Create app.py

app.py

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+import streamlit as st
+import PyPDF2
+from huggingface_hub import InferenceClient
+
+# Initialize Hugging Face Inference client
+client = InferenceClient()
+
+# Models
+GRAMMAR_MODEL = "pszemraj/grammar-synthesis-base"
+SENTIMENT_MODEL = "distilbert-base-uncased-finetuned-sst-2-english"
+
+st.set_page_config(page_title="Resume & Cover Letter Analyzer", layout="wide")
+
+st.title("📄 Resume & Cover Letter Analyzer")
+st.write("Upload your resume or cover letter (PDF/Text) and get instant AI feedback.")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload PDF or paste text below", type=["pdf", "txt"])
+
+resume_text = ""
+
+if uploaded_file:
+    if uploaded_file.type == "application/pdf":
+        pdf_reader = PyPDF2.PdfReader(uploaded_file)
+        for page in pdf_reader.pages:
+            resume_text += page.extract_text() + "\n"
+    else:
+        resume_text = uploaded_file.read().decode("utf-8")
+
+# Text area input
+manual_text = st.text_area("Or paste your resume / cover letter text here:")
+
+if manual_text.strip():
+    resume_text = manual_text
+
+if resume_text:
+    st.subheader("Extracted Text")
+    st.write(resume_text[:1000] + "..." if len(resume_text) > 1000 else resume_text)
+
+    st.subheader("AI Feedback")
+
+    # Grammar correction
+    with st.spinner("Checking grammar..."):
+        response = client.text_generation(
+            model=GRAMMAR_MODEL,
+            prompt=resume_text[:500],  # shorten to avoid token limits
+            max_new_tokens=200
+        )
+        st.markdown("✅ **Grammar Suggestions:**")
+        st.write(response)
+
+    # Sentiment analysis
+    with st.spinner("Analyzing tone..."):
+        sentiment = client.text_classification(
+            model=SENTIMENT_MODEL,
+            inputs=resume_text[:500]
+        )
+        st.markdown("🎭 **Tone & Sentiment:**")
+        st.json(sentiment)
+else:
+    st.info("⬆️ Upload a file or paste text to get started.")