Create app.py

app.py

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+import gradio as gr
+import requests
+import base64
+import pandas as pd
+import io
+import time
+import os
+from io import BytesIO
+from fpdf import FPDF
+import tempfile
+from typing import Tuple, List, Optional, Dict, Any
+from PIL import Image
+
+API_URL = "https://sroy46--insightsphere-wrapper.modal.run"
+
+LANGUAGES = [
+    ("en", "English"),
+    ("zh", "Chinese"),
+    #("ja", "Japanese"),
+    ("ko", "Korean"),
+]
+
+def language_name_to_code(name: str) -> str:
+    for code, lang_name in LANGUAGES:
+        if lang_name == name:
+            return code
+    return "en"
+
+def make_api_request(endpoint: str, payload: Dict[str, Any]) -> requests.Response:
+    response = requests.post(
+        f"{API_URL}{endpoint}",
+        json=payload,
+        timeout=None
+    )
+    response.raise_for_status()
+    return response
+
+def run_insightsphere(query: str, chart_types: List[str], tone: str, language_name: str) -> Tuple[str, List[Any], str, Optional[pd.DataFrame], Optional[Dict]]:
+    try:
+        language_code = language_name_to_code(language_name)
+        response = make_api_request("/interpret", {"query": query})
+        sql_query = response.json()["sql"]
+
+        response = make_api_request("/collect", {"sql": sql_query})
+        raw_data = pd.read_csv(io.StringIO(base64.b64decode(response.json()["data"]).decode()))
+
+        response = make_api_request("/clean", {
+            "data": base64.b64encode(raw_data.to_csv(index=False).encode()).decode()
+        })
+        clean_data = pd.read_csv(io.StringIO(base64.b64decode(response.json()["data"]).decode()))
+
+        response = make_api_request("/analyze", {
+            "data": base64.b64encode(clean_data.to_csv(index=False).encode()).decode()
+        })
+        insights = response.json()["insights"]
+
+        charts = []
+        clean_data_encoded = base64.b64encode(clean_data.to_csv(index=False).encode()).decode()
+        for chart_type in chart_types:
+            response = make_api_request("/visualize", {
+                "data": clean_data_encoded,
+                "insights": insights,
+                "chart_type": chart_type,
+                "tone": tone
+            })
+            img_bytes = base64.b64decode(response.json()["image"])
+            img = Image.open(BytesIO(img_bytes))
+            charts.append(img)
+
+        response = make_api_request("/summarize", {
+            "insights": insights,
+            "tone": tone,
+            "language_code": language_code
+        })
+        summary = response.json()["summary"]
+
+        response = make_api_request("/recommend", {
+            "insights": insights,
+            "tone": tone,
+            "language_code": language_code
+        })
+        recommendations = response.json()["recommendations"]
+
+        return summary, charts, recommendations, clean_data, insights
+
+    except Exception as e:
+        error_msg = f"Error: {str(e)}"
+        return error_msg, [], error_msg, None, None
+
+def save_pdf(summary: str, charts: List[Any], recommendations: str) -> str:
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("helvetica", size=12)
+
+    def add_unicode_text(text):
+        text = text.encode('latin-1', 'replace').decode('latin-1')
+        pdf.multi_cell(0, 10, text)
+
+    pdf.multi_cell(0, 10, "Summary:")
+    add_unicode_text(summary)
+    pdf.ln(10)
+
+    for i, img in enumerate(charts):
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
+            img.save(tmp.name, format="PNG")
+            pdf.image(tmp.name, x=10, w=180)
+            pdf.ln(10)
+        try:
+            os.unlink(tmp.name)
+        except:
+            pass
+
+    pdf.multi_cell(0, 10, "Recommendations:")
+    add_unicode_text(recommendations)
+
+    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    pdf.output(tmp_file.name)
+    return tmp_file.name
+
+def save_excel(data: pd.DataFrame, insights: Dict) -> str:
+    try:
+        tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx")
+        with pd.ExcelWriter(tmp_file.name, engine='xlsxwriter') as writer:
+            data.to_excel(writer, sheet_name='Clean Data', index=False)
+            if isinstance(insights, dict):
+                pd.DataFrame([insights]).to_excel(writer, sheet_name='Insights', index=False)
+        return tmp_file.name
+    except Exception as e:
+        raise
+
+with gr.Blocks(title="InsightSphere", css=".gradio-container {max-width: 900px !important}") as demo:
+    gr.Markdown("# Your AI Agent for Multilingual Query-to-Insight Data Workflows")
+
+    with gr.Row():
+        with gr.Column():
+            query_input = gr.Textbox(
+                label="Enter a query to explore your data with AI:",
+                lines=2,
+                value="Show the most popular baby names in the US from 1910 to 2013, ranked by total occurrences.",
+                placeholder="Enter a natural language query"
+            )
+
+            gr.Examples(
+                examples=[
+                    "List the top 10 most popular male baby names by total count from 1910 to 2013 in California.",
+                    "Compare average income by education level across all states.",
+                    "Summarize total agricultural yield by crop type over the past decade.",
+                    "What are the top 5 counties with highest unemployment rates in 2019?"
+                ],
+                inputs=query_input
+            )
+
+            with gr.Row():
+                tone_input = gr.Dropdown(
+                    label="Narrative Style for Reports",
+                    choices=["formal", "casual", "executive"],
+                    value="formal"
+                )
+                language_input = gr.Dropdown(
+                    label="Select Your Preferred Language",
+                    choices=[name for _, name in LANGUAGES],
+                    value="English"
+                )
+
+            chart_type_input = gr.CheckboxGroup(
+                label="Select Graph Types to Visualize Insights",
+                choices=["histogram", "scatter", "boxplot", "bar", "line"],
+                value=["histogram"]
+            )
+
+            generate_btn = gr.Button("Generate Insights", variant="primary")
+
+        with gr.Column():
+            summary_output = gr.Markdown(label="### Analysis Summary")
+            rec_output = gr.Markdown(label="### Recommendations")
+            chart_gallery = gr.Gallery(
+                label="Generated Visualizations",
+                columns=2,
+                height="auto"
+            )
+            download_excel_btn = gr.Button("Download Data as Excel File")
+
+    state = gr.State()
+
+    def run_and_store(query: str, chart_types: List[str], tone: str, language: str):
+        result = run_insightsphere(query, chart_types, tone, language)
+        state.value = {
+            "summary": result[0],
+            "charts": result[1],
+            "recommendations": result[2],
+            "clean_data": result[3],
+            "insights": result[4],
+        }
+        chart_names = {
+            "histogram": "Distribution",
+            "scatter": "Scatter Plot",
+            "boxplot": "Box Plot",
+            "bar": "Bar Chart",
+            "line": "Line Chart"
+        }
+        display_images = []
+        for i, img in enumerate(result[1]):
+            chart_type = chart_types[i] if i < len(chart_types) else "chart"
+            display_images.append((img, f"{chart_names.get(chart_type, 'Chart')} {i+1}"))
+        return result[0], display_images, result[2]
+
+    generate_btn.click(
+        run_and_store,
+        inputs=[query_input, chart_type_input, tone_input, language_input],
+        outputs=[summary_output, chart_gallery, rec_output]
+    )
+
+    download_excel_btn.click(
+        lambda: save_excel(state.value["clean_data"], state.value["insights"]) if state.value else None,
+        outputs=gr.File(label="Download Excel Report")
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)