Update app.py

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+import gradio as gr
 import requests
 import pandas as pd
 import json
@@ -6,14 +6,15 @@ import json
 # ----------------------------
 # CONFIG
 # ----------------------------
-st.set_page_config(page_title="Startup Candidate Dashboard", layout="wide")
-
 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"
 
 MODEL_ID = "HuggingFaceH4/zephyr-7b-beta"
 
-# **HF_API_TOKEN must be added in Hugging Face Spaces Secrets**
-HF_API_TOKEN = st.secrets["HF_API_TOKEN"]
+# Access Hugging Face secret in Spaces
+import os
+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.")
 
 # ----------------------------
 # CATEGORIES DEFINED BY JOB TITLES
@@ -61,24 +62,22 @@ def call_zephyr(prompt):
         timeout=60
     )
     if response.status_code != 200:
-        st.error(f"Zephyr API error: {response.text}")
-        return None
+        return f"Zephyr API error: {response.text}"
     result = response.json()
     if isinstance(result, dict) and "error" in result:
-        st.error(result["error"])
-        return None
+        return f"Zephyr API error: {result['error']}"
     return result[0].get("generated_text", "")
 
-def get_candidates_by_category(data, category_name, job_titles):
+def get_candidates_by_category(category_name, job_titles):
+    data = fetch_json(JSON_URL)
     candidates = []
     for person in data:
         work_exps = person.get("work_experiences", [])
         if len(work_exps) == 0:
-            continue  # skip no experience
+            continue
         if any("full stack developer" in exp.get("roleName","").lower() for exp in work_exps):
-            continue  # skip full stack
+            continue
 
-        # Prepare prompt: ask Zephyr if this candidate matches any of the predefined job titles
         prompt = f"""
 You are an HR assistant. Determine if the following candidate is suitable for the category '{category_name}'.
 The category is defined by the job titles: {job_titles}
@@ -87,35 +86,39 @@ Candidate JSON: {json.dumps(person)}
 
 Respond only 'Yes' or 'No'.
 """
-        try:
-            llm_response = call_zephyr(prompt)
-            if llm_response and "Yes" in llm_response:
-                candidates.append({
-                    "Name": person.get("name"),
-                    "Email": person.get("email"),
-                    "Phone": person.get("phone"),
-                    "Location": person.get("location"),
-                    "Roles": ", ".join([exp.get("roleName") for exp in work_exps]),
-                    "Skills": ", ".join(person.get("skills", [])),
-                    "Salary": person.get("annual_salary_expectation", {}).get("full-time", "N/A")
-                })
-        except Exception as e:
-            st.warning(f"Error processing {person.get('name')}: {e}")
+        llm_response = call_zephyr(prompt)
+        if llm_response and "Yes" in llm_response:
+            candidates.append({
+                "Name": person.get("name"),
+                "Email": person.get("email"),
+                "Phone": person.get("phone"),
+                "Location": person.get("location"),
+                "Roles": ", ".join([exp.get("roleName") for exp in work_exps]),
+                "Skills": ", ".join(person.get("skills", [])),
+                "Salary": person.get("annual_salary_expectation", {}).get("full-time", "N/A")
+            })
+    if len(candidates) == 0:
+        return f"No suitable candidates found for {category_name}."
     return pd.DataFrame(candidates)
 
 # ----------------------------
-# MAIN
+# GRADIO INTERFACE
 # ----------------------------
-st.title("🚀 Startup Candidate Dashboard - Zephyr LLM")
+def run_dashboard(category):
+    if category not in CATEGORIES:
+        return f"Category {category} not found."
+    df = get_candidates_by_category(category, CATEGORIES[category])
+    return df
+
+category_options = list(CATEGORIES.keys())
 
-st.markdown("Fetching candidate data from Notion...")
-data = fetch_json(JSON_URL)
-st.success(f"✅ Loaded {len(data)} submissions!")
+demo = gr.Interface(
+    fn=run_dashboard,
+    inputs=gr.Dropdown(category_options, label="Select Category"),
+    outputs=gr.Dataframe(label="Suitable Candidates"),
+    live=False,
+    title="Startup Candidate Dashboard - Zephyr-7B-Beta"
+)
 
-for category_name, job_titles in CATEGORIES.items():
-    st.subheader(f"{category_name} Suitable Candidates")
-    df = get_candidates_by_category(data, category_name, job_titles)
-    if df.empty:
-        st.write("No suitable candidates found.")
-    else:
-        st.dataframe(df)
+if __name__ == "__main__":
+    demo.launch()