Update app.py

app.py

@@ -1,184 +1,83 @@
-
-import os
 import traceback
 import streamlit as st
-from dotenv import load_dotenv
-from tavily import TavilyClient
-from langchain_openai import ChatOpenAI
-from typing import Dict, TypedDict, List
-from langgraph.graph import StateGraph, END
 import streamlit.components.v1 as components
-from langchain_core.prompts import ChatPromptTemplate
-
-# Load environment variables
-load_dotenv()
-
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
-
-# Initialize the LLM (using OpenAI as an example, replace with your preferred model)
-llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
-
-# Initialize Tavily client for web search
-tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
-
-# Define the shared state for the agents
-class ResearchState(TypedDict):
-    query: str
-    research_data: List[Dict]
-    final_answer: str
-
-# Research Agent: Crawls the web and gathers information
-def research_agent(state: ResearchState) -> ResearchState:
-    query = state["query"]
-    print(f"Research Agent: Searching for '{query}'...")
-
-    # Use Tavily to search the web
-    search_results = tavily_client.search(query, max_results=5)
-
-    # Extract relevant information from search results
-    research_data = []
-    for result in search_results["results"]:
-        research_data.append({
-            "title": result["title"],
-            "url": result["url"],
-            "content": result["content"][:500]  # Limit content length for brevity
-        })
-
-    print(f"Research Agent: Found {len(research_data)} relevant sources.")
-    return {"research_data": research_data}
-
-# Answer Drafter Agent: Processes research data and drafts a response
-def answer_drafter_agent(state: ResearchState) -> ResearchState:
-    research_data = state["research_data"]
-    query = state["query"]
-
-    # Create a prompt for the answer drafter
-    prompt = ChatPromptTemplate.from_template(
-        """
-        You are an expert at drafting concise and accurate answers. Based on the following research data, provide a clear and informative response to the query: "{query}".
-        Research Data:
-        {research_data}
-        Provide a well-structured answer in 3-5 sentences, citing the sources where relevant.
-        """
-    )
-
-    # Format the research data for the prompt
-    research_text = "\n".join([f"- {item['title']}: {item['content']} (Source: {item['url']})" for item in research_data])
-    chain = prompt | llm
-
-    # Generate the final answer
-    response = chain.invoke({"query": query, "research_data": research_text})
-    final_answer = response.content
-
-    print("Answer Drafter Agent: Drafted the final answer.")
-    return {"final_answer": final_answer}
-
-# Define the LangGraph workflow
-def create_workflow():
-    workflow = StateGraph(ResearchState)
-
-    # Add nodes for each agent
-    workflow.add_node("research_agent", research_agent)
-    workflow.add_node("answer_drafter_agent", answer_drafter_agent)
-
-    # Define the flow: Research Agent -> Answer Drafter Agent -> End
-    workflow.add_edge("research_agent", "answer_drafter_agent")
-    workflow.add_edge("answer_drafter_agent", END)
-
-    # Set the entry point
-    workflow.set_entry_point("research_agent")
-
-    return workflow.compile()
-
-# Main function to run the system
-def run_deep_research_system(query: str) -> str:
-    # Initialize the workflow
-    app = create_workflow()
-
-    # Initial state
-    initial_state = {
-        "query": query,
-        "research_data": [],
-        "final_answer": ""
-    }
-
-    # Run the workflow
-    final_state = app.invoke(initial_state)
-
-    return final_state["final_answer"]
-
-# Set page configuration
-st.set_page_config(page_title="Deep Research AI", layout="centered")
-
-# Title and instructions
-st.title("Deep Research AI Agentic System")
-st.write("Enter a question below to get the latest insights from web research. Click 'Reset' to start over.")
-
-# Initialize session state
-if "show_reset_button" not in st.session_state:
-    st.session_state.show_reset_button = False
-if "question" not in st.session_state:
-    st.session_state.question = ""
-if "reset_triggered" not in st.session_state:
-    st.session_state.reset_triggered = False
-
-# JavaScript to clear the input field
-clear_input_js = """
-<script>
-    const input = document.querySelector('input[aria-label="Your Question"]');
-    if (input) {
-        input.value = '';
-    }
-</script>
-"""
-
-# Use a form to manage the question input and submission
-with st.form(key="question_form"):
-    st.session_state.question = st.text_input(
-        "Your Question",
-        placeholder="e.g., What are the latest advancements in quantum computing?",
-        value=st.session_state.question,
-        key="question_input"
-    )
-    submit_button = st.form_submit_button("Get Answer")
-
-# Process the form submission
-if submit_button:
-    if st.session_state.question:
-        st.write(f"Research Agent: Searching for '{st.session_state.question}'...")
