Create app.py

app.py

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+
+import os
+import time
+from typing import TypedDict
+from langgraph.graph import StateGraph, START, END
+from langchain_groq import ChatGroq
+from langchain_community.tools import DuckDuckGoSearchRun
+import gradio as gr
+
+# ─────────────────────────────────────────
+# 1. Config — set your key here or via HF Secrets
+# ─────────────────────────────────────────
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+
+llm = ChatGroq(model="llama-3.1-8b-instant")
+search = DuckDuckGoSearchRun()
+
+# ─────────────────────────────────────────
+# 2. State + Graph (same as research_agent.py)
+# ─────────────────────────────────────────
+class ResearchState(TypedDict):
+    query: str
+    search_results: str
+    summary: str
+
+def search_node(state: ResearchState):
+    return {"search_results": search.run(state["query"])}
+
+def summarize_node(state: ResearchState):
+    prompt = f"""You are a research assistant. Answer the user's question clearly 
+based on the search results below. Be factual and concise (3-5 sentences).
+
+Question: {state["query"]}
+
+Search Results:
+{state["search_results"]}"""
+    response = llm.invoke(prompt)
+    return {"summary": response.content}
+
+graph = StateGraph(ResearchState)
+graph.add_node("search", search_node)
+graph.add_node("summarize", summarize_node)
+graph.add_edge(START, "search")
+graph.add_edge("search", "summarize")
+graph.add_edge("summarize", END)
+agent = graph.compile()
+
+# ─────────────────────────────────────────
+# 3. Gradio handler
+# ─────────────────────────────────────────
+def run_agent(query):
+    if not query.strip():
+        return "", "⚠️ Please enter a question."
+    
+    start = time.time()
+    result = agent.invoke({"query": query})
+    elapsed = round(time.time() - start, 1)
+    
+    answer = result["summary"]
+    status = f"✅ Done in {elapsed}s · LangGraph → DuckDuckGo → ChatGroq"
+    return answer, status
+
+def clear_all():
+    return "", "", "Ready."
+
+# ─────────────────────────────────────────
+# 4. UI
+# ─────────────────────────────────────────
+EXAMPLES = [
+    "What is LangGraph and how does it work?",
+    "Latest AI language models released in 2025?",
+    "How does Retrieval-Augmented Generation (RAG) work?",
+    "What is the difference between LangChain and LangGraph?",
+]
+
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Research Agent") as demo:
+
+    gr.Markdown("""
+    ## 🔍 AI Research Agent
+    Ask any question. The agent searches the web and returns a clear, summarized answer.
+    """)
+
+    with gr.Row():
+        # ── Left panel: Input ──
+        with gr.Column(scale=2):
+            query_input = gr.Textbox(
+                label="Your Question",
+                placeholder="e.g. What is LangGraph?",
+                lines=3,
+            )
+            with gr.Row():
+                submit_btn = gr.Button("🚀 Search", variant="primary", scale=3)
+                clear_btn = gr.Button("Clear", scale=1)
+
+            gr.Markdown("**Try an example:**")
+            for example in EXAMPLES:
+                gr.Button(example, size="sm").click(
+                    fn=lambda e=example: e,
+                    outputs=query_input
+                )
+
+        # ── Right panel: Output ──
+        with gr.Column(scale=3):
+            output_box = gr.Textbox(
+                label="📄 Answer",
+                lines=12,
+                interactive=False,
+            )
+            status_md = gr.Markdown("Ready.")
