first commit

src/streamlit_app.py

@@ -2,10 +2,14 @@ import streamlit as st
 import requests
 import sqlite3
 import os
+import json
+
+# --- DATABASE PATH ---
+DB_PATH = "/tmp/writing_assistant.db"  # ✅ Hugging Face writable folder
 
 # --- DATABASE SETUP ---
 def init_db():
-    conn = sqlite3.connect("writing_assistant.db")
+    conn = sqlite3.connect(DB_PATH)
     c = conn.cursor()
     c.execute('''CREATE TABLE IF NOT EXISTS history (
                     id INTEGER PRIMARY KEY AUTOINCREMENT,
@@ -17,14 +21,14 @@ def init_db():
     conn.close()
 
 def save_feedback(user_text, feedback, tone):
-    conn = sqlite3.connect("writing_assistant.db")
+    conn = sqlite3.connect(DB_PATH)
     c = conn.cursor()
     c.execute("INSERT INTO history (text, feedback, tone) VALUES (?, ?, ?)", (user_text, feedback, tone))
     conn.commit()
     conn.close()
 
 def load_history():
-    conn = sqlite3.connect("writing_assistant.db")
+    conn = sqlite3.connect(DB_PATH)
     c = conn.cursor()
     c.execute("SELECT * FROM history ORDER BY id DESC LIMIT 10")
     data = c.fetchall()
@@ -34,7 +38,7 @@ def load_history():
 # --- API CONFIG ---
 st.set_page_config(page_title="AI Grammarly + Tone Analyzer", page_icon="✍️")
 st.title("✍️ AI Writing Assistant + Tone Analyzer")
-st.write("Improve your writing and see its tone (formal, casual, persuasive, emotional) using Groq LLaMA models.")
+st.write("Improve your writing and see its tone (formal, casual, persuasive, emotional, or neutral) using Groq LLaMA models.")
 
 # --- API KEY ---
 api_key = None
@@ -52,8 +56,10 @@ if not api_key:
 GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
 HEADERS = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
 
+# --- INIT DATABASE ---
 init_db()
 
+# --- USER INPUT ---
 user_input = st.text_area("📝 Enter your text:", height=200, placeholder="Type or paste your paragraph here...")
 
 # --- FUNCTION TO CALL GROQ ---
@@ -71,7 +77,7 @@ def analyze_text(text):
     Text: {text}
     """
     payload = {
-        "model": "llama-3.1-8b-instant",
+        "model": "llama-3.1-8b-instant",  # ✅ Active Groq model
         "messages": [
             {"role": "system", "content": "You are a professional writing assistant that improves text and detects tone."},
             {"role": "user", "content": prompt}
@@ -82,12 +88,9 @@ def analyze_text(text):
     response.raise_for_status()
     content = response.json()["choices"][0]["message"]["content"]
     try:
-        result = response.json()["choices"][0]["message"]["content"]
-        # Ensure JSON parsing
-        import json
         parsed = json.loads(content)
         return parsed
-    except:
+    except json.JSONDecodeError:
         return {"feedback": content, "tone": "Unknown"}
 
 # --- ANALYZE BUTTON ---