Update app.py

app.py

@@ -2,11 +2,9 @@ import os
 import json
 import random
 import datetime
-from flask import Flask, request, jsonify
 from transformers import pipeline
 from geopy.geocoders import Nominatim
-
-app = Flask(__name__)
+import gradio as gr
 
 # ------------------------------
 # 💬 Emotion Analyzer Pipeline
@@ -56,7 +54,6 @@ def detect_crisis(text):
     ]
     return any(signal in text.lower() for signal in crisis_signals)
 
-# Geo-based crisis helpline lookup
 def get_geo_helpline(location_query="world"):
     helplines = {
         "india": "AASRA Helpline: 91-9820466726 or 91-22-27546669",
@@ -102,16 +99,19 @@ def enrich_response(reply_text):
     return reply_text
 
 # ------------------------------
-# 🧠 Generate AI Response (Simulated)
+# 🧠 Generate AI Response
 # ------------------------------
-def generate_supportive_reply(user_input, user_name, user_age):
+def generate_supportive_reply(user_input, user_name="Friend", user_age="20"):
     emotions = emotion_analyzer(user_input)[0]
     dominant_emotion = max(emotions, key=lambda x: x["score"])["label"].lower()
 
     # Crisis response
     if detect_crisis(user_input):
-        geo = Nominatim(user_agent="support_bot").geocode("your location") or None
-        location = geo.address if geo else "world"
+        try:
+            geo = Nominatim(user_agent="support_bot").geocode("your location") or None
+            location = geo.address if geo else "world"
+        except:
+            location = "world"
         helpline = get_geo_helpline(location)
         return (
             f"{user_name}, I’m really sorry that you’re feeling this way. "
@@ -134,47 +134,41 @@ def generate_supportive_reply(user_input, user_name, user_age):
     return enrich_response(base_reply)
 
 # ------------------------------
-# 🌐 Flask Routes
+# 🌐 Gradio Interface
 # ------------------------------
-@app.route('/')
-def home():
-    return jsonify({"message": "💛 Emotional Support Chatbot API is running!"})
-
-@app.route('/chat', methods=['POST'])
-def chat():
+def chatbot_interface(user_message, user_name="Friend", user_age="20"):
     global memory
-    data = request.get_json()
-    user_input = data.get('message', '').strip()
-    user_name = data.get('name', 'Friend')
-    user_age = data.get('age', '20')
 
-    if not user_input:
-        return jsonify({'reply': "I didn’t quite catch that — could you say that again?"})
+    if not user_message.strip():
+        return "I didn’t quite catch that — could you say that again?"
 
-    # Store memory
+    # Memory system
     if user_name not in memory:
         memory[user_name] = {"last_chat": datetime.datetime.now().isoformat(), "mood": "neutral"}
     else:
         memory[user_name]["last_chat"] = datetime.datetime.now().isoformat()
     save_memory(memory)
 
-    # Detect if the user was sad before
     last_mood = memory[user_name].get("mood", "neutral")
-    if last_mood == "sadness":
-        pretext = f"{user_name}, last time you were feeling a bit down. How are you feeling today? 💛\n"
-    else:
-        pretext = ""
+    pretext = f"{user_name}, last time you were feeling a bit down. How are you feeling today? 💛\n" if last_mood == "sadness" else ""
 
-    # Generate response
-    reply = generate_supportive_reply(user_input, user_name, user_age)
-    reply = pretext + reply
+    reply = generate_supportive_reply(user_message, user_name, user_age)
 
     # Update mood
-    mood = emotion_analyzer(user_input)[0]
+    mood = emotion_analyzer(user_message)[0]
     memory[user_name]["mood"] = max(mood, key=lambda x: x["score"])["label"].lower()
     save_memory(memory)
 
-    return jsonify({'reply': reply})
+    return pretext + reply
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 💛 Emotional Support Chatbot")
+    name_input = gr.Textbox(label="Your Name", placeholder="Enter your name...")
+    age_input = gr.Textbox(label="Your Age", placeholder="Enter your age...")
+    message_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=3)
+    output_chat = gr.Textbox(label="Chatbot Response", interactive=False)
+
+    message_input.submit(chatbot_interface, [message_input, name_input, age_input], output_chat)
 
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+demo.launch()