Update app.py

app.py

@@ -1,21 +1,16 @@
+import streamlit as st
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
-import random
 from textblob import TextBlob
 import pandas as pd
 import requests
 from io import StringIO
-import gradio as gr
 import speech_recognition as sr
 import json
-import re
-from sklearn.preprocessing import LabelEncoder
-from collections import Counter
-import numpy as np
+import random
+
+# --- Your Dummy Model and Helpers (same as before) ---
 
-# ---------------------------
-# Prepare dummy vocab, label encoder, and model (replace with real model if you want)
 vocab = {'<PAD>': 0, '<UNK>': 1, 'i': 2, 'am': 3, 'feeling': 4, 'sad': 5, 'happy': 6, 'angry': 7, 'love': 8, 'stressed': 9, 'anxious': 10}
 MAX_LEN = 16
 
@@ -27,11 +22,11 @@ class DummyLabelEncoder:
 
 le = DummyLabelEncoder()
 
-class DummyModel(nn.Module):
+class DummyModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        self.embedding = nn.Embedding(len(vocab), 8)
-        self.fc = nn.Linear(8, len(le.classes_))
+        self.embedding = torch.nn.Embedding(len(vocab), 8)
+        self.fc = torch.nn.Linear(8, len(le.classes_))
     def forward(self, x):
         x = self.embedding(x)
         x = x.mean(dim=1)
@@ -43,20 +38,14 @@ def preprocess_input(text):
     tokens = text.lower().split()
     encoded = [vocab.get(token, vocab['<UNK>']) for token in tokens]
     padded = encoded[:MAX_LEN] + [vocab['<PAD>']] * max(0, MAX_LEN - len(encoded))
-    return torch.tensor([padded], dtype=torch.long).to(next(model.parameters()).device)
+    return torch.tensor([padded], dtype=torch.long)
 
-# ---------------------------
-# Load solutions CSV from URL (you can update link if you want)
-file_id = "1yVJh_NVL4Y4YqEXGym47UCK5ZNZgVZYv"
+# Load CSV from Google Drive
+file_id = "1yVJh_NVL4Y4qEXGym47UCK5ZNZgVZYv"
 url = f"https://drive.google.com/uc?export=download&id={file_id}"
 response = requests.get(url)
 csv_text = response.text
-
-if csv_text.strip().startswith('<'):
-    raise Exception("ERROR: Google Drive link is not returning CSV! Check your sharing settings.")
-
 solutions_df = pd.read_csv(StringIO(csv_text), header=0, on_bad_lines='skip')
-
 used_solutions = {emotion: set() for emotion in solutions_df['emotion'].unique()}
 
 negative_words = [
@@ -119,7 +108,7 @@ def get_emotion(user_input):
         return "sadness"
     sentiment = get_sentiment(user_input)
     x = preprocess_input(user_input)
-    model.train()
+    model.eval()
     with torch.no_grad():
         probs = torch.stack([F.softmax(model(x), dim=1) for _ in range(5)])
         avg_probs = probs.mean(dim=0)
@@ -145,75 +134,60 @@ def audio_to_text(audio_file):
     except Exception:
         return ""
 
-# Chatbot conversation history and feedback state
-GLOBAL_CONVO_HISTORY = []
-USER_FEEDBACK_STATE = {}
+# --- Streamlit UI and Logic ---
+
+st.title("EmotiBot Connect (Streamlit)")
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# User input
+audio_input = st.file_uploader("🎤 Upload audio message (wav, mp3)", type=["wav", "mp3"])
+text_input = st.text_input("💬 Or type your message here")
 
