Update app.py

app.py

@@ -1,66 +1,86 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from PyPDF2 import PdfFileReader
+import requests
+from bs4 import BeautifulSoup
 
 # Initialize the Inference Client
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def ats_friendly_checker(resume):
-    # Implement the ATS-friendly checker logic here
-    # For demonstration, we'll return a mock score and feedback
-    score = 75  # Mock score
-    feedback = "Your resume needs more keywords related to the job."
+def extract_text_from_pdf(file):
+    reader = PdfFileReader(file)
+    text = ""
+    for page in range(reader.getNumPages()):
+        text += reader.getPage(page).extract_text()
+    return text
+
+def ats_friendly_checker(file):
+    resume_text = extract_text_from_pdf(file)
+    # Implement ATS-friendly checker logic using LLM
+    system_message = "Evaluate the following resume for ATS-friendliness and provide a score and feedback."
+    message = resume_text
+    response = client.chat_completion(
+        [{"role": "system", "content": system_message}, {"role": "user", "content": message}],
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.95
+    ).choices[0].message["content"]
+    
+    score = response.split("\n")[0].split(":")[-1].strip()
+    feedback = "\n".join(response.split("\n")[1:])
     return score, feedback
 
-def resume_match_checker(resume, job_description):
-    # Implement the resume match logic here
-    # For demonstration, we'll return a mock match score
-    match_score = 80  # Mock match score
+def scrape_job_description(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    job_description = soup.get_text(separator=" ", strip=True)
+    return job_description
+
+def resume_match_checker(file, job_url):
+    resume_text = extract_text_from_pdf(file)
+    job_description = scrape_job_description(job_url)
+    # Implement resume match checker logic using LLM
+    system_message = "Compare the following resume with the job description and provide a match score."
+    message = f"Resume: {resume_text}\n\nJob Description: {job_description}"
+    response = client.chat_completion(
+        [{"role": "system", "content": system_message}, {"role": "user", "content": message}],
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.95
+    ).choices[0].message["content"]
+
+    match_score = response.split(":")[-1].strip()
     return match_score
 
-def resume_quality_score(resume):
-    # Implement the resume quality scoring logic here
-    # For demonstration, we'll return a mock quality score
-    quality_score = 85  # Mock quality score
+def resume_quality_score(file):
+    resume_text = extract_text_from_pdf(file)
+    # Implement resume quality scoring logic using LLM
+    system_message = "Evaluate the following resume for overall quality and provide a score."
+    message = resume_text
+    response = client.chat_completion(
+        [{"role": "system", "content": system_message}, {"role": "user", "content": message}],
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.95
+    ).choices[0].message["content"]
+
+    quality_score = response.split(":")[-1].strip()
     return quality_score
 
 def text_to_overleaf(resume_text):
-    # Implement the conversion to Overleaf code here
-    # For demonstration, we'll return a mock Overleaf code
-    overleaf_code = "Mock Overleaf Code"
+    # Implement the conversion to Overleaf code using LLM
+    system_message = "Convert the following resume text to Overleaf code."
+    message = resume_text
+    response = client.chat_completion(
+        [{"role": "system", "content": system_message}, {"role": "user", "content": message}],
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.95
+    ).choices[0].message["content"]
+    
+    overleaf_code = response
     return overleaf_code
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
 # Define the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Resume Enhancement Tool\nEnhance your resume with the following features.")
@@ -75,9 +95,9 @@ with gr.Blocks() as demo:
     with gr.Tab("Resume Match Checker"):
         with gr.Row():
             resume = gr.File(label="Upload your Resume (PDF)")
-            job_description = gr.Textbox(label="Job Description")
+            job_url = gr.Textbox(label="Job Description URL")
             match_score = gr.Number(label="Match Score", interactive=False)
-        resume.upload(resume_match_checker, [resume, job_description], match_score)
+        gr.Button("Check Match").click(resume_match_checker, [resume, job_url], match_score)
     
     with gr.Tab("Resume Quality Score"):
         with gr.Row():