-        try:
-            with st.spinner("Gathering research data..."):
-                answer = run_deep_research_system(st.session_state.question)
-            st.write("Research Agent: Found 5 relevant sources.")
-            st.write("Answer Drafter Agent: Drafted the final answer.")
-            st.write("**Final Answer:**")
-            st.write(answer)
-            st.session_state.show_reset_button = True
-        except Exception as e:
-            st.error(f"An error occurred: {str(e)}\n{traceback.format_exc()}")
-            st.session_state.show_reset_button = True
-    else:
-        st.warning("Please enter a question!")
-    # Reset the trigger flag after submission
-    st.session_state.reset_triggered = False
-
-# Function to clear the input state
-def clear_input():
-    st.session_state.show_reset_button = False
-    st.session_state.question = ""
-    st.session_state.pop("question_input", None)
-    st.session_state.pop("question_form", None)
-    st.session_state.reset_triggered = True
-
-# Show Reset button only if show_reset_button is True
-if st.session_state.show_reset_button:
-    if st.button("Reset"):
-        # Clear the input and reset state
-        clear_input()
-        # Refresh the webpage
-        st.rerun()
-
-# Execute JavaScript to clear the input field if reset was triggered
-if st.session_state.reset_triggered:
-    components.html(clear_input_js, height=0)
+from deep_research_system import run_deep_research_system
+ 
+if __name__ == "__main__":
+    # Define API base URL (Update for Hugging Face deployment)
+    API_URL = "http://localhost:8000/"
+
+    # Set page configuration
+    st.set_page_config(page_title="Deep Research AI", layout="centered")
+
+    # Title and instructions
+    st.title("Deep Research AI Agentic System")
+    st.write("Enter a question below to get the latest insights from web research. Click 'Reset' to start over.")
+
+    # Initialize session state
+    if "show_reset_button" not in st.session_state:
+        st.session_state.show_reset_button = False
+    if "question" not in st.session_state:
+        st.session_state.question = ""
+    if "reset_triggered" not in st.session_state:
+        st.session_state.reset_triggered = False
+
+    # JavaScript to clear the input field
+    clear_input_js = """
+    <script>
+        const input = document.querySelector('input[aria-label="Your Question"]');
+        if (input) {
+            input.value = '';
+        }
+    </script>
+    """
+
+    # Use a form to manage the question input and submission
+    with st.form(key="question_form"):
+        st.session_state.question = st.text_input(
+            "Your Question",
+            placeholder="e.g., What are the latest advancements in quantum computing?",
+            value=st.session_state.question,
+            key="question_input"
+        )
+        submit_button = st.form_submit_button("Get Answer")
+
+    # Process the form submission
+    if submit_button:
+        if st.session_state.question:
+            st.write(f"Research Agent: Searching for '{st.session_state.question}'...")
+            try:
+                with st.spinner("Gathering research data..."):
+                    answer = run_deep_research_system(st.session_state.question)
+                st.write("Research Agent: Found 5 relevant sources.")
+                st.write("Answer Drafter Agent: Drafted the final answer.")
+                st.write("**Final Answer:**")
+                st.write(answer)
+                st.session_state.show_reset_button = True
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}\n{traceback.format_exc()}")
+                st.session_state.show_reset_button = True
+        else:
+            st.warning("Please enter a question!")
+        # Reset the trigger flag after submission
+        st.session_state.reset_triggered = False
+
+    # Function to clear the input state
+    def clear_input():
+        st.session_state.show_reset_button = False
+        st.session_state.question = ""
+        st.session_state.pop("question_input", None)
+        st.session_state.pop("question_form", None)
+        st.session_state.reset_triggered = True
+
+    # Show Reset button only if show_reset_button is True
+    if st.session_state.show_reset_button:
+        if st.button("Reset"):
+            # Clear the input and reset state
+            clear_input()
+            # Refresh the webpage
+            st.rerun()
+
+    # Execute JavaScript to clear the input field if reset was triggered
+    if st.session_state.reset_triggered:
+        components.html(clear_input_js, height=0)