Update app.py

app.py

@@ -1,39 +1,36 @@
 import os
-import json
 import asyncio
+import json
+import time
 import gradio as gr
 
 from web_search import XrudraWebSearch
 
 
-# ============================================================
-# X-RUDRA GRADIO APP
-# ============================================================
-
-VERSION = "3.0.0"
-
 ENGINE = XrudraWebSearch()
 
 
 # ============================================================
-# ASYNC SEARCH
+# RESEARCH ENGINE
 # ============================================================
 
-async def run_search(
-    question: str,
-    max_results: int,
-    max_rounds: int,
-    use_models: bool,
-    freshness: str,
+async def research(
+    question,
+    max_results,
+    max_rounds,
+    use_models,
+    freshness,
 ):
     if not question or not question.strip():
         return (
-            " Please enter a question.",
-            "",
-            "",
+            [],
+            "⚪ Waiting for a question...",
+            "No research started.",
             "",
         )
 
+    start = time.perf_counter()
+
     try:
         report = await ENGINE.search(
             question=question.strip(),
@@ -49,132 +46,161 @@ async def run_search(
             else report
         )
 
+        sources = data.get("sources", [])
+        claims = data.get("claims", [])
+        contradictions = data.get("contradictions", [])
+
         # ----------------------------------------------------
-        # Sources
+        # FINAL ANSWER
         # ----------------------------------------------------
 
-        sources = data.get("sources", [])
+        answer_parts = []
+
+        for claim in claims[:8]:
+            text = claim.get("claim", "").strip()
+
+            if text:
+                answer_parts.append(text)
+
+        if answer_parts:
+            answer = "\n\n".join(answer_parts)
+        else:
+            answer = (
+                "Research completed, but no high-confidence "
+                "evidence passages were extracted."
+            )
+
+        # ----------------------------------------------------
+        # ACTIVITY
+        # ----------------------------------------------------
+
+        activity = f"""
+### ⚡ X-RUDRA Research Pipeline
+
+✓ Task analyzed  
+✓ M1 + M2 query planning completed  
+✓ DuckDuckGo web discovery completed  
+✓ {len(sources)} sources fetched  
+✓ Evidence extraction completed  
+✓ Source quality evaluated  
+✓ Cross-source verification completed  
+{"⚠️ Contradictions detected" if contradictions else "✓ No major contradictions detected"}  
+✓ Final synthesis completed  
+
+**Research time:** {int((time.perf_counter() - start) * 1000)} ms
+"""
+
+        # ----------------------------------------------------
+        # SOURCES
+        # ----------------------------------------------------
 
-        source_lines = []
+        source_md = "### 📚 Sources\n\n"
 
         for i, source in enumerate(sources, 1):
-            title = source.get("title", "Untitled")
+            title = source.get("title") or "Untitled"
             url = source.get("url", "")
             method = source.get("fetch_method", "unknown")
             score = source.get("source_score", 0)
 
-            source_lines.append(
-                f"### {i}. {title}\n"
-                f"**URL:** {url}\n"
-                f"**Fetch:** `{method}`\n"
-                f"**Score:** `{score}`\n"
+            source_md += (
+                f"**{i}. [{title}]({url})**\n\n"
+                f"`{method}` · source score `{score}`\n\n"
             )
 
-        source_text = "\n\n".join(source_lines)
+        if not sources:
+            source_md += "No sources returned."
 
         # ----------------------------------------------------
-        # Claims
+        # EVIDENCE
         # ----------------------------------------------------
 
-        claims = data.get("claims", [])
-
-        claim_lines = []
-
-        for i, claim in enumerate(claims, 1):
-            claim_text = claim.get("claim", "")
-            url = claim.get("source_url", "")
-            score = claim.get("support_score", 0)
+        evidence_md = "### 🧠 Evidence\n\n"
 
-            claim_lines.append(
-                f"### Claim {i}\n"
-                f"{claim_text}\n\n"
-                f"Source: `{url}`\n"
-                f"Support: `{score}`"
+        for i, claim in enumerate(claims[:20], 1):
+            evidence_md += (
+                f"**Evidence {i}**\n\n"
+                f"{claim.get('claim', '')}\n\n"
+                f"Support score: "
+                f"`{claim.get('support_score', 0)}`\n\n"
+                f"Source: `{claim.get('source_url', '')}`\n\n"
+                "---\n\n"
             )
 
-        claim_text = "\n\n".join(claim_lines)
+        if not claims:
+            evidence_md += "No evidence extracted."
 
