Update app.py

app.py

@@ -22,16 +22,51 @@ os.environ['HF_TOKEN'] = hk
 # accessing the llm
 # ------ accesssing the llm for geenral prompting -------------------
 llm_skeleton = HuggingFaceEndpoint(repo_id='meta-llama/Llama-3.2-3B-Instruct',
-                    provider = 'novita',
-                    temperature=0.7,
-                    max_new_tokens=150,
-                    task = 'conversational')
+                                   provider='novita',
+                                   temperature=0.7,
+                                   max_new_tokens=150,
+                                   task='conversational')
+llm = ChatHuggingFace(llm=llm_skeleton,
+                      repo_id='meta-llama/Llama-3.2-3B-Instruct',
+                      provider='novita',
+                      temperature=0.7,
+                      max_new_tokens=150,
+                      task='conversational')
 
+# App Layout
+st.title("📄 Resume & Job Description Extractor")
 
-# ------------- wrapping the llm to be a conversational model ------------------
-llm = ChatHuggingFace(llm=llm_skeleton,
-                    repo_id='meta-llama/Llama-3.2-3B-Instruct',
-                    provider = 'novita',
-                    temperature=0.7,
-                    max_new_tokens=150,
-                    task = 'conversational')
+# Upload Resume PDF
+resume_file = st.file_uploader("Upload Resume (PDF)", type=["pdf"])
+
+# Upload or Input Job Description
+jd_file = st.file_uploader("Upload Job Description (PDF or TXT)", type=["pdf", "txt"])
+jd_text = st.text_area("Or paste Job Description text here")
+
+
+
+
+if st.button("Extract Data"):
+    if resume_file:
+        # Extract text from resume
+        loader = UnstructuredPDFLoader(resume_file)
+        resume_text = loader.load()[0].page_content
+        
+        # LLM prompt for resume extraction
+        resume_prompt = f"Extract the following from the resume:\n1. Name\n2. Education\n3. Experience\n4. Skills\n5. Project Names and Results\n\nResume:\n{resume_text}"
+        resume_data = llm.invoke(resume_prompt)
+        st.subheader("Extracted Resume Data")
+        st.write(resume_data)
+
+    if jd_file or jd_text:
+        if jd_file:
+            loader = UnstructuredPDFLoader(jd_file)
+            jd_text_extracted = loader.load()[0].page_content
+        else:
+            jd_text_extracted = jd_text
+
+        # LLM prompt for JD extraction
+        jd_prompt = f"Extract the following from the job description:\n1. Job ID\n2. Company Name\n3. Role\n4. Experience Required\n5. Skills Required\n6. Education Required\n7. Location\n\nJob Description:\n{jd_text_extracted}"
+        jd_data = llm.invoke(jd_prompt)
+        st.subheader("Extracted Job Description Data")
+        st.write(jd_data)