Create app.py

app.py

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+import gradio as gr
+from typing import Dict, TypedDict
+from langgraph.graph import Graph
+import transformers
+from transformers import pipeline
+
+class AgentState(TypedDict):
+    messages: list[str]
+    current_step: int
+    final_answer: str
+
+def analyze_sentiment(state: AgentState) -> AgentState:
+    sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+    message = state["messages"][-1]
+    result = sentiment_analyzer(message)[0]
+    state["messages"].append(f"Sentiment analysis: {result['label']} ({result['score']:.2f})")
+    state["current_step"] += 1
+    return state
+
+def generate_response(state: AgentState) -> AgentState:
+    generator = pipeline("text-generation", model="gpt2")
+    context = " ".join(state["messages"][-2:])
+    generated_text = generator(context, max_length=50, num_return_sequences=1)[0]["generated_text"]
+    state["messages"].append(f"Generated response: {generated_text}")
+    state["current_step"] += 1
+    return state
+
+def create_summary(state: AgentState) -> AgentState:
+    if state["current_step"] >= 4:
+        summary = "Analysis complete. Final summary: "
+        summary += " | ".join(state["messages"])
+        state["final_answer"] = summary
+    return state
+
+def build_graph():
+    workflow = Graph()
+    workflow.add_node("sentiment", analyze_sentiment)
+    workflow.add_node("generate", generate_response)
+    workflow.add_node("summarize", create_summary)
+    workflow.add_edge("sentiment", "generate")
+    workflow.add_edge("generate", "summarize")
+    workflow.add_edge("summarize", "sentiment")
+    workflow.set_entry_point("sentiment")
+    return workflow.compile()
+
+# Initialize the graph globally
+GRAPH = build_graph()
+
+def process_input(message: str, history: list) -> tuple:
+    # Initialize state
+    state = AgentState(
+        messages=[message],
+        current_step=0,
+        final_answer=""
+    )
+    
+    # Run the graph for a few steps
+    for _ in range(3):
+        state = GRAPH(state)
+        if state["final_answer"]:
+            break
+    
+    # Format the conversation history
+    conversation = "\n".join(state["messages"])
+    
+    # Add final answer if available
+    if state["final_answer"]:
+        conversation += f"\n\nFinal Summary:\n{state['final_answer']}"
+    
+    return conversation
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_input,
+    inputs=[
+        gr.Textbox(label="Enter your message"),
+        gr.State([])  # For maintaining conversation history
+    ],
+    outputs=gr.Textbox(label="Analysis Results"),
+    title="LangGraph Demo with Hugging Face",
+    description="Enter a message to analyze sentiment and generate responses using LangGraph and Hugging Face models."
+)
+
+if __name__ == "__main__":
+    iface.launch()