Update app.py

app.py

@@ -1,64 +1,116 @@
+# app.py
+import os
+from typing import Dict, TypedDict, Annotated, Sequence
 import gradio as gr
-from huggingface_hub import InferenceClient
+from langgraph.graph import END, StateGraph
+from langchain_core.messages import AIMessage
+import google.generativeai as genai
+from langsmith import Client
+from langchain_core.tracers.context import tracing_v2_enabled
 
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
+# Configuration - use environment variables for secrets
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
 
+# Setting up APIs
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+if LANGSMITH_API_KEY:
+    os.environ["LANGCHAIN_TRACING_V2"] = "true"
+    os.environ["LANGCHAIN_API_KEY"] = LANGSMITH_API_KEY
+    os.environ["LANGCHAIN_PROJECT"] = "Gemini-Multi-Agent-HF"
+    os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+    client = Client()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# I use my free tier model
+gemini_model = genai.GenerativeModel('gemini-1.5-flash')
 
-    messages.append({"role": "user", "content": message})
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[AIMessage], lambda x, y: x + y]
+    user_query: str
 
-    response = ""
+def research_agent(state: AgentState) -> Dict:
+    user_query = state['user_query']
+    prompt = f"""You are a research assistant. Conduct research on: {user_query}
+    Provide:
+    - 3 key findings
+    - 2 reliable sources
+    - Potential applications
+    Keep response concise (1-2 paragraphs)."""
+    
+    try:
+        response = gemini_model.generate_content(prompt)
+        return {"messages": [AIMessage(content=f"RESEARCH REPORT:\n{response.text}")]}
+    except Exception as e:
+        return {"messages": [AIMessage(content=f"Research failed: {str(e)}")]}
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+def writing_agent(state: AgentState) -> Dict:
+    messages = state['messages']
+    research_report = next(m.content for m in messages if "RESEARCH REPORT" in m.content)
+    prompt = f"""Write a short article (3 paragraphs max) about:
+    Topic: {state['user_query']}
+    Research: {research_report}
+    Include:
+    - Introduction
+    - Key points
+    - Conclusion"""
+    
+    try:
+        response = gemini_model.generate_content(prompt)
+        return {"messages": [AIMessage(content=f"ARTICLE DRAFT:\n{response.text}")]}
+    except Exception as e:
+        return {"messages": [AIMessage(content=f"Writing failed: {str(e)}")]}
 
-        response += token
-        yield response
+def editing_agent(state: AgentState) -> Dict:
+    draft = next(m.content for m in state['messages'] if "ARTICLE DRAFT" in m.content)
+    prompt = f"""Improve this draft (keep under 300 words):
+    {draft}
+    Make it:
+    - More engaging
+    - Better structured
+    - Easier to read"""
+    
+    try:
+        response = gemini_model.generate_content(prompt)
+        return {"messages": [AIMessage(content=f"FINAL OUTPUT:\n{response.text}")]}
+    except Exception as e:
+        return {"messages": [AIMessage(content=f"Editing failed: {str(e)}")]}
 
+def create_workflow():
+    workflow = StateGraph(AgentState)
+    workflow.add_node("researcher", research_agent)
+    workflow.add_node("writer", writing_agent)
+    workflow.add_node("editor", editing_agent)
+    workflow.add_edge("researcher", "writer")
+    workflow.add_edge("writer", "editor")
+    workflow.add_edge("editor", END)
+    workflow.set_entry_point("researcher")
+    return workflow.compile()
 
-"""
-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)",
-        ),
+app = create_workflow()
+
+def process_input(user_input):
+    try:
+        result = app.invoke({"messages": [], "user_query": user_input})
+        final_output = next(m.content for m in result['messages'] if "FINAL OUTPUT" in m.content)
+        return final_output.split("FINAL OUTPUT:")[1].strip()
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+demo = gr.Interface(
+    fn=process_input,
+    inputs=gr.Textbox(label="Your question", placeholder="Ask me anything..."),
+    outputs=gr.Textbox(label="Generated Article", lines=10),
+    title="🧠 AI Research Assistant",
+    description="A multi-agent system that researches topics and writes articles using Gemini 1.5 Flash",
+    examples=[
+        ["Explain quantum computing simply"],
+        ["What are the health benefits of meditation?"],
+        ["How do solar panels work?"]
     ],
+    allow_flagging="never"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()