-def emoti_chat(audio, text, history_json=""):
-    if text and text.strip():
-        user_input = text
-    elif audio is not None:
-        user_input = audio_to_text(audio)
+if st.button("Send"):
+
+    # Get text from audio or text input
+    if text_input.strip():
+        user_input = text_input
+    elif audio_input is not None:
+        user_input = audio_to_text(audio_input)
+        if not user_input:
+            st.warning("Sorry, could not recognize speech from audio.")
+            user_input = ""
     else:
         user_input = ""
-    if not user_input.strip():
-        return "Please say something or type your message.", json.dumps(GLOBAL_CONVO_HISTORY[-5:], indent=2), ""
-
-    user_input = correct_spelling(user_input)
-
-    # Exit phrases
-    exit_phrases = ["exit", "quit", "goodbye", "bye", "close"]
-    if user_input.lower().strip() in exit_phrases:
-        return "Take care! I’m here whenever you want to talk. 👋", json.dumps(GLOBAL_CONVO_HISTORY[-5:], indent=2), gr.update(visible=False)
-
-    # Feedback logic
-    user_id = "default_user"
-    state = USER_FEEDBACK_STATE.get(user_id, {"emotion": None, "pending": False})
-
-    if state["pending"]:
-        feedback = user_input.lower().strip()
-        GLOBAL_CONVO_HISTORY[-1]["feedback"] = feedback
-        if feedback == "no":
-            suggestion = get_unique_solution(state["emotion"])
-            reply = f"Here's another suggestion for you: {suggestion}\nDid this help? (yes/no/skip)"
-            USER_FEEDBACK_STATE[user_id]["pending"] = True
-            return reply, json.dumps(GLOBAL_CONVO_HISTORY[-5:], indent=2), ""
-        else:
-            USER_FEEDBACK_STATE[user_id] = {"emotion": None, "pending": False}
-            return "How can I help you further?", json.dumps(GLOBAL_CONVO_HISTORY[-5:], indent=2), ""
 
-    pred_emotion = get_emotion(user_input)
-    support = random.choice(responses.get(pred_emotion, responses["neutral"]))
-    try:
-        suggestion = get_unique_solution(pred_emotion)
-    except Exception:
-        suggestion = get_unique_solution("neutral")
-
-    reply = f"{support}\n\nHere's a suggestion for you: {suggestion}\nDid this help? (yes/no/skip)"
-    GLOBAL_CONVO_HISTORY.append({
-        "user_input": user_input,
-        "emotion": pred_emotion,
-        "bot_support": support,
-        "bot_suggestion": suggestion,
-        "feedback": ""
-    })
-    USER_FEEDBACK_STATE[user_id] = {"emotion": pred_emotion, "pending": True}
-    return reply, json.dumps(GLOBAL_CONVO_HISTORY[-5:], indent=2), ""
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=emoti_chat,
-    inputs=[
-        gr.Audio(type="filepath", label="🎤 Speak your message"),
-        gr.Textbox(lines=2, placeholder="Or type your message here...", label="💬 Type message"),
-        gr.Textbox(lines=1, value="", visible=False)  # Hidden history state
-    ],
-    outputs=[
-        gr.Textbox(label="EmotiBot Reply"),
-        gr.Textbox(label="Hidden", visible=False)
-    ],
-    title="EmotiBot Connect",
-    description="Talk to EmotiBot using your voice or by typing. Detects your emotion and offers dynamic support."
-)
+    if user_input.strip() == "":
+        st.warning("Please say something or type your message.")
+    else:
+        # Correct spelling
+        user_input_corrected = correct_spelling(user_input)
+
+        # Handle exit phrases
+        if user_input_corrected.lower() in ["exit", "quit", "goodbye", "bye", "close"]:
+            st.success("Take care! I’m here whenever you want to talk. 👋")
+        else:
+            # Get emotion and response
+            pred_emotion = get_emotion(user_input_corrected)
+            support = random.choice(responses.get(pred_emotion, responses["neutral"]))
+            try:
+                suggestion = get_unique_solution(pred_emotion)
+            except Exception:
+                suggestion = get_unique_solution("neutral")
+
+            reply = f"{support}\n\nHere's a suggestion for you: {suggestion}\nDid this help? (yes/no/skip)"
+
+            # Save in history
+            st.session_state.history.append({
+                "user": user_input_corrected,
+                "emotion": pred_emotion,
+                "bot": reply,
+                "feedback": ""
+            })
+
+# Show conversation history
+for chat in st.session_state.history[-5:]:
+    st.markdown(f"**You:** {chat['user']}")
+    st.markdown(f"**EmotiBot:** {chat['bot']}")
 
-iface.launch()