Update app.py

app.py

@@ -1,32 +1,97 @@
+from fastapi import FastAPI, UploadFile, File
 import pandas as pd
-from faker import Faker
-import random
-
-# Initialize Faker
-fake = Faker()
-
-# Sample case problems and feedback
-case_problems = ["Login Issues", "Payment Failure", "UI Bug", "Slow Performance", "Feature Request"]
-feedback_types = ["Negative", "Positive", "Neutral"]
-details = [
-    "Unable to login after password reset.",
-    "Payment went through after retrying.",
-    "The interface is a bit confusing at times.",
-    "The page load time is slow.",
-    "Would love a dark mode feature."
-]
-
-# Generate 50 unique records
-data = {
-    "Name": [fake.name() for _ in range(50)],
-    "Email": [fake.email() for _ in range(50)],
-    "Case Problem": [random.choice(case_problems) for _ in range(50)],
-    "Feedback": [random.choice(feedback_types) for _ in range(50)],
-    "Details": [random.choice(details) for _ in range(50)],
-}
-
-# Create DataFrame and Save as CSV
-df = pd.DataFrame(data)
-df.to_csv("sample_case_records_real_names.csv", index=False)
-
-print("CSV File Generated Successfully")
+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
+
+app = FastAPI()
+
+# Ensure file upload directory exists
+UPLOAD_DIR = "uploaded_files"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+# Function to load dataset
+def load_data(file_path):
+    if file_path.endswith(".csv"):
+        df = pd.read_csv(file_path)
+    elif file_path.endswith(".xlsx"):
+        df = pd.read_excel(file_path)
+    else:
+        raise ValueError("Unsupported file format. Please upload a CSV or Excel file.")
+    
+    return df
+
+# Placeholder for dataset and model
+df = None
+model = None
+encoder = LabelEncoder()
+
+@app.post("/upload/")
+async def upload_file(file: UploadFile = File(...)):
+    """ Upload and process the dataset """
+    global df, model, encoder
+
+    file_path = os.path.join(UPLOAD_DIR, file.filename)
+    
+    # Save the uploaded file
+    with open(file_path, "wb") as buffer:
+        buffer.write(await file.read())
+
+    # Load the dataset
+    df = load_data(file_path)
+
+    # Encode categorical variables
+    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")
+
+    return {"message": f"File '{file.filename}' uploaded and 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. Please upload a dataset first."}
+
+    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}
+
+# Gradio UI
+def gradio_interface(case_problem):
+    if model is None:
+        return "Model not trained yet. Please upload a dataset first."
+
+    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}"
+
+# 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()