Update app.py

app.py

@@ -1,85 +1,34 @@
-import gradio as gr
-from typing import Dict, TypedDict
 from langgraph.graph import Graph
-import transformers
-from transformers import pipeline
+from typing import TypedDict
 
-class AgentState(TypedDict):
+# Define the state type
+class State(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()
+    current_step: str
+
+# Create nodes (functions that represent different states)
+def collect_info(state: State) -> State:
+    return {
+        **state,
+        "messages": state["messages"] + ["Information collected"],
+        "current_step": "process"
+    }
+
+def process_info(state: State) -> State:
+    return {
+        **state,
+        "messages": state["messages"] + ["Information processed"],
+        "current_step": "end"
+    }
+
+# Create the graph
+workflow = Graph()
+
+# Add nodes and edges
+workflow.add_node("collect", collect_info)
+workflow.add_node("process", process_info)
+workflow.add_edge("collect", "process")
+
+# Set the entry point and compile
+workflow.set_entry_point("collect")
+app = workflow.compile()