Update app.py

app.py

@@ -10,9 +10,8 @@ import gradio as gr
 import speech_recognition as sr
 import json
 
-# Dummy vocab and label encoder
-vocab = {'<PAD>': 0, '<UNK>': 1, 'i': 2, 'am': 3, 'feeling': 4, 'sad': 5, 'happy': 6,
-         'angry': 7, 'love': 8, 'stressed': 9, 'anxious': 10}
+# ----- Dummy Model and vocab -----
+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
 
 class DummyLabelEncoder:
@@ -34,46 +33,73 @@ class DummyModel(nn.Module):
         return self.fc(x)
 
 model = DummyModel()
-model.eval()
 
 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)
+    return torch.tensor([padded], dtype=torch.long).to(next(model.parameters()).device)
 
-# Load solutions CSV from Google Drive
-file_id = "1yVJh_NVL4Y4YqEXGym47UCK5ZNZgVZYv"  # replace with your CSV file ID
+# ----- Load CSV from Google Drive -----
+file_id = "1yVJh_NVL4Y4YqEXGym47UCK5ZNZgVZYv"
 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 sharing settings.")
+    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 = ["not", "bad", "sad", "anxious", "anxiety", "depressed", "upset", "shit", "stress",
-                 "worried", "unwell", "struggling", "low", "down", "terrible", "awful",
-                 "nervous", "panic", "afraid", "scared", "tense", "overwhelmed", "fear", "uneasy"]
+# ----- Data and responses -----
+negative_words = [
+    "not", "bad", "sad", "anxious", "anxiety", "depressed", "upset", "shit", "stress",
+    "worried", "unwell", "struggling", "low", "down", "terrible", "awful",
+    "nervous", "panic", "afraid", "scared", "tense", "overwhelmed", "fear", "uneasy"
+]
 
 responses = {
-    "sadness": ["It’s okay to feel down sometimes. I’m here to support you.",
-                "I'm really sorry you're going through this. Want to talk more about it?",
-                "You're not alone — I’m here for you."],
-    "anger": ["That must have been frustrating. Want to vent about it?",
-              "It's okay to feel this way. I'm listening.",
-              "Would it help to talk through it?"],
-    "love": ["That’s beautiful to hear! What made you feel that way?",
-             "It’s amazing to experience moments like that.",
-             "Sounds like something truly meaningful."],
-    "happiness": ["That's awesome! What’s bringing you joy today?",
-                  "I love hearing good news. 😊",
-                  "Yay! Want to share more about it?"],
-    "neutral": ["Got it. I’m here if you want to dive deeper.",
-                "Thanks for sharing that. Tell me more if you’d like.",
-                "I’m listening. How else can I support you?"]
+    "sadness": [
+        "It’s okay to feel down sometimes. I’m here to support you.",
+        "I'm really sorry you're going through this. Want to talk more about it?",
+        "You're not alone — I’m here for you."
+    ],
+    "anger": [
+        "That must have been frustrating. Want to vent about it?",
+        "It's okay to feel this way. I'm listening.",
+        "Would it help to talk through it?"
+    ],
+    "love": [
+        "That’s beautiful to hear! What made you feel that way?",
+        "It’s amazing to experience moments like that.",
+        "Sounds like something truly meaningful."
+    ],
+    "happiness": [
+        "That's awesome! What’s bringing you joy today?",
+        "I love hearing good news. 😊",
+        "Yay! Want to share more about it?"
+    ],
+    "neutral": [
+        "Got it. I’m here if you want to dive deeper.",
+        "Thanks for sharing that. Tell me more if you’d like.",
+        "I’m listening. How else can I support you?"
+    ]
 }
 
+# --- Helper functions ---
+
+def correct_spelling(text):
+    return str(TextBlob(text).correct())
+
+def get_sentiment(text):
+    return TextBlob(text).sentiment.polarity
+
+def is_negative_input(text):
+    text_lower = text.lower()
+    return any(word in text_lower for word in negative_words)
+
 def get_unique_solution(emotion):
     available = solutions_df[solutions_df['emotion'] == emotion]
     unused = available[~available['solution'].isin(used_solutions[emotion])]
@@ -84,24 +110,23 @@ def get_unique_solution(emotion):
     used_solutions[emotion].add(solution_row['solution'])
     return solution_row['solution']
 
-def correct_spelling(text):
-    return str(TextBlob(text).correct())
-
-def is_negative_input(text):
-    text_lower = text.lower()
-    return any(word in text_lower for word in negative_words)
-
 def get_emotion(user_input):
     if is_negative_input(user_input):
         return "sadness"
+    sentiment = get_sentiment(user_input)
     x = preprocess_input(user_input)
+    model.train()
     with torch.no_grad():
-        logits = model(x)
-        probs = F.softmax(logits, dim=1)
-        prob, idx = torch.max(probs, dim=1)
+        probs = torch.stack([F.softmax(model(x), dim=1) for _ in range(5)])
+        avg_probs = probs.mean(dim=0)
+        prob, idx = torch.max(avg_probs, dim=1)
     pred_emotion = le.classes_[idx.item()]
     if prob.item() < 0.6:
         return "neutral"
+    if sentiment < -0.25 and pred_emotion == "happiness":
+        return "sadness"
+    if sentiment > 0.25 and pred_emotion == "sadness":
+        return "happiness"
     return pred_emotion
 
 def audio_to_text(audio_file):
@@ -111,10 +136,12 @@ def audio_to_text(audio_file):
     with sr.AudioFile(audio_file) as source:
         audio = recog.record(source)
     try:
-        return recog.recognize_google(audio)
+        text = recog.recognize_google(audio)
+        return text
     except Exception:
         return ""
 
+# ----- Chat function -----
 GLOBAL_CONVO_HISTORY = []
 USER_FEEDBACK_STATE = {}
 
@@ -125,13 +152,13 @@ def emoti_chat(audio, text, history_json=""):
         user_input = audio_to_text(audio)
     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)
 
-    if user_input.lower().strip() in ["exit", "quit", "goodbye", "bye", "close"]:
+    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)
 
     user_id = "default_user"
@@ -157,7 +184,6 @@ def emoti_chat(audio, text, history_json=""):
         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,
@@ -168,19 +194,21 @@ def emoti_chat(audio, text, history_json=""):
     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)
+        gr.Textbox(lines=1, value="", visible=False)  # hidden, history state
     ],
     outputs=[
         gr.Textbox(label="EmotiBot Reply"),
-        gr.Textbox(label="Conversation History (JSON)", visible=False)
+        gr.Textbox(label="Hidden", visible=False)
     ],
     title="EmotiBot Connect",
-    description="Talk to EmotiBot using your voice or by typing. Detects your emotion, gives suggestions, and keeps history!"
+    description="Talk to EmotiBot using your voice or by typing. Detects your emotion, gives dynamic suggestions, remembers your feedback, and keeps a conversation history! Type 'exit' to leave."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch(debug=True)