Create app.py

app.py

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+import torch
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from transformers import pipeline
+import tempfile
+import os
+
+# ---------------- LOAD MODEL ----------------
+
+print("Loading sentiment model...")
+
+# model_path = (
+#     "../Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
+#     "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13"
+# )
+
+analyzer = pipeline(
+    "text-classification",
+    model= "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+    device = -1  # CPU
+)
+
+print("Model loaded.")
+
+# ---------------- SINGLE REVIEW ----------------
+
+def sentiment_analyzer(review):
+    result = analyzer(review)[0]
+    return f"{result['label']} (Confidence: {round(result['score'], 4)})"
+
+
+# ---------------- EXCEL + VISUALIZATION ----------------
+
+def analyze_excel(file):
+    df = pd.read_excel(file.name)
+
+    if "review" not in df.columns:
+        return None, None, None, None
+
+    sentiments = []
+    scores = []
+
+    for review in df["review"]:
+        result = analyzer(review)[0]
+        sentiments.append(result["label"])
+        scores.append(round(result["score"], 4))
+
+    df["sentiment"] = sentiments
+    df["confidence_score"] = scores
+
+    # ---- Sentiment counts ----
+    counts = df["sentiment"].value_counts()
+
+    # ---- Bar Chart ----
+    fig_bar = plt.figure()
+    counts.plot(
+        kind="bar",
+        color=["#22c55e", "#ef4444"],
+        xlabel="Sentiment",
+        ylabel="Number of Reviews",
+        title="Sentiment Distribution (Bar Chart)"
+    )
+    plt.xticks(rotation=0)
+    plt.tight_layout()
+
+    # ---- Pie Chart ----
+    fig_pie = plt.figure()
+    counts.plot(
+        kind="pie",
+        autopct="%1.1f%%",
+        startangle=90,
+        colors=["#22c55e", "#ef4444"],
+        ylabel="",
+        title="Sentiment Distribution (Pie Chart)"
+    )
+    plt.tight_layout()
+
+    # ---- Save Excel for download ----
+    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx")
+    df.to_excel(tmp_file.name, index=False)
+
+    return df, fig_bar, fig_pie, tmp_file.name
+
+
+# ---------------- GRADIO UI ----------------
+
+with gr.Blocks(theme=gr.themes.Soft(), title="Gen AI Project 3: Sentiment Analyzer") as demo:
+
+    gr.Markdown("""
+    Gen AI Project 3: Sentiment Analyzer  
+    Analyze **individual reviews** or **bulk Excel reviews** using a fine-tuned **DistilBERT** model.  
+    Get **AI insights + visual analytics + downloadable results**.
+    """)
+
+    with gr.Tabs():
+
+        # -------- SINGLE REVIEW TAB --------
+        with gr.Tab("📝 Single Review"):
+            with gr.Row():
+                review_input = gr.Textbox(
+                    label="Enter Review Text",
+                    placeholder="e.g. I absolutely loved this product!",
+                    lines=4
+                )
+                sentiment_output = gr.Textbox(label="Predicted Sentiment")
+
+            gr.Button("Analyze Sentiment 🚀").click(
+                fn=sentiment_analyzer,
+                inputs=review_input,
+                outputs=sentiment_output
+            )
+
+        # -------- EXCEL TAB --------
+        with gr.Tab("📊 Excel Upload & Analysis"):
+            file_input = gr.File(
+                label="Upload Excel File (.xlsx)",
+                file_types=[".xlsx"]
+            )
+
+            analyze_btn = gr.Button("Analyze Excel 📈")
+
+            df_output = gr.Dataframe(label="Sentiment Results Table")
+            bar_output = gr.Plot(label="Bar Chart")
+            pie_output = gr.Plot(label="Pie Chart")
+            download_output = gr.File(label="Download Analyzed Excel")
+
+            analyze_btn.click(
+                fn=analyze_excel,
+                inputs=file_input,
+                outputs=[df_output, bar_output, pie_output, download_output]
+            )
+
+    gr.Markdown("""
+    ---
+    ✅ **Tech Stack:** Python, Transformers, DistilBERT, Pandas, Matplotlib, Gradio  
+    🚀 **Use Case:** Product reviews, customer feedback, social media sentiment  
+    """)
+
+demo.launch()