Update app.py

Author: Hariprashad-Ravikumar

backend/app.py

@@ -2,21 +2,19 @@
 import io
 import pandas as pd
 import matplotlib
-matplotlib.use('Agg')  # <- forces a non-GUI backend
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
-from flask import Flask, request, jsonify, send_file, redirect, url_for, render_template
+from flask import Flask, request, jsonify
 from sklearn.linear_model import LinearRegression
 import numpy as np
 import openai
 import sys
 from datetime import datetime
 from flask_cors import CORS
 
-app = Flask(__name__, static_folder="static", template_folder="../frontend")
+app = Flask(__name__, static_folder="static")
 CORS(app, resources={r"/*": {"origins": "https://hariprashad-ravikumar.github.io"}})
-@app.route("/")
-def home():
-    return render_template("index.html", summary="", log="", forecast="", plot_url=None)
+
 UPLOAD_FOLDER = "uploads"
 STATIC_FOLDER = "static"
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
@@ -33,20 +31,19 @@ def upload_file():
     log_stream.truncate(0)
     log_stream.seek(0)
 
-    file = request.files["file"]
+    file = request.files.get("file")
+    if file is None:
+        return jsonify({"error": "No file uploaded"}), 400
+
     filepath = os.path.join(UPLOAD_FOLDER, file.filename)
     file.save(filepath)
-    
     log_print("📦 File uploaded:", file.filename)
 
-    # Load and clean
-    log_print("🔧 Importing pandas...")
     df = pd.read_csv(filepath)
     df.dropna(inplace=True)
     df.columns = ['X', 'Y']
     log_print("🔍 Cleaned Data:", df.head())
 
-    # Plot
     plt.figure()
     plt.scatter(df['X'], df['Y'])
     plt.xlabel('X')
@@ -56,82 +53,101 @@ def upload_file():
     plt.close()
     log_print("📊 Scatter plot saved.")
 
-    # Model
-    log_print("⚙️ Converting X (dates) to numeric format...")
     df['X'] = pd.to_datetime(df['X'], errors='coerce')
     df.dropna(inplace=True)
     X = df['X'].map(pd.Timestamp.toordinal).values.reshape(-1, 1)
     y = df['Y'].values
 
     if len(X) == 0:
         log_print("❌ No valid data to fit the model.")
-        return render_template("index.html", summary="No valid data found.", log=log_stream.getvalue(), forecast="N/A", plot_url=None)
+        return jsonify({
+            "summary": "No valid data found.",
+            "log": log_stream.getvalue(),
+            "forecast": "N/A",
+            "plot_url": None
+        })
 
     model = LinearRegression()
     model.fit(X, y)
     log_print("🤖 Model trained.")
 
-    # OpenAI Summary
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-    response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "Summarize this dataset:"},
-            {"role": "user", "content": df.head(10).to_csv()}
-        ]
-     )
-
-    summary = response.choices[0].message.content
-
-    log_print("🧠 OpenAI Summary generated.")
-    return jsonify({"summary": summary, "log": log_stream.getvalue(), "forecast": "Submit future x-values below to get predictions.", "plot_url": "/static/plot.png"})
-
+    try:
+        openai.api_key = os.getenv("OPENAI_API_KEY")
+        client = openai.OpenAI(api_key=openai.api_key)
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "Summarize this dataset:"},
+                {"role": "user", "content": df.head(10).to_csv()}
+            ]
+        )
+        summary = response.choices[0].message.content
+        log_print("🧠 OpenAI Summary generated.")
+    except Exception as e:
+        summary = "OpenAI summarization failed."
+        log_print("❌ OpenAI error:", str(e))
+
+    return jsonify({
+        "summary": summary,
+        "log": log_stream.getvalue(),
+        "forecast": "Submit future x-values below to get predictions.",
+        "plot_url": "/static/plot.png"
+    })
 
 @app.route("/predict", methods=["POST"])
 def predict():
     future_x = request.form.get("future_x")
+    if not future_x:
+        return jsonify({
+            "forecast": "No future values provided.",
+            "log": log_stream.getvalue(),
+            "plot_url": None
+        })
 
     try:
-        # Convert input strings to ordinal integers
-        values = [
-            datetime.strptime(x.strip(), "%Y-%m-%d").toordinal()
-            for x in future_x.split(",")
-        ]
+        values = [datetime.strptime(x.strip(), "%Y-%m-%d").toordinal()
+                  for x in future_x.split(",")]
     except ValueError:
         log_print("❌ Invalid date format. Use YYYY-MM-DD.")
-        return render_template(
-            "index.html",
-            summary="",
-            log=log_stream.getvalue(),
-            forecast="Invalid date format. Use YYYY-MM-DD.",
-            plot_url=None
-        )
+        return jsonify({
+            "forecast": "Invalid date format. Use YYYY-MM-DD.",
+            "log": log_stream.getvalue(),
+            "plot_url": None
+        })
 
-    # Reload most recent uploaded file
-    filename = os.listdir(UPLOAD_FOLDER)[0]
-    df = pd.read_csv(os.path.join(UPLOAD_FOLDER, filename))
-    df.dropna(inplace=True)
-    df.columns = ['X', 'Y']
-    df['X'] = pd.to_datetime(df['X'], errors='coerce')
-    df.dropna(inplace=True)
-
-    X = df['X'].map(pd.Timestamp.toordinal).values.reshape(-1, 1)
-    y = df['Y'].values
-
-    model = LinearRegression()
-    model.fit(X, y)
-
-    predicted = model.predict(np.array(values).reshape(-1, 1))
-    result = {
-        datetime.fromordinal(v).strftime("%Y-%m-%d"): round(p, 2)
-        for v, p in zip(values, predicted)
-    }
-
-    log_print("🔮 Forecast complete.")
+    try:
+        filename = os.listdir(UPLOAD_FOLDER)[0]
+        df = pd.read_csv(os.path.join(UPLOAD_FOLDER, filename))
+        df.dropna(inplace=True)
+        df.columns = ['X', 'Y']
+        df['X'] = pd.to_datetime(df['X'], errors='coerce')
+        df.dropna(inplace=True)
+        X = df['X'].map(pd.Timestamp.toordinal).values.reshape(-1, 1)
+        y = df['Y'].values
+
+        model = LinearRegression()
+        model.fit(X, y)
+
+        predicted = model.predict(np.array(values).reshape(-1, 1))
+        result = {
+            datetime.fromordinal(v).strftime("%Y-%m-%d"): round(p, 2)
+            for v, p in zip(values, predicted)
+        }
+
+        log_print("🔮 Forecast complete.")
+        return jsonify({
+            "forecast": result,
+            "log": log_stream.getvalue(),
+            "plot_url": "/static/plot.png"
+        })
+
+    except Exception as e:
+        log_print("❌ Prediction failed:", str(e))
+        return jsonify({
+            "forecast": "Prediction failed.",
+            "log": log_stream.getvalue(),
+            "plot_url": None
+        })
 
-    return jsonify({ "forecast": result, "log": log_stream.getvalue(), "plot_url": "/static/plot.png" })
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=80, debug=True)
-
+    app.run(host="0.0.0.0", port=80)