Update app.py (#1)

- Update app.py (6b6eb54081619bdccba0f01ce657214fc719a357)


Co-authored-by: Umair Mir <[email protected]>

app.py

@@ -1,64 +1,81 @@
+import os
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer, util
 import gradio as gr
-from huggingface_hub import InferenceClient
+from huggingface_hub import login
+import requests
+from huggingface_hub import HfApi
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load environment variables
+import os
+from huggingface_hub import HfApi
 
+hf_token = os.getenv("hf_token")
+api = HfApi(token=hf_token)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Load embedding model
+embed_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# API call function to Zephyr model
+def call_zephyr_api(prompt):
+    url = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
+    headers = {
+        "Authorization": f"Bearer {hf_token}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 512,
+            "temperature": 0.7
+        }
+    }
+    try:
+        response = requests.post(url, headers=headers, json=payload)
+        response.raise_for_status()
+        return response.json()[0]["generated_text"]
+    except Exception as e:
+        return f"⚠️ Error generating suggestions: {e}"
 
-    messages.append({"role": "user", "content": message})
+# Resume matcher function
+def resume_matcher_and_advisor(resume_text, job_desc):
+    embeddings = embed_model.encode([resume_text, job_desc], convert_to_tensor=True)
+    similarity_score = util.pytorch_cos_sim(embeddings[0], embeddings[1]).item()
+    res_score = round(similarity_score * 100, 2)
 
-    response = ""
+    if res_score > 85:
+        suggestions = "✅ This resume is a good fit for the job description!"
+    else:
+        prompt = f"""<|system|>
+You are a professional resume reviewer.
+<|user|>
+Compare the following resume with the job description and give bullet-point suggestions to improve the resume so that it better fits the job requirements.
+- Be critical and constructive.
+- Do NOT assume the resume matches the job.
+- DO NOT rewrite the resume or job.
+- DO NOT hallucinate skills.
+Job Description:
+{job_desc}
+Resume:
+{resume_text}
+</s>"""
+        suggestions = call_zephyr_api(prompt)
 
-    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
+    return res_score, suggestions
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Launch Gradio interface
+iface = gr.Interface(
+    fn=resume_matcher_and_advisor,
+    inputs=[
+        gr.Textbox(lines=15, label="Paste Resume Text"),
+        gr.Textbox(lines=15, label="Paste Job Description")
     ],
+    outputs=[
+        gr.Number(label="Similarity Score (%)"),
+        gr.Textbox(label="Improvement Suggestions")
+    ],
+    title="🧠 Resume Matcher & Advisor",
+    description="Paste a resume and job description. Get a match score and improvement suggestions."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+iface.launch(share=True)