Update app.py

app.py

@@ -1,102 +1,20 @@
-from fastapi import FastAPI
-import pandas as pd
-import uvicorn
-import joblib
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from pydantic import BaseModel
-import gradio as gr
-import os
-import requests
-
-app = FastAPI()
-
-# ✅ Correct dataset URL from Hugging Face (Replace if needed)
-DATASET_URL = "https://huggingface.co/datasets/SailajaS/CDART"
-
-# File path for saving dataset
-DATASET_PATH = "dataset.csv"
-
-# Function to download dataset
-def download_dataset():
-    if not os.path.exists(DATASET_PATH):
-        print("📥 Downloading dataset from Hugging Face...")
-        response = requests.get(DATASET_URL)
-
-        if response.status_code == 200:
-            with open(DATASET_PATH, "wb") as file:
-                file.write(response.content)
-            print("✅ Dataset downloaded successfully!")
-        else:
-            print(f"❌ Failed to download dataset: {response.status_code}")
-            raise Exception("Dataset download failed.")
-
-# ✅ Download dataset at startup
-download_dataset()
-
-# ✅ Load dataset
-df = pd.read_csv(DATASET_PATH)
-
-# ✅ Check if necessary columns exist
-required_columns = ["Case Problem", "Feedback"]
-for col in required_columns:
-    if col not in df.columns:
-        raise Exception(f"❌ Column '{col}' is missing from the dataset!")
-
-# ✅ Encode categorical variables
-encoder = LabelEncoder()
-df["Case Problem"] = encoder.fit_transform(df["Case Problem"])
-df["Feedback"] = encoder.fit_transform(df["Feedback"])
-
-# ✅ Train Model
-X = df[["Case Problem"]]
-y = df["Feedback"]
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-model = RandomForestClassifier(n_estimators=100, random_state=42)
-model.fit(X_train, y_train)
-
-# ✅ Save model
-joblib.dump(model, "feedback_model.pkl")
-print("✅ Model trained successfully!")
-
-# ✅ API Input Model
-class PredictionInput(BaseModel):
-    case_problem: str
-
-@app.post("/predict/")
-def predict_feedback(data: PredictionInput):
-    """ Predicts feedback based on Case Problem """
-    if model is None:
-        return {"error": "Model is not trained yet."}
-
-    try:
-        case_problem_encoded = encoder.transform([data.case_problem])
-        prediction = model.predict([[case_problem_encoded[0]]])
-        feedback_predicted = encoder.inverse_transform(prediction)[0]
-        return {"Predicted Feedback": feedback_predicted}
-    except Exception as e:
-        return {"error": str(e)}
-
-# ✅ Gradio UI
-def gradio_interface(case_problem):
-    if model is None:
-        return "Model not trained yet."
-
-    try:
-        case_problem_encoded = encoder.transform([case_problem])
-        prediction = model.predict([[case_problem_encoded[0]]])
-        feedback_predicted = encoder.inverse_transform(prediction)[0]
-        return f"Predicted Feedback: {feedback_predicted}"
-    except:
-        return "Invalid case problem. Please enter a valid category."
-
-# ✅ Start both API & Gradio
-def start_app():
-    """ Start API and Gradio Interface """
-    gr_interface = gr.Interface(fn=gradio_interface, inputs="text", outputs="text")
-    gr_interface.launch()
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-
-if __name__ == "__main__":
-    start_app()
+runtime error
+Exit code: 1. Reason: 📥 Downloading dataset from Hugging Face...
+✅ Dataset downloaded successfully!
+Traceback (most recent call last):
+  File "/home/user/app/app.py", line 39, in <module>
+    df = pd.read_csv(DATASET_PATH)
+  File "/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
+    return _read(filepath_or_buffer, kwds)
+  File "/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 626, in _read
+    return parser.read(nrows)
+  File "/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 1923, in read
+    ) = self._engine.read(  # type: ignore[attr-defined]
+  File "/usr/local/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
+    chunks = self._reader.read_low_memory(nrows)
+  File "parsers.pyx", line 838, in pandas._libs.parsers.TextReader.read_low_memory
+  File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
+  File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
+  File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
+  File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
+pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 5, saw 3