Create app.py

app.py

@@ -0,0 +1,169 @@
+import json
+import pandas as pd
+import matplotlib.pyplot as plt
+import gradio as gr
+from openai import OpenAI
+
+# ==================================================
+# OpenAI client helper (API key from user)
+# ==================================================
+def get_openai_client(api_key: str):
+    return OpenAI(api_key=api_key)
+
+# ==================================================
+# CSV Loader + Normalizer
+# ==================================================
+def load_and_normalize_csv(csv_file):
+    df = pd.read_csv(csv_file)
+
+    # Normalize column names
+    df.columns = (
+        df.columns
+        .str.strip()
+        .str.lower()
+        .str.replace(" ", "")
+        .str.replace("/", "")
+        .str.replace("_", "")
+    )
+
+    # Normalize drcr column if present
+    if "drcr" in df.columns:
+        df["drcr"] = (
+            df["drcr"]
+            .astype(str)
+            .str.strip()
+            .str.lower()
+            .replace({
+                "cr": "credit",
+                "db": "debit"
+            })
+        )
+
+    return df
+
+# ==================================================
+# Ask OpenAI for INTENT (STRICT JSON)
+# ==================================================
+def get_intent(question: str, api_key: str) -> dict:
+    client = get_openai_client(api_key)
+
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        response_format={"type": "json_object"},
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are a data analysis planner.\n"
+                    "Return ONLY valid JSON.\n"
+                    "Do NOT explain.\n\n"
+                    "JSON format:\n"
+                    "{\n"
+                    '  "action": "count | sum | plot",\n'
+                    '  "filters": { "year": number | null, "drcr": string | null },\n'
+                    '  "groupby": "year | drcr | null"\n'
+                    "}"
+                )
+            },
+            {
+                "role": "user",
+                "content": question
+            }
+        ]
+    )
+
+    return json.loads(response.choices[0].message.content)
+
+# ==================================================
+# Execute intent using real Python
+# ==================================================
+def execute_intent(intent: dict, df: pd.DataFrame):
+    data = df.copy()
+
+    # Apply filters
+    filters = intent.get("filters", {})
+    for key, value in filters.items():
+        if value is not None and key in data.columns:
+            data = data[data[key] == value]
+
+    action = intent.get("action")
+    group_col = intent.get("groupby")
+
+    # COUNT
+    if action == "count":
+        if group_col:
+            return data.groupby(group_col).size()
+        return len(data)
+
+    # SUM
+    if action == "sum":
+        if group_col:
+            return data.groupby(group_col)["amount"].sum()
+        return data["amount"].sum()
+
+    # PLOT
+    if action == "plot":
+        if not group_col:
+            raise ValueError("Plot requires groupby")
+
+        result = data.groupby(group_col).size()
+        result.plot(kind="bar")
+        plt.title("Result")
+        plt.tight_layout()
+        plt.show()
+        return result
+
+    raise ValueError(f"Unknown action: {action}")
+
+# ==================================================
+# End-to-end question answering
+# ==================================================
+def answer_question(question: str, api_key: str, df: pd.DataFrame):
+    intent = get_intent(question, api_key)
+    return execute_intent(intent, df)
+
+# ==================================================
+# Gradio wrapper
+# ==================================================
+def gradio_answer(api_key, csv_file, question):
+    try:
+        if not api_key:
+            return "Please provide your OpenAI API key."
+
+        if csv_file is None:
+            return "Please upload a CSV file."
+
+        if not question:
+            return "Please enter a question."
+
+        df = load_and_normalize_csv(csv_file)
+        result = answer_question(question, api_key, df)
+
+        if hasattr(result, "to_string"):
+            return result.to_string()
+
+        return str(result)
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# ==================================================
+# Gradio Interface (Spaces entry point)
+# ==================================================
+demo = gr.Interface(
+    fn=gradio_answer,
+    inputs=[
+        gr.Textbox(label="OpenAI API Key", type="password"),
+        gr.File(label="Upload CSV File", file_types=[".csv"]),
+        gr.Textbox(
+            label="Ask a question about your CSV",
+            placeholder="How many credit operations happened in 2022?"
+        )
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Chat with your CSV 📊",
+    description="Upload any CSV file and ask natural language questions about it"
+)
+
+if __name__ == "__main__":
+    demo.launch()