Update src/app.py

src/app.py

@@ -1,58 +1,59 @@
-import streamlit as st
-from dotenv import load_dotenv
-import os
-from utils import *
-import uuid
-
-load_dotenv()  # ✅ Load .env at the very start
-
-if 'unique_id' not in st.session_state:
-    st.session_state['unique_id'] = ''
-
-
-def main():
-    st.set_page_config(page_title="Resume Screening Assistance")
-    st.title("HR - Resume Screening Assistance...💁 ")
-    st.subheader("I can help you in resume screening process")
-
-    job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...", key="1")
-    document_count = st.text_input("No.of 'RESUMES' to return", key="2")
-    pdf = st.file_uploader("Upload resumes here, only PDF files allowed", type=["pdf"], accept_multiple_files=True)
-
-    submit = st.button("Help me with the analysis")
-
-    if submit:
-        if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
-            st.error(
-                "ERROR: HUGGINGFACEHUB_API_TOKEN not found. Please set it in your .env file or Hugging Face Space Secrets.")
-            return
-
-        with st.spinner('Wait for it...'):
-            st.session_state['unique_id'] = uuid.uuid4().hex
-            final_docs_list = create_docs(pdf, st.session_state['unique_id'])
-
-            st.write("*Resumes uploaded* :" + str(len(final_docs_list)))
-
-            embeddings = create_embeddings_load_data()
-
-            # --- CHROMADB REPLACEMENT ---
-            push_to_chroma(st.session_state['unique_id'], embeddings, final_docs_list)
-            relavant_docs = similar_docs(job_description, document_count, st.session_state['unique_id'])
-            # ---------------------------
-
-            st.write(":heavy_minus_sign:" * 30)
-
-            for item in range(len(relavant_docs)):
-                st.subheader("👉 " + str(item + 1))
-                st.write("**File** : " + relavant_docs[item][0].metadata['name'])
-
-                with st.expander('Show me 👀'):
-                    st.info("**Match Score** : " + str(relavant_docs[item][1]))
-                    summary = get_summary(relavant_docs[item][0])
-                    st.write("**Summary** : " + summary)
-
-        st.success("Hope I was able to save your time❤️")
-
-
-if __name__ == '__main__':
+import streamlit as st
+import os
+import uuid
+from utils import *
+
+if 'unique_id' not in st.session_state:
+    st.session_state['unique_id'] = ''
+
+def main():
+    st.set_page_config(page_title="Resume Screening Assistance")
+    st.title("HR - Resume Screening Assistance...💁 ")
+    st.subheader("I can help you in resume screening process")
+
+    job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...", key="1")
+    document_count = st.text_input("No.of 'RESUMES' to return", key="2")
+    pdf = st.file_uploader("Upload resumes here, only PDF files allowed", type=["pdf"], accept_multiple_files=True)
+
+    submit = st.button("Help me with the analysis")
+
+    if submit:
+        API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+        if not API_TOKEN:
+            st.error("ERROR: HUGGINGFACEHUB_API_TOKEN not found. Set it in Hugging Face Space Secrets.")
+            return
+
+        try:
+            document_count = int(document_count)
+        except ValueError:
+            document_count = 5
+
+        from huggingface_hub import InferenceClient
+        hf_client = InferenceClient(token=API_TOKEN)
+
+        with st.spinner('Wait for it...'):
+            st.session_state['unique_id'] = uuid.uuid4().hex
+            final_docs_list = create_docs(pdf, st.session_state['unique_id'])
+
+            st.write("*Resumes uploaded* :" + str(len(final_docs_list)))
+
+            embeddings = create_embeddings_load_data()
+
+            push_to_chroma(st.session_state['unique_id'], embeddings, final_docs_list)
+            relavant_docs = similar_docs(job_description, document_count, st.session_state['unique_id'])
+
+            st.write(":heavy_minus_sign:" * 30)
+
+            for item in range(len(relavant_docs)):
+                st.subheader("👉 " + str(item + 1))
+                st.write("**File** : " + relavant_docs[item][0].metadata['name'])
+
+                with st.expander('Show me 👀'):
+                    st.info("**Match Score** : " + str(relavant_docs[item][1]))
+                    summary = get_summary(relavant_docs[item][0], hf_client)
+                    st.write("**Summary** : " + summary)
+
+        st.success("Hope I was able to save your time❤️")
+
+if __name__ == '__main__':
     main()