app.py

import gradio as gr
from transformers import pipeline
import requests
import json
import pandas as pd  # Import pandas

# Initialize sentiment analysis pipeline
sentiment_analyzer = pipeline(
    "sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"
)


# Function to calculate scores via API call
def calculate_scores_from_logs(
    log_type_1,
    quality_score_1,
    delay_percentage_1,
    safety_compliance_1,
    feedback_1,
    log_type_2,
    quality_score_2,
    delay_percentage_2,
    safety_compliance_2,
    feedback_2,
    vendor_id,
    month,
):
    """
    Calculates performance scores by calling the Salesforce API.
    Args:
        log_type_1, log_type_2 (str): Type of log (Quality, Delay, Incident, Communication).
        quality_score_1, quality_score_2 (float): Quality score.
        delay_percentage_1, delay_percentage_2 (float): Delay percentage.
        safety_compliance_1, safety_compliance_2 (str): Safety compliance (True/False).
        feedback_1, feedback_2 (str): Feedback text.
        vendor_id (str):  Vendor ID.
        month (str): The month for which to calculate scores (YYYY-MM-DD).
    Returns:
        dict: A dictionary containing the calculated scores and alert flag, or an error message.
    """
    print("Entered calculate_scores_from_logs")  # Added logging
    print(f"Vendor ID: {vendor_id}, Month: {month}")
    print(
        f"Log 1: {log_type_1}, Quality: {quality_score_1}, Delay: {delay_percentage_1}, Safety: {safety_compliance_1}, Feedback: {feedback_1}"
    )
    print(
        f"Log 2: {log_type_2}, Quality: {quality_score_2}, Delay: {delay_percentage_2}, Safety: {safety_compliance_2}, Feedback: {feedback_2}"
    )

    logs = [
        {
            "log_type": log_type_1,
            "quality_score": float(quality_score_1)
            if log_type_1 == "Quality" and quality_score_1
            else None,
            "delay_percentage": float(delay_percentage_1)
            if log_type_1 == "Delay" and delay_percentage_1
            else None,
            "safety_compliance": safety_compliance_1 == "True"
            if log_type_1 == "Incident"
            else None,
            "feedback": feedback_1 if log_type_1 == "Communication" else "",
        },
        {
            "log_type": log_type_2,
            "quality_score": float(quality_score_2)
            if log_type_2 == "Quality" and quality_score_2
            else None,
            "delay_percentage": float(delay_percentage_2)
            if log_type_2 == "Delay" and delay_percentage_2
            else None,
            "safety_compliance": safety_compliance_2 == "True"
            if log_type_2 == "Incident"
            else None,
            "feedback": feedback_2 if log_type_2 == "Communication" else "",
        },
    ]
    payload = json.dumps(logs)
    headers = {"Content-Type": "application/json"}
    #  Replace with your Salesforce API endpoint
    salesforce_api_url = (
        f"https://your-salesforce-domain.com/services/apexrest/VendorScoreCalculator?vendorId={vendor_id}&month={month}"  # Replace
    )
    print(f"Salesforce API URL: {salesforce_api_url}") #Added Log

    try:
        response = requests.post(salesforce_api_url, headers=headers, data=payload)
        response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
        print(f"Salesforce API Response: {response.text}")  # Added logging
        return response.json()  # Return the JSON response from Salesforce
    except requests.exceptions.RequestException as e:
        error_message = f"Error calling Salesforce API: {e}"
        print(error_message)
        return {"error": error_message}  # Return a user-friendly error message
    except json.JSONDecodeError as e:
        error_message = f"Error decoding JSON response: {e}, Response Text: {response.text}"
        print(error_message)
        return {"error": error_message}

# Gradio Interface
iface = gr.Interface(
    fn=calculate_scores_from_logs,
    inputs=[
        gr.Dropdown(["Quality", "Delay", "Incident", "Communication"], label="Log Type 1"),
        gr.Number(label="Quality Score 1 (for Quality)"),
        gr.Number(label="Delay Percentage 1 (for Delay)"),
        gr.Radio(["True", "False"], label="Safety Compliance 1 (for Incident)"),
        gr.Textbox(label="Feedback 1 (for Communication)"),
        gr.Dropdown(["Quality", "Delay", "Incident", "Communication"], label="Log Type 2"),
        gr.Number(label="Quality Score 2 (for Quality)"),
        gr.Number(label="Delay Percentage 2 (for Delay)"),
        gr.Radio(["True", "False"], label="Safety Compliance 2 (for Incident)"),
        gr.Textbox(label="Feedback 2 (for Communication)"),
        gr.Textbox(label="Vendor ID", placeholder="Enter Vendor ID"),  # Added Vendor ID
        gr.Textbox(
            label="Month (YYYY-MM-DD)", placeholder="Enter Month (YYYY-MM-DD)"
        ),  # Added Month
    ],
    outputs=gr.Label(label="Calculated Scores"),
    title="Vendor Performance Score Calculator",
    description="Calculate vendor performance scores based on log data and Vendor ID/Month.",
)
# Run the Gradio interface
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)