         # ----------------------------------------------------
-        # Contradictions
+        # CONTRADICTIONS
         # ----------------------------------------------------
 
-        contradictions = data.get(
-            "contradictions",
-            [],
-        )
-
         if contradictions:
-            contradiction_text = "\n\n".join(
-                [
-                    (
-                        f"### Possible contradiction {i}\n\n"
-                        f"**A:** {x.get('claim_a', '')}\n\n"
-                        f"Source A: {x.get('source_a', '')}\n\n"
-                        f"**B:** {x.get('claim_b', '')}\n\n"
-                        f"Source B: {x.get('source_b', '')}"
-                    )
-                    for i, x in enumerate(
-                        contradictions,
-                        1,
-                    )
-                ]
-            )
+            verification_md = "### ⚠️ Contradictions\n\n"
+
+            for i, item in enumerate(
+                contradictions,
+                1,
+            ):
+                verification_md += (
+                    f"**Potential contradiction {i}**\n\n"
+                    f"**Source A:**\n"
+                    f"{item.get('claim_a', '')}\n\n"
+                    f"**Source B:**\n"
+                    f"{item.get('claim_b', '')}\n\n"
+                    "---\n\n"
+                )
         else:
-            contradiction_text = (
+            verification_md = (
+                "### ✅ Verification\n\n"
                 "No major automatic contradictions detected."
             )
 
         # ----------------------------------------------------
-        # Full JSON
+        # RETURN CHAT + PANELS
         # ----------------------------------------------------
 
-        json_text = json.dumps(
-            data,
-            ensure_ascii=False,
-            indent=2,
-        )
-
-        # ----------------------------------------------------
-        # Summary
-        # ----------------------------------------------------
-
-        summary = (
-            f"## X-RUDRA Research Complete\n\n"
-            f"**Question:** {question}\n\n"
-            f"**Rounds:** {data.get('rounds', 0)}\n\n"
-            f"**Sources:** {len(sources)}\n\n"
-            f"**Claims:** {len(claims)}\n\n"
-            f"**Stopping reason:** "
-            f"{data.get('stopping_reason', 'unknown')}\n\n"
-            f"**Models:** "
-            f"`Shrijanagain/M1` + `Shrijanagain/M2`\n\n"
-            f"**Web:** DuckDuckGo + Scrapling + Playwright"
-        )
+        chat = [
+            {
+                "role": "user",
+                "content": question,
+            },
+            {
+                "role": "assistant",
+                "content": answer,
+            },
+        ]
 
         return (
-            summary,
-            source_text or "No sources returned.",
-            claim_text or "No claims extracted.",
-            contradiction_text + "\n\n---\n\n```json\n"
-            + json_text
-            + "\n```",
+            chat,
+            activity,
+            source_md,
+            evidence_md + "\n" + verification_md,
         )
 
     except Exception as exc:
+
+        chat = [
+            {
+                "role": "user",
+                "content": question,
+            },
+            {
+                "role": "assistant",
+                "content": (
+                    "❌ X-RUDRA encountered an error:\n\n"
+                    f"`{type(exc).__name__}: {exc}`"
+                ),
+            },
+        ]
+
         return (
-            f"## Search failed\n\n"
-            f"`{type(exc).__name__}: {exc}`",
-            "",
+            chat,
+            "❌ Research failed.",
             "",
             "",
         )
 
 
-# ============================================================
-# SYNC WRAPPER FOR GRADIO
-# ============================================================
-
-def search_wrapper(
+def run_research(
     question,
     max_results,
     max_rounds,
@@ -182,7 +208,7 @@ def search_wrapper(
     freshness,
 ):
     return asyncio.run(
-        run_search(
+        research(
             question,
             max_results,
             max_rounds,
@@ -193,195 +219,308 @@ def search_wrapper(
 
 
 # ============================================================
-# HEALTH
+# CSS
 # ============================================================
 
-def health():
-    return (
-        "##  X-RUDRA\n\n"
-        f"Version: `{VERSION}`\n\n"
-        "M1: `Shrijanagain/M1`\n\n"
-        "M2: `Shrijanagain/M2`\n\n"
-        "Web Search: `DuckDuckGo`\n\n"
-        "Fetcher: `Scrapling`\n\n"
-        "Browser: `Playwright`\n\n"
-        "Evidence Engine: `Enabled`"
-    )
+CSS = r"""
+:root {
+    --radius: 18px;
+}
 
