Update app.py

app.py

@@ -1,119 +1,121 @@
-import streamlit as st
-from finetune_lora import train_lora
-from evaluate_model import generate_review, compute_metrics, evaluate_perplexity
-import matplotlib.pyplot as plt
-import time
-import os
-
-st.set_page_config(page_title="LLM LoRA Fine-Tuning App", layout="wide")
-st.title("🧠 LoRA Fine-Tuned LLM for Product Review Generation & Evaluation")
-
-tab1, tab2 = st.tabs(["⚙️ Fine-Tuning", "🪶 Generate & Evaluate Reviews"])
-
-# ----------- TAB 1: FINE-TUNING -----------
-with tab1:
-    st.header("⚙️ Fine-Tune Model with LoRA")
-
-    model_choice = st.selectbox("Select Base Model", ["gpt2", "Qwen/Qwen2.5-1.5B", "meta-llama/Llama-3.2-1B"])
-    epochs = st.slider("Epochs", 1, 5, 2)
-    lr = st.number_input("Learning Rate", value=1e-4, step=1e-5, format="%.5f")
-
-    metrics = None
-
-    if st.button("🚀 Start Fine-Tuning"):
-        st.info(f"Starting LoRA fine-tuning on `{model_choice}` for {epochs} epochs...")
-
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-
-        for epoch in range(epochs):
-            status_text.text(f"Training... Epoch {epoch+1}/{epochs}")
-            time.sleep(1.5)  # simulate training progress
-            progress_bar.progress(int((epoch + 1) / epochs * 100))
-
-        with st.spinner("Finalizing and saving adapter..."):
-            metrics = train_lora(model_choice, epochs, lr)
-
-        st.success("✅ Fine-tuning Completed Successfully!")
-        st.balloons()
-
-        if metrics:
-            st.write("### 📊 Training Summary")
-            st.json(metrics)
-
-            if "train_loss" in metrics:
-                fig, ax = plt.subplots()
-                ax.plot(metrics["train_loss"], label="Training Loss", marker='o')
-                ax.set_xlabel("Steps")
-                ax.set_ylabel("Loss")
-                ax.legend()
-                ax.grid(True)
-                st.pyplot(fig)
-
-        progress_bar.empty()
-        status_text.text("Training finished ✔️")
-
-# ----------- TAB 2: GENERATION & EVALUATION -----------
-with tab2:
-    st.header("🪶 Generate & Evaluate Product Reviews")
-
-    model_choice = st.selectbox("Select Fine-Tuned Model", ["gpt2", "Qwen/Qwen2.5-1.5B", "meta-llama/Llama-3.2-1B"], key="gen_model")
-
-    product = st.text_input("Product Name", "Amazon Kindle E-Reader")
-    category = st.text_input("Category", "E-Reader")
-    features = st.text_area("Features", "6-inch display, WiFi, lightweight")
-    rating = st.slider("Rating", 1, 5, 5)
-    tone = st.selectbox("Tone", ["enthusiastic", "critical", "balanced"])
-
-    if "generated_reviews" not in st.session_state:
-        st.session_state.generated_reviews = []
-
-    if st.button("✨ Generate Review"):
-        with st.spinner("Generating review..."):
-            review = generate_review(model_choice, product, category, features, rating, tone)
-        st.success("✅ Generated Review:")
-        st.write(review)
-
-        st.session_state.generated_reviews.append(review)
-        count = len(st.session_state.generated_reviews)
-        st.info(f"🧩 Total Generated Reviews: {count}")
-
-        # --------- Evaluation Dashboard (Tracking) ---------
-        st.subheader("📊 Evaluation Dashboard")
-
-        # Initialize storage for evaluation history
-        if "evaluation_history" not in st.session_state:
-            st.session_state.evaluation_history = []
-
-        # -------- Evaluate every 10 reviews --------
-        if count % 10 == 0:
-            st.warning("📊 Evaluating model performance after 10 reviews...")
-            with st.spinner("Running metrics evaluation..."):
-                metrics = compute_metrics(st.session_state.generated_reviews, requested_tone=tone)
-                ppl = evaluate_perplexity(model_choice, test_csv="dataset/amazon_product_reviews.csv")
-
-            st.subheader("📈 Evaluation Metrics")
-            st.write(f"- **Average Length:** {metrics['avg_length']:.2f} words")
-            st.write(f"- **Tone Match Ratio:** {metrics['tone_match_ratio']*100:.1f}%")
-            st.write(f"- **Perplexity (↓ better):** {ppl:.2f}")
-
-            # Save current evaluation data
-            eval_data = {
-                "reviews_count": count,
-                "avg_length": metrics["avg_length"],
-                "tone_match": metrics["tone_match_ratio"] * 100,
-                "perplexity": ppl
-            }
-            st.session_state.evaluation_history.append(eval_data)
-
-        import pandas as pd
-        df = pd.DataFrame(st.session_state.evaluation_history)
-
-        # Show line trends over evaluations
-        if not df.empty:
-            st.line_chart(df.set_index("reviews_count")[["avg_length", "tone_match", "perplexity"]])
-            st.dataframe(df.style.format({"avg_length": "{:.2f}", "tone_match": "{:.1f}", "perplexity": "{:.2f}"}))
-
-        st.markdown("✅ Model evaluation updates automatically after every 10 generated reviews ��� showing live performance improvements.")