+
+    # ── Wire up events ──
+    submit_btn.click(
+        fn=run_agent,
+        inputs=query_input,
+        outputs=[output_box, status_md],
+    )
+    query_input.submit(          # also fires on Enter key
+        fn=run_agent,
+        inputs=query_input,
+        outputs=[output_box, status_md],
+    )
+    clear_btn.click(
+        fn=clear_all,
+        outputs=[query_input, output_box, status_md],
+    )
+
+demo.launch()# app.py
+import os
+import time
+from typing import TypedDict
+from langgraph.graph import StateGraph, START, END
+from langchain_groq import ChatGroq
+from langchain_community.tools import DuckDuckGoSearchRun
+import gradio as gr
+
+# ─────────────────────────────────────────
+# 1. Config — set your key here or via HF Secrets
+# ─────────────────���───────────────────────
+os.environ["GROQ_API_KEY"] = "your_groq_api_key_here"
+
+llm = ChatGroq(model="llama3-8b-8192", temperature=0.3)
+search = DuckDuckGoSearchRun()
+
+# ─────────────────────────────────────────
+# 2. State + Graph (same as research_agent.py)
+# ─────────────────────────────────────────
+class ResearchState(TypedDict):
+    query: str
+    search_results: str
+    summary: str
+
+def search_node(state: ResearchState):
+    return {"search_results": search.run(state["query"])}
+
+def summarize_node(state: ResearchState):
+    prompt = f"""You are a research assistant. Answer the user's question clearly 
+based on the search results below. Be factual and concise (3-5 sentences).
+
+Question: {state["query"]}
+
+Search Results:
+{state["search_results"]}"""
+    response = llm.invoke(prompt)
+    return {"summary": response.content}
+
+graph = StateGraph(ResearchState)
+graph.add_node("search", search_node)
+graph.add_node("summarize", summarize_node)
+graph.add_edge(START, "search")
+graph.add_edge("search", "summarize")
+graph.add_edge("summarize", END)
+agent = graph.compile()
+
+# ─────────────────────────────────────────
+# 3. Gradio handler
+# ─────────────────────────────────────────
+def run_agent(query):
+    if not query.strip():
+        return "", "⚠️ Please enter a question."
+    
+    start = time.time()
+    result = agent.invoke({"query": query})
+    elapsed = round(time.time() - start, 1)
+    
+    answer = result["summary"]
+    status = f"✅ Done in {elapsed}s · LangGraph → DuckDuckGo → ChatGroq"
+    return answer, status
+
+def clear_all():
+    return "", "", "Ready."
+
+# ─────────────────────────────────────────
+# 4. UI
+# ─────────────────────────────────────────
+EXAMPLES = [
+    "What is LangGraph and how does it work?",
+    "Latest AI language models released in 2025?",
+    "How does Retrieval-Augmented Generation (RAG) work?",
+    "What is the difference between LangChain and LangGraph?",
+]
+
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Research Agent") as demo:
+
+    gr.Markdown("""
+    ## 🔍 AI Research Agent
+    Ask any question. The agent searches the web and returns a clear, summarized answer.
+    """)
+
+    with gr.Row():
+        # ── Left panel: Input ──
+        with gr.Column(scale=2):
+            query_input = gr.Textbox(
+                label="Your Question",
+                placeholder="e.g. What is LangGraph?",
+                lines=3,
+            )
+            with gr.Row():
+                submit_btn = gr.Button("🚀 Search", variant="primary", scale=3)
+                clear_btn = gr.Button("Clear", scale=1)
+
+            gr.Markdown("**Try an example:**")
+            for example in EXAMPLES:
+                gr.Button(example, size="sm").click(
+                    fn=lambda e=example: e,
+                    outputs=query_input
+                )
+
+        # ── Right panel: Output ──
+        with gr.Column(scale=3):
+            output_box = gr.Textbox(
+                label="📄 Answer",
+                lines=12,
+                interactive=False,
+            )
+            status_md = gr.Markdown("Ready.")
+
+    # ── Wire up events ──
+    submit_btn.click(
+        fn=run_agent,
+        inputs=query_input,
+        outputs=[output_box, status_md],
+    )
+    query_input.submit(          # also fires on Enter key
+        fn=run_agent,
+        inputs=query_input,
+        outputs=[output_box, status_md],
+    )
+    clear_btn.click(
+        fn=clear_all,
+        outputs=[query_input, output_box, status_md],
+    )
+
+demo.launch()