+body {
+    background: #f7f7f8 !important;
+}
 
-# ============================================================
-# GRADIO UI
-# ============================================================
+.gradio-container {
+    max-width: 1500px !important;
+    margin: auto !important;
+}
 
-CSS = """
-#title {
+#header {
     text-align: center;
-    font-size: 42px;
+    padding: 18px 0 8px 0;
+}
+
+#logo {
+    font-size: 34px;
     font-weight: 800;
+    letter-spacing: -1px;
 }
 
-#subtitle {
-    text-align: center;
-    opacity: 0.75;
+#tagline {
+    opacity: .65;
+    font-size: 14px;
+}
+
+#chat {
+    border-radius: 18px !important;
+}
+
+#research-panel {
+    border-radius: 18px !important;
 }
 
-.search-btn {
-    min-height: 55px;
-    font-size: 18px;
-    font-weight: 700;
+#search-button {
+    border-radius: 14px !important;
+    min-height: 50px !important;
+    font-weight: 700 !important;
+}
+
+.status-box {
+    border-radius: 14px !important;
+}
+
+footer {
+    display: none !important;
 }
 """
 
 
+# ============================================================
+# UI
+# ============================================================
+
 with gr.Blocks(
     title="X-RUDRA",
     css=CSS,
+    theme=gr.themes.Soft(
+        primary_hue="indigo",
+        neutral_hue="slate",
+    ),
 ) as demo:
 
-    gr.Markdown(
-        "# ⚡ X-RUDRA",
-        elem_id="title",
-    )
+    # --------------------------------------------------------
+    # HEADER
+    # --------------------------------------------------------
 
-    gr.Markdown(
-        "Dual-model AI research + live web intelligence",
-        elem_id="subtitle",
-    )
-
-    gr.Markdown(
+    gr.HTML(
+        """
+        <div id="header">
+            <div id="logo">⚡ X-RUDRA</div>
+            <div id="tagline">
+                Dual-Model AI Research · Live Web Intelligence · Evidence
+            </div>
+        </div>
         """
-**M1 + M2 → Query Planning → DuckDuckGo → Scrapling
-→ Playwright → Evidence → Cross-check**
-"""
     )
 
+    # --------------------------------------------------------
+    # MAIN
+    # --------------------------------------------------------
+
     with gr.Row():
 
-        with gr.Column(scale=4):
+        # ====================================================
+        # CHAT
+        # ====================================================
+
+        with gr.Column(
+            scale=7,
+        ):
 
-            question = gr.Textbox(
-                label="Research Question",
+            chatbot = gr.Chatbot(
+                label="X-RUDRA",
+                type="messages",
+                height=620,
+                elem_id="chat",
                 placeholder=(
-                    "Ask anything that requires current web research..."
+                    "Ask X-RUDRA anything that needs "
+                    "real-time research..."
                 ),
-                lines=5,
             )
 
             with gr.Row():
 
-                max_results = gr.Slider(
-                    minimum=1,
-                    maximum=30,
-                    value=10,
-                    step=1,
-                    label="Max Sources",
+                question = gr.Textbox(
+                    placeholder=(
+                        "Ask a research question..."
+                    ),
+                    lines=2,
+                    scale=8,
+                    show_label=False,
                 )
 
-                max_rounds = gr.Slider(
-                    minimum=1,
-                    maximum=5,
-                    value=3,
-                    step=1,
-                    label="Research Rounds",
+                send = gr.Button(
+                    "➤",
+                    variant="primary",
+                    scale=1,
+                    elem_id="search-button",
                 )
 
-            with gr.Row():
+        # ====================================================
+        # LIVE RESEARCH PANEL
+        # ====================================================
 
-                use_models = gr.Checkbox(
-                    value=True,
-                    label="Use M1 + M2",
-                )
+        with gr.Column(
+            scale=4,
+        ):
 
-                freshness = gr.Dropdown(
-                    choices=[
-                        "auto",
-                        "latest",
-                        "recent",
-                        "current",
-                    ],
-                    value="auto",
-                    label="Freshness",
-                )
+            gr.Markdown(
+                "## 🔬 Live Research"
+            )
 
-            search_button = gr.Button(
-                "SEARCH WITH X-RUDRA",
-                variant="primary",
-                elem_classes=["search-btn"],
+            activity = gr.Markdown(
+                "⚪ Waiting for research..."
             )
 
