donee hopefully

app.py

@@ -0,0 +1,100 @@
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+import os
+from langchain_folder.main import ReturnKeywordsfromPrompt
+from dotenv import load_dotenv
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+load_dotenv()
+
+app = Flask(__name__)
+CORS(app)
+
+CSV_FILE_PATH = os.getenv('file_path')
+GRAPH_DIR = './static/graphs'
+
+
+@app.route('/api/search', methods=['POST'])
+def search():
+    data = request.get_json()
+    query = data.get('query', '')
+    keywords = ReturnKeywordsfromPrompt(query)
+    return jsonify({"status": "success", "queryReceived": query})
+
+
+def generate_graphs_from_csv(csv_path, output_dir):
+    df = pd.read_csv(csv_path)
+    os.makedirs(output_dir, exist_ok=True)
+
+    numeric_cols = df.select_dtypes(include='number').columns.tolist()
+    categorical_cols = df.select_dtypes(include='object').columns.tolist()
+    graph_paths = []
+    print (categorical_cols)
+
+    if len(numeric_cols) >= 1:
+        plt.figure(figsize=(4, 3))
+        sns.histplot(df[numeric_cols[0]], kde=True)
+        plt.title(f'{numeric_cols[0]} Distribution')
+        plt.tight_layout()
+        path = f'{output_dir}/graph_1_hist.png'
+        plt.savefig(path)
+        graph_paths.append(path)
+
+    if len(numeric_cols) >= 2 and categorical_cols:
+        plt.figure(figsize=(4, 3))
+        sns.boxplot(data=df, x=categorical_cols[0], y=numeric_cols[1])
+        plt.title(f'{numeric_cols[1]} by {categorical_cols[0]}')
+        plt.tight_layout()
+        path = f'{output_dir}/graph_2_box.png'
+        plt.savefig(path)
+        graph_paths.append(path)
+
+    if categorical_cols:
+        plt.figure(figsize=(4, 3))
+        sns.countplot(data=df, x=categorical_cols[0])
+        plt.title(f'{categorical_cols[0]} Distribution')
+        plt.tight_layout()
+        path = f'{output_dir}/graph_3_count.png'
+        plt.savefig(path)
+        graph_paths.append(path)
+
+    if len(numeric_cols) >= 2:
+        plt.figure(figsize=(4, 3))
+        sns.scatterplot(data=df, x=numeric_cols[0], y=numeric_cols[1])
+        plt.title(f'{numeric_cols[0]} vs {numeric_cols[1]}')
+        plt.tight_layout()
+        path = f'{output_dir}/graph_4_scatter.png'
+        plt.savefig(path)
+        graph_paths.append(path)
+
+    return graph_paths
+
+
+@app.route('/api/get_csv', methods=['GET'])
+def get_csv():
+    try:
+        if not os.path.exists(CSV_FILE_PATH):
+            return jsonify({"error": "CSV file not found"}), 404
+        with open(CSV_FILE_PATH, "r", encoding="utf-8") as f:
+            return f.read(), 200, {
+                "Content-Type": "text/csv",
+                "Content-Disposition": "inline; filename=dataset.csv"
+            }
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route('/api/download_csv', methods=['GET'])
+def download_csv():
+    return send_file(CSV_FILE_PATH, as_attachment=True)
+
+
+@app.route('/api/get_graphs', methods=['GET'])
+def get_graphs():
+    paths = generate_graphs_from_csv(CSV_FILE_PATH, GRAPH_DIR)
+    return jsonify({"graphs": [p.replace("./static", "/static") for p in paths]})
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,21 @@
+fastapi
+uvicorn
+numpy
+pandas
+spacy
+keras
+tensorflow
+scikit-learn
+en-core-web-lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.8.0/en_core_web_lg-3.8.0.tar.gz
+langchain_groq
+langchain_core
+selenium
+openml
+openai
+rapidfuzz
+matplotlib
+seaborn
+flask
+google
+datasets
+kaggle

space.yaml

@@ -0,0 +1,4 @@
+runtime: docker
+docker:
+  ports: 7860
+  cmd: ["flask", "run", "--host=0.0.0.0", "--port=7860"]