Update app.py

app.py

@@ -1,17 +1,16 @@
 import gradio as gr
-import requests
 import pandas as pd
 import json
-import io
+import os
+import requests
 
 # ----------------------------
 # CONFIG
 # ----------------------------
-JSON_URL = "https://file.notion.so/f/f/f86ed84d-b33c-4dfb-b0e0-97c5661516a3/3ed586a1-78e7-46af-9cf1-0961f95b5109/form-submissions-1.json?table=block&id=18a5392c-c93e-8054-b617-eb2a1a213d6c&spaceId=f86ed84d-b33c-4dfb-b0e0-97c5661516a3&expirationTimestamp=1758932214635&signature=sq1Jw2w3WoKIVMc8X078LO4SbfViD9ppdO0VXZ72Nro&downloadName=form-submissions.json"
-
+JSON_FILE = "form-submissions-1.json"  # local JSON file in the Space
 MODEL_ID = "HuggingFaceH4/zephyr-7b-beta"
 
-import os
+# Hugging Face token from Space Secrets
 HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
 if not HF_API_TOKEN:
     raise ValueError("HF_API_TOKEN not found in environment. Add it in Space Secrets.")
@@ -44,10 +43,9 @@ CATEGORIES = {
 # ----------------------------
 # HELPER FUNCTIONS
 # ----------------------------
-def fetch_json(url):
-    resp = requests.get(url)
-    resp.raise_for_status()
-    return resp.json()
+def fetch_json_local(file_path):
+    with open(file_path, "r", encoding="utf-8") as f:
+        return json.load(f)
 
 def call_zephyr(prompt):
     headers = {
@@ -69,7 +67,7 @@ def call_zephyr(prompt):
     return result[0].get("generated_text", "")
 
 def get_candidates_by_category(category_name, job_titles):
-    data = fetch_json(JSON_URL)
+    data = fetch_json_local(JSON_FILE)
     candidates = []
     for person in data:
         work_exps = person.get("work_experiences", [])
@@ -99,35 +97,31 @@ Respond only 'Yes' or 'No'.
             })
 
     if len(candidates) == 0:
-        return pd.DataFrame(), None
+        return pd.DataFrame()
 
     df = pd.DataFrame(candidates)
-    # Convert to CSV as bytes and provide filename for Gradio
-    csv_buffer = io.BytesIO()
-    df.to_csv(csv_buffer, index=False)
-    csv_buffer.seek(0)
-    return df, ("candidates.csv", csv_buffer.read())
+    return df
 
 # ----------------------------
 # GRADIO INTERFACE
 # ----------------------------
 def run_dashboard(category):
     if category not in CATEGORIES:
-        return f"Category {category} not found.", None
-    df, csv_file = get_candidates_by_category(category, CATEGORIES[category])
+        return pd.DataFrame()
+    df = get_candidates_by_category(category, CATEGORIES[category])
     if df.empty:
-        return f"No suitable candidates found for {category}.", None
-    return df, csv_file
+        return pd.DataFrame()
+    return df
 
 category_options = list(CATEGORIES.keys())
 
 demo = gr.Interface(
     fn=run_dashboard,
     inputs=gr.Dropdown(category_options, label="Select Category"),
-    outputs=[gr.Dataframe(label="Suitable Candidates"), gr.File(label="Download CSV")],
+    outputs=gr.Dataframe(label="Suitable Candidates"),
     live=False,
     title="Startup Candidate Dashboard - Zephyr-7B-Beta",
-    description="Select a category to view suitable candidates. You can also download the results as CSV."
+    description="Select a category to view suitable candidates."
 )
 
 if __name__ == "__main__":