Update model.py

model.py

@@ -1,30 +1,57 @@
-import os
-from dotenv import load_dotenv
-from transformers import pipeline, AutoTokenizer
+import csv
+import io
+import requests
+import json
+import html  # For escaping HTML characters
+from bs4 import BeautifulSoup
+from openai import OpenAI 
 
-load_dotenv()
+# Initialize OpenAI API with Nvidia's Llama 3.1 70b nemotron model
+client = OpenAI(
+    base_url="https://integrate.api.nvidia.com/v1",
+    api_key="KEY"
+)
 
-# Load a larger Hugging Face model
-model_name = "EleutherAI/gpt-neo-2.7B"
-generator = pipeline("text-generation", model=model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+def clean_text_output(text):
+    """
+    Cleans the output to handle HTML characters and unwanted tags.
+    """
+    text = html.unescape(text)  # Unescape HTML entities
+    soup = BeautifulSoup(text, 'html.parser')  # Use BeautifulSoup to handle HTML tags
+    cleaned_text = soup.get_text(separator="\n").strip()  # Remove tags and handle newlines
+    return cleaned_text
 
-def modelFeedback(ats_score, resume_data):
+def modelFeedback(ats_score, resume_data, job_description):
     input_prompt = f"""
     You are now an ATS Score analyzer and given ATS Score is {int(ats_score * 100)}%. 
     Your task is to provide feedback to the user based on the ATS score.
     Print ATS score first. Mention where the resume is good and where the resume lacks. 
-    Talk about each section of the user's resume and talk about good and bad points of it. 
+    Show list of missing skills and suggest improvements. 
+    Show list of weak action verbs and suggest improvements.
+    Show weaker sentences and suggest improvements.
+    Talk about each section of the user's resume and discuss good and bad points of it only if it has any. 
     Resume Data: {resume_data}
+    Job Description: {job_description}
     """
-    
-    # Tokenize the input to check its length
-    input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
-    input_length = input_ids.shape[1]
-    
-    print(f"Input length: {input_length}")
 
-    # Generate response with max_new_tokens instead of max_length
-    response = generator(input_prompt, max_new_tokens=150, num_return_sequences=1)
+    try:
+        # Generate response using the OpenAI API
+        response = client.chat.completions.create(
+            model="nvidia/llama-3.1-nemotron-70b-instruct",  # Using Llama 3.1 70b
+            messages=[
+                {"role": "user", "content": input_prompt}
+            ],
+            temperature=0.03,  # Lowering temperature for precise output
+            top_p=0.7,  # Prioritize high-probability tokens
+            max_tokens=700,  # Allow longer content
+        )
+
+        # Extract and clean the response
+        feedback_text = response.choices[0].message.content.strip()  # Corrected line
+        cleaned_feedback = clean_text_output(feedback_text)
+
+        return cleaned_feedback
 
-    return response[0]['generated_text']  
+    except requests.exceptions.RequestException as e:
+        print(f"API request failed: {str(e)}")
+        return "Error: Unable to generate feedback."