Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import json
+import joblib
+import numpy as np
+import pandas as pd
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+from datetime import datetime
+import os
+import gspread
+from google.oauth2.service_account import Credentials
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+
+def save_to_google_sheet(data):
+    scope = [
+        "https://spreadsheets.google.com/feeds",
+        "https://www.googleapis.com/auth/drive"
+    ]
+
+    # Convert Streamlit's AttrDict to a normal dict (correct way)
+    creds_dict = {k: v for k, v in st.secrets["gcp_credentials"].items()}
+
+    # Handle multiline private key properly
+    if "private_key" in creds_dict:
+        creds_dict["private_key"] = creds_dict["private_key"].replace("\\n", "\n")
+
+    # Authenticate and connect
+    creds = Credentials.from_service_account_info(creds_dict, scopes=scope)
+    client = gspread.authorize(creds)
+    sheet = client.open("Sentiment Feedback Log").sheet1
+
+    # Append row
+    sheet.append_row([
+        data.get("timestamp", ""),
+        data.get("username", ""),
+        data.get("user_id", ""),
+        data.get("text", ""),
+        data.get("model_a", ""),
+        data.get("model_b", ""),
+        data.get("ensemble", ""),
+        data.get("feedback", "")
+    ])
+
+
+st.set_page_config(page_title="Sentiment Model Comparison", layout="wide")
+st.title("📊 Sentiment Classifier Comparison")
+
+# --- Load models and tokenizers ---
+import streamlit as st
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+import json
+
+@st.cache_resource
+def load_model_and_tokenizer(model_file, tokenizer_file):
+    model_path = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename=model_file)
+    tokenizer_path = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename=tokenizer_file)
+
+    with open(tokenizer_path, "r") as f:
+        tokenizer = tokenizer_from_json(f.read())
+
+    model = load_model(model_path)
+    return model, tokenizer
+
+# === Load Cached Models ===
+model_a, tokenizer_a = load_model_and_tokenizer("sentiment_model_imdb_6.6M.keras", "tokenizer_50k.json") # 6.6M params & 50K vocab
+model_b, tokenizer_b = load_model_and_tokenizer("sentiment_model_imdb_34M.keras", "tokenizer_256k.json") # 34M params & 256K vocab
+
+# --- Constants ---
+maxlen = 300
+labels = ["Negative", "Neutral", "Positive"]
+
+# --- Preprocess ---
+def preprocess(text, tokenizer):
+    text = text.lower()
+    seq = tokenizer.texts_to_sequences([text])
+    padded = pad_sequences(seq, maxlen=maxlen, padding='post')
+    return padded
+
+# --- Format Output ---
+def format_probs(probs):
+    return {labels[i]: f"{probs[i]*100:.2f}%" for i in range(3)}
+
+# --- Text Input ---
+st.markdown("### 📝 Enter a review:")
+text = st.text_area("", height=150)
+
+# --- File Upload ---
+st.markdown("---")
+file = st.file_uploader("📂 Or upload a CSV file with a 'review' column for bulk analysis", type=["csv"])
+
+# Optional: User identification
+user_name = st.text_input("🔐 Enter your name:")
+user_id = st.text_input("🔐 Enter your email (optional):")
+
+pred_a = pred_b = ensemble_label = None
+
+if st.button("🔍 Analyze") and (text.strip() or file):
+    if text.strip():
+        padded_a = preprocess(text, tokenizer_a)
+        padded_b = preprocess(text, tokenizer_b)
+        pred_a = model_a.predict(padded_a)[0]
+        pred_b = model_b.predict(padded_b)[0]
+        ensemble_pred = (pred_a + pred_b) / 2
+        ensemble_label = labels[int(ensemble_pred.argmax())]
+
+        col1, col2, col3 = st.columns(3)
+
+        with col1:
+            st.subheader("🔹 Model A")
+            st.caption("🧠 6M Parameters | 📖 50k Vocab")
+            st.markdown(" | ".join([f"**{l}:** {v}" for l, v in format_probs(pred_a).items()]))
+            st.write(f"→ **Predicted:** _{labels[int(pred_a.argmax())]}_")
+
+        with col2:
+            st.subheader("🔸 Model B")
+            st.caption("🧠 34M Parameters | 📖 256k Vocab")
+            st.markdown(" | ".join([f"**{l}:** {v}" for l, v in format_probs(pred_b).items()]))
+            st.write(f"→ **Predicted:** _{labels[int(pred_b.argmax())]}_")
+
+        with col3:
+            st.subheader("⚖️ Ensemble Average")
+            st.caption("🧮 Averaged Output (A + B)")
+            st.markdown(" | ".join([f"**{l}:** {v}" for l, v in format_probs(ensemble_pred).items()]))
+            st.write(f"→ **Final Sentiment:** ✅ _{ensemble_label}_")
+
+        st.markdown("### 📈 Confidence Comparison")
+        st.bar_chart({
+            "Model A": pred_a,
+            "Model B": pred_b,
+            "Ensemble": ensemble_pred
+        })
+
+    if file:
+        df = pd.read_csv(file)
+        if 'review' not in df.columns:
+            st.error("CSV must contain a 'review' column.")
+        else:
+            preds = []
+            for text in df['review']:
+                padded_a = preprocess(text, tokenizer_a)
+                padded_b = preprocess(text, tokenizer_b)
+                pred_a = model_a.predict(padded_a)[0]
+                pred_b = model_b.predict(padded_b)[0]
+                ensemble = (pred_a + pred_b) / 2
+                preds.append(labels[int(ensemble.argmax())])
+
+            df['Predicted Sentiment'] = preds
+            st.dataframe(df)
+            st.download_button("📥 Download Results", df.to_csv(index=False), file_name="sentiment_predictions.csv")
+
+# --- Info Panel ---
+with st.expander("ℹ️ Model Details"):
+    st.markdown("""
+    - **Model A**: Smaller model, faster, trained on 50k vocab.
+    - **Model B**: Larger model, more accurate, trained on 256k vocab.
+    - Ensemble averages predictions from both.
+    """)
+
+# --- Feedback ---
+st.markdown("---")
+st.markdown("### 💬 Feedback")
+feedback = st.radio("Was the prediction helpful?", ["👍 Yes", "👎 No", "No comment"], horizontal=True)
+
+if feedback and (user_name.strip() or user_id.strip() or text.strip()):
+    st.success("Thanks for your feedback! ✅")
+
+    feedback_data = {
+        "timestamp": datetime.now().isoformat(),
+        "username": user_name,
+        "user_id": user_id,
+        "text": text if text else None,
+        "model_a": labels[int(pred_a.argmax())] if pred_a is not None else None,
+        "model_b": labels[int(pred_b.argmax())] if pred_b is not None else None,
+        "ensemble": ensemble_label if ensemble_label is not None else None,
+        "feedback": feedback if feedback != "No comment" else None,
+    }
+
+    # Save to local CSV
+    log_path = "user_feedback.csv"
+    feedback_df = pd.DataFrame([feedback_data])
+    if not os.path.exists(log_path):
+        feedback_df.to_csv(log_path, index=False)
+    else:
+        feedback_df.to_csv(log_path, mode='a', header=False, index=False)
+
+    # Save to Google Sheets
+    try:
+        save_to_google_sheet(feedback_data)
+    except Exception as e:
+        st.error(f"Error saving feedback to Google Sheets: {e}")
+