Update app.py

app.py

@@ -1,38 +1,49 @@
 import streamlit as st
 import os
 import tempfile
-import numpy as np
-import easyocr
-
-# Only needed if you're doing any OCR or OpenCV processing
-# import cv2
-
+import docx2txt
+from pdfminer.high_level import extract_text
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
 
-# Set page config
+# Set your Hugging Face token via environment variable or secret management
+os.environ["HUGGINGFACEHUB_API_KEY"] = os.getenv("HF")
+os.environ["HF_TOKEN"] = os.getenv("HF")
+
+# UI Configuration
 st.set_page_config(page_title="Resume Validator", layout="centered", page_icon="📄")
-st.title("📄 AI Resume Validator")
-st.write("Upload your resume and get instant feedback on its quality and suggestions to improve.")
 
-# File uploader
-uploaded_file = st.file_uploader("Upload Resume (PDF or DOCX)", type=["pdf", "docx"])
+st.markdown("""
+    <h1 style='text-align: center;'>📄 AI Resume Validator</h1>
+    <p style='text-align: center;'>Upload your resume and receive instant feedback with suggestions for improvement</p>
+    <br>
+""", unsafe_allow_html=True)
+
+# File upload
+uploaded_file = st.file_uploader("📤 Upload Resume (PDF or DOCX)", type=["pdf", "docx"])
+
+resume_text = ""
 
-# Only run logic if a file is uploaded
 if uploaded_file:
-    # Extract text based on file type
+    # Save the uploaded file temporarily
+    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[-1]) as tmp_file:
+        tmp_file.write(uploaded_file.read())
+        temp_path = tmp_file.name
+
+    # Extract text
     if uploaded_file.name.endswith(".pdf"):
-        from pdfminer.high_level import extract_text
-        resume_text = extract_text(uploaded_file)
+        resume_text = extract_text(temp_path)
     else:
-        import docx2txt
-        resume_text = docx2txt.process(uploaded_file)
+        resume_text = docx2txt.process(temp_path)
+
+    # Clean up temp file
+    os.remove(temp_path)
 
     st.markdown("### 📃 Extracted Resume Text")
     st.code(resume_text)
 
-    # Prompt template for evaluation
+    # Prompt template
     template = """
     You are an expert HR recruiter.
 
@@ -51,23 +62,30 @@ if uploaded_file:
     - Weaknesses
     - Actionable suggestions to improve
     """
+
     prompt = PromptTemplate(input_variables=["resume_text"], template=template)
 
-    # Initialize the model
+    # LLM Configuration
     llm = HuggingFaceEndpoint(
         repo_id="mistralai/Mistral-7B-Instruct-v0.3",
         temperature=0.5,
-        max_new_tokens=300,
+        max_new_tokens=10,
         task="conversational"
     )
+
     model = ChatHuggingFace(llm=llm)
+
     chain = LLMChain(llm=model, prompt=prompt)
 
-    # Analyze resume on button click
     if st.button("✅ Validate Resume"):
         with st.spinner("Analyzing your resume..."):
-            result = chain.run(resume_text=resume_text)
-        st.markdown("### 📊 Feedback")
-        st.success(result)
+            try:
+                result = chain.run(resume_text=resume_text)
+                st.success("✅ Resume Analysis Completed")
+                st.markdown("### 📊 Feedback")
+                st.write(result)
+            except Exception as e:
+                st.error(f"⚠️ An error occurred: {e}")
+
 else:
-    st.info("Please upload a resume file (PDF or DOCX) to start the validation.")
+    st.markdown("<center><i>Please upload your resume to start validation.</i></center>", unsafe_allow_html=True)