Update app.py

app.py

@@ -1,5 +1,126 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from typing import TypedDict, Dict
+from langgraph.graph import StateGraph, END
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables.graph import MermaidDrawMethod
+from IPython.display import display, Image
+
+class State(TypedDict):
+    query: str
+    category: str
+    sentiment: str
+    response: str
+
+from langchain_groq import ChatGroq
+
+llm = ChatGroq(
+    temperature=0,
+    groq_api_key="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",
+    model_name="llama-3.3-70b-versatile"
+)
+
+
+def categorize(state: State) -> State:
+    """Categorize the query."""
+    prompt = ChatPromptTemplate.from_template(
+        "Categorize the following customer query into one of these categories: "
+        "Technical, Billing, General. Query: {query}"
+    )
+    chain = prompt | llm
+    category = chain.invoke({"query": state["query"]}).content
+    return {"category": category}
+
+def analyze_sentiment(state: State) -> State:
+    """Analyze sentiment of the query."""
+    prompt = ChatPromptTemplate.from_template(
+        "Analyze the sentiment of the following customer query "
+        "Response with either 'Positive', 'Neutral', or 'Negative'. Query: {query}"
+    )
+    chain = prompt | llm
+    sentiment = chain.invoke({"query": state["query"]}).content
+    return {"sentiment": sentiment}
+
+def handle_technical(state: State) -> State:
+    """Handle technical queries."""
+    prompt = ChatPromptTemplate.from_template(
+        "Provide a technical support response to the following query: {query}"
+    )
+    chain = prompt | llm
+    response = chain.invoke({"query": state["query"]}).content
+    return {"response": response}
+
+def handle_billing(state: State) -> State:
+    """Handle billing queries."""
+    prompt = ChatPromptTemplate.from_template(
+        "Provide a billing support response to the following query: {query}"
+    )
+    chain = prompt | llm
+    response = chain.invoke({"query": state["query"]}).content
+    return {"response": response}
+
+def handle_general(state: State) -> State:
+    """Handle general queries."""
+    prompt = ChatPromptTemplate.from_template(
+        "Provide a general support response to the following query: {query}"
+    )
+    chain = prompt | llm
+    response = chain.invoke({"query": state["query"]}).content
+    return {"response": response}
+
+def escalate(state: State) -> State:
+    """Escalate negative sentiment queries."""
+    return {"response": "This query has been escalated to a human agent due to its negative sentiment."}
+
+def route_query(state: State) -> State:
+    """Route query based on category and sentiment."""
+    if state["sentiment"] == "Negative":
+        return "escalate"
+    elif state["category"] == "Technical":
+        return "handle_technical"
+    elif state["category"] == "Billing":
+        return "handle_billing"
+    else:
+        return "handle_general"
+
+workflow = StateGraph(State)
+
+workflow.add_node("categorize", categorize)
+workflow.add_node("analyze_sentiment", analyze_sentiment)
+workflow.add_node("handle_technical", handle_technical)
+workflow.add_node("handle_billing", handle_billing)
+workflow.add_node("handle_general", handle_general)
+workflow.add_node("escalate", escalate)
+
+workflow.add_edge("categorize", "analyze_sentiment")
+workflow.add_conditional_edges(
+    "analyze_sentiment",
+    route_query, {
+        "handle_technical": "handle_technical",
+        "handle_billing": "handle_billing",
+        "handle_general": "handle_general",
+        "escalate": "escalate"
+    }
+)
+
+workflow.add_edge("handle_technical", END)
+workflow.add_edge("handle_billing", END)
+workflow.add_edge("handle_general", END)
+workflow.add_edge("escalate", END)
+
+workflow.set_entry_point("categorize")
+
+app = workflow.compile()
+
+# Define the function that integrates the workflow.
+def run_customer_support(query: str) -> Dict[str, str]:
+    results = app.invoke({"query": query})
+    return {
+        "Category": results['category'],
+        "Sentiment": results['sentiment'],
+        "Response": results['response']
+    }
+
 
 """
 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
@@ -7,58 +128,84 @@ For more information on `huggingface_hub` Inference API support, please check th
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
+import gradio as gr
+
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    message, 
+    history: list[tuple[str, str]], 
+    system_message, 
+    max_tokens, 
+    temperature, 
+    top_p
 ):
     messages = [{"role": "system", "content": system_message}]
-
+    
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-
+    
     messages.append({"role": "user", "content": message})
-
+    
     response = ""
-
+    
+    # Simulate streaming from the client
     for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
+        messages, 
+        max_tokens=max_tokens, 
+        stream=True, 
+        temperature=temperature, 
+        top_p=top_p
     ):
         token = message.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
-"""
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
-)
-
+# Define a custom Gradio Chat Interface
+with gr.Blocks() as demo:
+    gr.Markdown("### AI-Powered Customer Support Assistant")
+    
+    chatbot = gr.ChatInterface(
+        respond, 
+        additional_inputs=[
+            gr.Textbox(
+                value="You are a friendly chatbot.",
+                label="System Message",
+                info="Customize how the assistant behaves in conversations."
+            ),
+            gr.Slider(
+                minimum=1, 
+                maximum=2048, 
+                value=512, 
+                step=1, 
+                label="Max New Tokens",
+                info="Maximum number of tokens for the assistant's response."
+            ),
+            gr.Slider(
+                minimum=0.1, 
+                maximum=4.0, 
+                value=0.7, 
+                step=0.1, 
+                label="Temperature",
+                info="Controls randomness in the assistant's response."
+            ),
+            gr.Slider(
+                minimum=0.1, 
+                maximum=1.0, 
+                value=0.95, 
+                step=0.05, 
+                label="Top-p (Nucleus Sampling)",
+                info="Limits sampling to a subset of tokens with cumulative probability top_p."
+            ),
+        ]
+    )
+    
+    gr.Markdown("### Instructions")
+    gr.Textbox(
+        value="Enter your query, select response settings, and start the conversation.",
+        interactive=False,
+    )
 
 if __name__ == "__main__":
     demo.launch()