Update app.py

app.py

@@ -1,140 +1,140 @@
-import streamlit as st
-import os
-from dotenv import load_dotenv
-from tavily import TavilyClient
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.agents import AgentExecutor, Tool
-from langchain.agents import create_tool_calling_agent
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langgraph.graph import END, Graph
-
-
-load_dotenv()
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
-
-llm = ChatGoogleGenerativeAI(
-    model="gemini-2.0-flash",  
-    temperature=0.7,
-    google_api_key=GOOGLE_API_KEY
-)
-
-
-tavily_client = TavilyClient(api_key=TAVILY_API_KEY)
-
-def tavily_search(query: str):
-    try:
-        result = tavily_client.search(
-            query=query,
-            search_depth="advanced",
-            include_raw_content=True
-        )
-        return str(result)
-    except Exception as e:
-        return f"Error searching Tavily: {str(e)}"
-
-
-tavily_tool = Tool(
-    name="tavily_search",
-    func=tavily_search,
-    description="A premium search engine that returns high-quality, up-to-date information."
-)
-
-
-def create_researcher_agent():
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", """You are a senior research analyst. Your job:
-1. Use Tavily for authoritative info.
-2. Cross-validate across multiple sources.
-3. Extract key facts, stats & quotes.
-4. Always include source URLs."""),
-        ("user", "{input}"),
-        MessagesPlaceholder(variable_name="agent_scratchpad")
-    ])
-    agent = create_tool_calling_agent(llm, [tavily_tool], prompt)
-    return AgentExecutor(agent=agent, tools=[tavily_tool])
-
-
-def generate_final_report(research_output: str) -> str:
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", """You are a technical writer. Structure and present the research:
-- Use markdown formatting
-- Academic tone
-- Include proper citations
-- Highlight insights.
-- Make sure to answer the user's original query completely."""), 
-        ("user", "{input}")
-    ])
-    formatted_prompt = prompt.format_messages(input=research_output)
-    return llm.invoke(formatted_prompt).content
-
-
-def create_workflow():
-    workflow = Graph()
-
-    def research_node(state):
-        agent = create_researcher_agent()
-        output = agent.invoke({"input": state["query"], "agent_scratchpad": []})
-        return {"research_output": output["output"]}
-
-    def write_node(state):
-        report = generate_final_report(state["research_output"])
-        return {"final_report": report}
-
-    workflow.add_node("research", research_node)
-    workflow.add_node("write", write_node)
-    workflow.add_edge("research", "write")
-    workflow.add_edge("write", END)
-    workflow.set_entry_point("research")
-    return workflow
-
-
-def main():
-    st.set_page_config(page_title="AI Research Assistant", page_icon="🔍", layout="wide")
-    st.title("🔍 AI Research Assistant")
-    st.markdown("""Enter a query and let the AI prepare a research-backed report:
-- Searches authoritative sources
-- Validates information
-- Delivers a professional markdown report.
-""")
-
-    query = st.text_area("Enter your research query:",
-                         placeholder="e.g., 'Impact of AI on healthcare jobs in 2024'",
-                         height=100)
-
-    if st.button("Research", type="primary"):
-        if not query:
-            st.warning("Please enter a research query first.")
-            return
-
-        if not GOOGLE_API_KEY or not TAVILY_API_KEY:
-            st.error(" API keys missing! Check your `.env` file.")
-            return
-
-        with st.spinner("Researching your topic..."):
-            try:
-                workflow = create_workflow()
-                app = workflow.compile()
-                
-                
-                final_output = app.invoke({"query": query})
-                
-                
-                with st.expander(" Research Report", expanded=True):
-                    if "final_report" in final_output:
-                        st.markdown(final_output["final_report"])
-                    elif "research_output" in final_output:
-                        st.markdown("### Research Findings (Unformatted)")
-                        st.markdown(final_output["research_output"])
-                    else:
-                        st.warning("No results were generated")
-
-                st.success(" Research completed!")