-            health_button = gr.Button(
-                "Check Engine Health",
+            gr.Markdown(
+                "---"
             )
 
-        with gr.Column(scale=6):
+            gr.Markdown(
+                "### ⚙️ Engine"
+            )
+
+            gr.Markdown(
+                """
+**Models**
+
+`M1` · `Shrijanagain/M1`
+
+`M2` · `Shrijanagain/M2`
+
+**Search**
 
-            summary = gr.Markdown(
-                label="Research Summary",
+`DuckDuckGo`
+
+**Fetching**
+
+`Scrapling`
+
+**Dynamic pages**
+
+`Playwright`
+
+**Verification**
+
+`Evidence Engine`
+"""
             )
 
-    with gr.Tab("Sources"):
+    # --------------------------------------------------------
+    # CONTROLS
+    # --------------------------------------------------------
 
-        sources = gr.Markdown(
-            "Sources will appear here."
-        )
+    with gr.Accordion(
+        "⚙️ Research Controls",
+        open=False,
+    ):
 
-    with gr.Tab(" Evidence / Claims"):
+        with gr.Row():
 
-        claims = gr.Markdown(
-            "Evidence will appear here."
-        )
+            max_results = gr.Slider(
+                minimum=1,
+                maximum=30,
+                value=10,
+                step=1,
+                label="Maximum Sources",
+            )
 
-    with gr.Tab("⚠️ Contradictions + Raw JSON"):
+            max_rounds = gr.Slider(
+                minimum=1,
+                maximum=5,
+                value=3,
+                step=1,
+                label="Research Rounds",
+            )
 
-        contradictions = gr.Markdown(
-            "Contradictions and raw response will appear here."
-        )
+        with gr.Row():
 
-    health_output = gr.Markdown()
+            use_models = gr.Checkbox(
+                value=True,
+                label="Use M1 + M2",
+            )
 
-    search_button.click(
-        fn=search_wrapper,
-        inputs=[
-            question,
-            max_results,
-            max_rounds,
-            use_models,
-            freshness,
-        ],
-        outputs=[
-            summary,
-            sources,
-            claims,
-            contradictions,
-        ],
+            freshness = gr.Dropdown(
+                choices=[
+                    "auto",
+                    "latest",
+                    "recent",
+                    "current",
+                ],
+                value="auto",
+                label="Freshness",
+            )
+
+    # --------------------------------------------------------
+    # RESEARCH DETAILS
+    # --------------------------------------------------------
+
+    with gr.Tabs():
+
+        with gr.Tab(
+            "📚 Sources",
+        ):
+            sources = gr.Markdown(
+                "Sources will appear after research."
+            )
+
+        with gr.Tab(
+            "🧠 Evidence",
+        ):
+            evidence = gr.Markdown(
+                "Evidence will appear after research."
+            )
+
+        with gr.Tab(
+            "⚖️ Verification",
+        ):
+            verification = gr.Markdown(
+                "Verification will appear after research."
+            )
+
+    # --------------------------------------------------------
+    # EXAMPLES
+    # --------------------------------------------------------
+
+    gr.Markdown(
+        "### Try X-RUDRA"
     )
 
-    question.submit(
-        fn=search_wrapper,
-        inputs=[
-            question,
-            max_results,
-            max_rounds,
-            use_models,
-            freshness,
-        ],
-        outputs=[
-            summary,
-            sources,
-            claims,
-            contradictions,
+    gr.Examples(
+        examples=[
+            ["What are the latest AI education initiatives by UNESCO?"],
+            ["What happened in AI research this week?"],
+            ["Compare the latest open-source AI models."],
+            ["Find reliable evidence about India's AI ecosystem."],
         ],
+        inputs=question,
+    )
+
+    # --------------------------------------------------------
+    # EVENTS
+    # --------------------------------------------------------
+
+    inputs = [
+        question,
+        max_results,
+        max_rounds,
+        use_models,
+        freshness,
+    ]
+
+    outputs = [
+        chatbot,
+        activity,
+        sources,
+        evidence,
+    ]
+
+    send.click(
+        fn=run_research,
+        inputs=inputs,
+        outputs=outputs,
     )
 
-    health_button.click(
-        fn=health,
-        inputs=[],
-        outputs=[health_output],
+    question.submit(
+        fn=run_research,
+        inputs=inputs,
+        outputs=outputs,
     )
 
 
 # ============================================================
-# LAUNCH
+# HF SPACES LAUNCH
 # ============================================================
 
 if __name__ == "__main__":