Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,103 +1,58 @@
 import streamlit as st
-from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline
-import torch
-import re
+from deep_translator import GoogleTranslator
+from transformers import pipeline
 
-# ==========================================================
-# 1️⃣ LOAD MODEL
-# ==========================================================
 @st.cache_resource
-def load_model():
-    model_path = "zaid002/finetunedmodel"  # Your Hugging Face repo path
-    tokenizer = GPT2Tokenizer.from_pretrained(model_path)
-    model = GPT2LMHeadModel.from_pretrained(model_path)
-    device = 0 if torch.cuda.is_available() else -1
-    text_gen = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
-    return tokenizer, text_gen
+def load_translator():
+    def translate_to_english(text, src_lang='auto'):
+        """Translate any input text to English."""
+        try:
+            translated = GoogleTranslator(source=src_lang, target='en').translate(text)
+            return translated
+        except Exception as e:
+            return f"Translation error: {e}"
+
+    def translate_from_english(text, target_lang):
+        """Translate English response back to user language."""
+        try:
+            translated = GoogleTranslator(source='en', target=target_lang).translate(text)
+            return translated
+        except Exception as e:
+            return f"Translation error: {e}"
+    return translate_to_english, translate_from_english
 
-tokenizer, chatbot = load_model()
-
-# ==========================================================
-# 2️⃣ TRANSLATION DICTIONARY
-# ==========================================================
-def translate_response(text, lang):
-    translations = {
-        "en": text,
-        "hi": "यह रहा आपका उत्तर हिंदी में: " + text,
-        "ta": "உங்கள் பதில் தமிழில்: " + text,
-        "te": "మీ సమాధానం తెలుగులో: " + text,
-        "fr": "Votre réponse en français : " + text,
-        "es": "Su respuesta en español: " + text,
-    }
-    return translations.get(lang, text)
-
-# ==========================================================
-# 3️⃣ CHAT FUNCTION
-# ==========================================================
-def multilingual_chat(user_input, lang="en"):
-    user_input_lower = user_input.lower()
-
-    if re.search(r"\bguvi\b", user_input_lower):
-        input_text = f"Question: {user_input}\nAnswer:"
-        raw_response = chatbot(
-            input_text,
-            max_length=150,
-            num_return_sequences=1,
-            pad_token_id=tokenizer.eos_token_id,
-            do_sample=True,
-            top_k=50,
-            top_p=0.9,
-            temperature=0.7
-        )
-        model_output = raw_response[0]["generated_text"].replace(input_text, "").strip()
-        if len(model_output) > 400:
-            model_output = model_output[:400]
-        response = model_output
-    else:
-        response = "⚠️ I can only answer questions related to GUVI."
-
-    return translate_response(response, lang)
+@st.cache_resource
+def load_chatbot_model():
+    # Use a small multilingual model (or fine-tuned one from Hugging Face)
+    chatbot = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
+    def get_chatbot_response(prompt):
+        """Generate chatbot reply using a transformer model."""
+        result = chatbot(prompt, max_length=120, num_return_sequences=1)
+        return result[0]['generated_text']
+    return get_chatbot_response
 
-# ==========================================================
-# 4️⃣ STREAMLIT UI
-# ==========================================================
-st.set_page_config(page_title="GUVI-only Chatbot", page_icon="🤖", layout="centered")
+translate_to_english, translate_from_english = load_translator()
+get_chatbot_response = load_chatbot_model()
 
-st.markdown(
-    """
-    <h1 style='text-align:center; font-family:Calibri;'>🤖 GUVI-only LLM Chatbot</h1>
-    <p style='text-align:center; font-size:18px;'>Ask me anything about GUVI (courses, fees, placements, etc.)</p>
-    """,
-    unsafe_allow_html=True
-)
+st.set_page_config(page_title="GUVI Multilingual Chatbot", layout="centered")
+st.title("🤖 GUVI Multilingual Chatbot")
+st.write("Chat with AI in your own language!")
 
-# Keep conversation in session_state
-if "history" not in st.session_state:
-    st.session_state.history = []
+# User selects language
+language = st.selectbox("Select your language:",
+                        ["en", "ta", "hi", "ml", "te", "bn", "fr", "es"])
 
-# User input and language selection
-col1, col2 = st.columns([3, 1])
-with col1:
-    user_input = st.text_input("💬 Ask about GUVI...")
-with col2:
-    lang = st.selectbox("🌐 Language", ["en", "hi", "ta", "te", "fr", "es"], index=0)
+user_input = st.text_input("You:", "")
 
-# Button row
-colA, colB = st.columns([1, 1])
-with colA:
-    send = st.button("🚀 Send")
-with colB:
-    clear = st.button("🗑️ Clear Chat")
+if st.button("Send"):
+    if user_input:
+        # Step 1: Translate to English
+        english_text = translate_to_english(user_input, src_lang=language)
 
-if send and user_input.strip():
-    reply = multilingual_chat(user_input, lang)
-    st.session_state.history.append((user_input, reply))
+        # Step 2: Get GPT response
+        english_reply = get_chatbot_response(english_text)
 
-if clear:
-    st.session_state.history = []
+        # Step 3: Translate back
+        final_reply = translate_from_english(english_reply, target_lang=language)
 
-# Display conversation
-for i, (u, r) in enumerate(st.session_state.history):
-    st.markdown(f"**🧑 You:** {u}")
-    st.markdown(f"**🤖 Bot:** {r}")
-    st.markdown("---")
+        st.markdown(f"**Bot:** {final_reply}")