-
-            except Exception as e:
-                st.error(f" An error occurred: {str(e)}")
-                st.exception(e)
-
-
-if __name__ == "__main__":
+import streamlit as st
+import os
+from dotenv import load_dotenv
+from tavily import TavilyClient
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.agents import AgentExecutor, Tool
+from langchain.agents import create_tool_calling_agent
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langgraph.graph import END, Graph
+
+
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+
+llm = ChatGoogleGenerativeAI(
+    model="gemini-2.0-flash",  
+    temperature=0.7,
+    google_api_key=GOOGLE_API_KEY
+)
+
+
+tavily_client = TavilyClient(api_key=TAVILY_API_KEY)
+
+def tavily_search(query: str):
+    try:
+        result = tavily_client.search(
+            query=query,
+            search_depth="advanced",
+            include_raw_content=True
+        )
+        return str(result)
+    except Exception as e:
+        return f"Error searching Tavily: {str(e)}"
+
+
+tavily_tool = Tool(
+    name="tavily_search",
+    func=tavily_search,
+    description="A premium search engine that returns high-quality, up-to-date information."
+)
+
+
+def create_researcher_agent():
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", """You are a senior research analyst. Your job:
+1. Use Tavily for authoritative info.
+2. Cross-validate across multiple sources.
+3. Extract key facts, stats & quotes.
+4. Always include source URLs."""),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad")
+    ])
+    agent = create_tool_calling_agent(llm, [tavily_tool], prompt)
+    return AgentExecutor(agent=agent, tools=[tavily_tool])
+
+
+def generate_final_report(research_output: str) -> str:
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", """You are a technical writer. Structure and present the research:
+- Use markdown formatting
+- Academic tone
+- Include proper citations
+- Highlight insights.
+- Make sure to answer the user's original query completely."""), 
+        ("user", "{input}")
+    ])
+    formatted_prompt = prompt.format_messages(input=research_output)
+    return llm.invoke(formatted_prompt).content
+
+
+def create_workflow():
+    workflow = Graph()
+
+    def research_node(state):
+        agent = create_researcher_agent()
+        output = agent.invoke({"input": state["query"], "agent_scratchpad": []})
+        return {"research_output": output["output"]}
+
+    def write_node(state):
+        report = generate_final_report(state["research_output"])
+        return {"final_report": report}
+
+    workflow.add_node("research", research_node)
+    workflow.add_node("write", write_node)
+    workflow.add_edge("research", "write")
+    workflow.add_edge("write", END)
+    workflow.set_entry_point("research")
+    return workflow
+
+
+def main():
+    st.set_page_config(page_title="AI Research Assistant", page_icon="🔍", layout="wide")
+    st.title(" AI Research Assistant")
+    st.markdown("""Enter a query and let the AI prepare a research-backed report:
+- Searches authoritative sources
+- Validates information
+- Delivers a professional markdown report.
+""")
+
+    query = st.text_area("Enter your research query:",
+                         placeholder="e.g., 'Impact of AI on healthcare jobs in 2024'",
+                         height=100)
+
+    if st.button("Research", type="primary"):
+        if not query:
+            st.warning("Please enter a research query first.")
+            return
+
+        if not GOOGLE_API_KEY or not TAVILY_API_KEY:
+            st.error(" API keys missing! Check your `.env` file.")
+            return
+
+        with st.spinner("Researching your topic..."):
+            try:
+                workflow = create_workflow()
+                app = workflow.compile()
+                
+                
+                final_output = app.invoke({"query": query})
+                
+                
+                with st.expander(" Research Report", expanded=True):
+                    if "final_report" in final_output:
+                        st.markdown(final_output["final_report"])
+                    elif "research_output" in final_output:
+                        st.markdown("### Research Findings (Unformatted)")
+                        st.markdown(final_output["research_output"])
+                    else:
+                        st.warning("No results were generated")
+
+                st.success(" Research completed!")
+
+            except Exception as e:
+                st.error(f" An error occurred: {str(e)}")
+                st.exception(e)
+
+
+if __name__ == "__main__":
     main()