workflow.add_edge(START, "generate_query_or_respond")
+
+    # Decide whether to retrieve
+    workflow.add_conditional_edges(
+        "generate_query_or_respond",
+        # Assess LLM decision (call `retriever_tool` tool or respond to the user)
+        tools_condition,
+        {
+            # Translate the condition outputs to nodes in our graph
+            "tools": "retrieve",
+            END: END,
+        },
+    )
+
+    # Edges taken after the `action` node is called.
+    workflow.add_conditional_edges(
+        "retrieve",
+        # Assess agent decision
+        grade_documents,
+    )
+    workflow.add_edge("generate_answer", END)
+    workflow.add_edge("rewrite_question", "generate_query_or_respond")
+
+    # Compile
+    graph = workflow.compile()
+
+    return graph
+
+
+
+
+
+graph = create_graph()
+
+
+from IPython.display import Image, display
+display(Image(graph.get_graph().draw_mermaid_png()))
+
+# This is a simple general-purpose chatbot built on top of LangChain and Gradio.
+# Before running this, make sure you have exported your OpenAI API key as an environment variable:
+# export OPENAI_API_KEY="your-openai-api-key"
+
+from langchain_openai import ChatOpenAI  
+from langchain.schema import AIMessage, HumanMessage  
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    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(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    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,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = 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
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    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)",
-        ),
-    ],
-)
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+model = ChatOpenAI(model="gpt-4o-mini")
+
+def predict(message, history):
+    history_langchain_format = []
+    for msg in history:
+        if msg['role'] == "user":
+            history_langchain_format.append(HumanMessage(content=msg['content']))
+        elif msg['role'] == "assistant":
+            history_langchain_format.append(AIMessage(content=msg['content']))
+    history_langchain_format.append(HumanMessage(content=message))
+    gpt_response = model.invoke(history_langchain_format)
+    return gpt_response.content
 
+demo = gr.ChatInterface(
+    predict,
+    api_name="chat",
+)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()