Create app.py

app.py

@@ -0,0 +1,400 @@
+import os
+import json
+import asyncio
+import gradio as gr
+
+from web_search import XrudraWebSearch
+
+
+# ============================================================
+# X-RUDRA GRADIO APP
+# ============================================================
+
+VERSION = "3.0.0"
+
+ENGINE = XrudraWebSearch()
+
+
+# ============================================================
+# ASYNC SEARCH
+# ============================================================
+
+async def run_search(
+    question: str,
+    max_results: int,
+    max_rounds: int,
+    use_models: bool,
+    freshness: str,
+):
+    if not question or not question.strip():
+        return (
+            " Please enter a question.",
+            "",
+            "",
+            "",
+        )
+
+    try:
+        report = await ENGINE.search(
+            question=question.strip(),
+            max_results=int(max_results),
+            max_rounds=int(max_rounds),
+            use_models=use_models,
+            freshness_mode=freshness,
+        )
+
+        data = (
+            report.model_dump()
+            if hasattr(report, "model_dump")
+            else report
+        )
+
+        # ----------------------------------------------------
+        # Sources
+        # ----------------------------------------------------
+
+        sources = data.get("sources", [])
+
+        source_lines = []
+
+        for i, source in enumerate(sources, 1):
+            title = source.get("title", "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_text = "\n\n".join(source_lines)
+
+        # ----------------------------------------------------
+        # Claims
+        # ----------------------------------------------------
+
+        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)
+
+            claim_lines.append(
+                f"### Claim {i}\n"
+                f"{claim_text}\n\n"
+                f"Source: `{url}`\n"
+                f"Support: `{score}`"
+            )
+
+        claim_text = "\n\n".join(claim_lines)
+
+        # ----------------------------------------------------
+        # 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,
+                    )
+                ]
+            )
+        else:
+            contradiction_text = (
+                "No major automatic contradictions detected."
+            )
+
+        # ----------------------------------------------------
+        # Full JSON
+        # ----------------------------------------------------
+
+        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"
+        )
+
+        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```",
+        )
+
+    except Exception as exc:
+        return (
+            f"## Search failed\n\n"
+            f"`{type(exc).__name__}: {exc}`",
+            "",
+            "",
+            "",
+        )
+
+
+# ============================================================
+# SYNC WRAPPER FOR GRADIO
+# ============================================================
+
+def search_wrapper(
+    question,
+    max_results,
+    max_rounds,
+    use_models,
+    freshness,
+):
+    return asyncio.run(
+        run_search(
+            question,
+            max_results,
+            max_rounds,
+            use_models,
+            freshness,
+        )
+    )
+
+
+# ============================================================
+# HEALTH
+# ============================================================
+
+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`"
+    )
+
+
+# ============================================================
+# GRADIO UI
+# ============================================================
+
+CSS = """
+#title {
+    text-align: center;
+    font-size: 42px;
+    font-weight: 800;
+}
+
+#subtitle {
+    text-align: center;
+    opacity: 0.75;
+}
+
+.search-btn {
+    min-height: 55px;
+    font-size: 18px;
+    font-weight: 700;
+}
+"""
+
+
+with gr.Blocks(
+    title="X-RUDRA",
+    css=CSS,
+) as demo:
+
+    gr.Markdown(
+        "# ⚡ X-RUDRA",
+        elem_id="title",
+    )
+
+    gr.Markdown(
+        "Dual-model AI research + live web intelligence",
+        elem_id="subtitle",
+    )
+
+    gr.Markdown(
+        """
+**M1 + M2 → Query Planning → DuckDuckGo → Scrapling
+→ Playwright → Evidence → Cross-check**
+"""
+    )
+
+    with gr.Row():
+
+        with gr.Column(scale=4):
+
+            question = gr.Textbox(
+                label="Research Question",
+                placeholder=(
+                    "Ask anything that requires current web research..."
+                ),
+                lines=5,
+            )
+
+            with gr.Row():
+
+                max_results = gr.Slider(
+                    minimum=1,
+                    maximum=30,
+                    value=10,
+                    step=1,
+                    label="Max Sources",
+                )
+
+                max_rounds = gr.Slider(
+                    minimum=1,
+                    maximum=5,
+                    value=3,
+                    step=1,
+                    label="Research Rounds",
+                )
+
+            with gr.Row():
+
+                use_models = gr.Checkbox(
+                    value=True,
+                    label="Use M1 + M2",
+                )
+
+                freshness = gr.Dropdown(
+                    choices=[
+                        "auto",
+                        "latest",
+                        "recent",
+                        "current",
+                    ],
+                    value="auto",
+                    label="Freshness",
+                )
+
+            search_button = gr.Button(
+                "SEARCH WITH X-RUDRA",
+                variant="primary",
+                elem_classes=["search-btn"],
+            )
+
+            health_button = gr.Button(
+                "Check Engine Health",
+            )
+
+        with gr.Column(scale=6):
+
+            summary = gr.Markdown(
+                label="Research Summary",
+            )
+
+    with gr.Tab("Sources"):
+
+        sources = gr.Markdown(
+            "Sources will appear here."
+        )
+
+    with gr.Tab(" Evidence / Claims"):
+
+        claims = gr.Markdown(
+            "Evidence will appear here."
+        )
+
+    with gr.Tab("⚠️ Contradictions + Raw JSON"):
+
+        contradictions = gr.Markdown(
+            "Contradictions and raw response will appear here."
+        )
+
+    health_output = gr.Markdown()
+
+    search_button.click(
+        fn=search_wrapper,
+        inputs=[
+            question,
+            max_results,
+            max_rounds,
+            use_models,
+            freshness,
+        ],
+        outputs=[
+            summary,
+            sources,
+            claims,
+            contradictions,
+        ],
+    )
+
+    question.submit(
+        fn=search_wrapper,
+        inputs=[
+            question,
+            max_results,
+            max_rounds,
+            use_models,
+            freshness,
+        ],
+        outputs=[
+            summary,
+            sources,
+            claims,
+            contradictions,
+        ],
+    )
+
+    health_button.click(
+        fn=health,
+        inputs=[],
+        outputs=[health_output],
+    )
+
+
+# ============================================================
+# LAUNCH
+# ============================================================
+
+if __name__ == "__main__":
+
+    port = int(
+        os.environ.get(
+            "PORT",
+            "7860",
+        )
+    )
+
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=port,
+        show_error=True,
+    )