-
-
+# app_type: streamlit
+
+import streamlit as st
+from finetune_lora import train_lora
+from evaluate_model import generate_review, compute_metrics, evaluate_perplexity
+import matplotlib.pyplot as plt
+import time
+import os
+
+st.set_page_config(page_title="LLM LoRA Fine-Tuning App", layout="wide")
+st.title("🧠 LoRA Fine-Tuned LLM for Product Review Generation & Evaluation")
+
+tab1, tab2 = st.tabs(["⚙️ Fine-Tuning", "🪶 Generate & Evaluate Reviews"])
+
+# ----------- TAB 1: FINE-TUNING -----------
+with tab1:
+    st.header("⚙️ Fine-Tune Model with LoRA")
+
+    model_choice = st.selectbox("Select Base Model", ["gpt2", "Qwen/Qwen2.5-1.5B", "meta-llama/Llama-3.2-1B"])
+    epochs = st.slider("Epochs", 1, 5, 2)
+    lr = st.number_input("Learning Rate", value=1e-4, step=1e-5, format="%.5f")
+
+    metrics = None
+
+    if st.button("🚀 Start Fine-Tuning"):
+        st.info(f"Starting LoRA fine-tuning on `{model_choice}` for {epochs} epochs...")
+
+        progress_bar = st.progress(0)
+        status_text = st.empty()
+
+        for epoch in range(epochs):
+            status_text.text(f"Training... Epoch {epoch+1}/{epochs}")
+            time.sleep(1.5)  # simulate training progress
+            progress_bar.progress(int((epoch + 1) / epochs * 100))
+
+        with st.spinner("Finalizing and saving adapter..."):
+            metrics = train_lora(model_choice, epochs, lr)
+
+        st.success("✅ Fine-tuning Completed Successfully!")
+        st.balloons()
+
+        if metrics:
+            st.write("### 📊 Training Summary")
+            st.json(metrics)
+
+            if "train_loss" in metrics:
+                fig, ax = plt.subplots()
+                ax.plot(metrics["train_loss"], label="Training Loss", marker='o')
+                ax.set_xlabel("Steps")
+                ax.set_ylabel("Loss")
+                ax.legend()
+                ax.grid(True)
+                st.pyplot(fig)
+
+        progress_bar.empty()
+        status_text.text("Training finished ✔️")
+
+# ----------- TAB 2: GENERATION & EVALUATION -----------
+with tab2:
+    st.header("🪶 Generate & Evaluate Product Reviews")
+
+    model_choice = st.selectbox("Select Fine-Tuned Model", ["gpt2", "Qwen/Qwen2.5-1.5B", "meta-llama/Llama-3.2-1B"], key="gen_model")
+
+    product = st.text_input("Product Name", "Amazon Kindle E-Reader")
+    category = st.text_input("Category", "E-Reader")
+    features = st.text_area("Features", "6-inch display, WiFi, lightweight")
+    rating = st.slider("Rating", 1, 5, 5)
+    tone = st.selectbox("Tone", ["enthusiastic", "critical", "balanced"])
+
+    if "generated_reviews" not in st.session_state:
+        st.session_state.generated_reviews = []
+
+    if st.button("✨ Generate Review"):
+        with st.spinner("Generating review..."):
+            review = generate_review(model_choice, product, category, features, rating, tone)
+        st.success("✅ Generated Review:")
+        st.write(review)
+
+        st.session_state.generated_reviews.append(review)
+        count = len(st.session_state.generated_reviews)
+        st.info(f"🧩 Total Generated Reviews: {count}")
+
+        # --------- Evaluation Dashboard (Tracking) ---------
+        st.subheader("📊 Evaluation Dashboard")
+
+        # Initialize storage for evaluation history
+        if "evaluation_history" not in st.session_state:
+            st.session_state.evaluation_history = []
+
+        # -------- Evaluate every 10 reviews --------
+        if count % 10 == 0:
+            st.warning("📊 Evaluating model performance after 10 reviews...")
+            with st.spinner("Running metrics evaluation..."):
+                metrics = compute_metrics(st.session_state.generated_reviews, requested_tone=tone)
+                ppl = evaluate_perplexity(model_choice, test_csv="dataset/amazon_product_reviews.csv")
+
+            st.subheader("📈 Evaluation Metrics")
+            st.write(f"- **Average Length:** {metrics['avg_length']:.2f} words")
+            st.write(f"- **Tone Match Ratio:** {metrics['tone_match_ratio']*100:.1f}%")
+            st.write(f"- **Perplexity (↓ better):** {ppl:.2f}")
+
+            # Save current evaluation data
+            eval_data = {
+                "reviews_count": count,
+                "avg_length": metrics["avg_length"],
+                "tone_match": metrics["tone_match_ratio"] * 100,
+                "perplexity": ppl
+            }
+            st.session_state.evaluation_history.append(eval_data)
+
+        import pandas as pd
+        df = pd.DataFrame(st.session_state.evaluation_history)
+
+        # Show line trends over evaluations
+        if not df.empty:
+            st.line_chart(df.set_index("reviews_count")[["avg_length", "tone_match", "perplexity"]])
+            st.dataframe(df.style.format({"avg_length": "{:.2f}", "tone_match": "{:.1f}", "perplexity": "{:.2f}"}))
+
+        st.markdown("✅ Model evaluation updates automatically after every 10 generated reviews — showing live performance improvements.")
+
+