Update app.py

app.py

@@ -5,20 +5,25 @@ import joblib
 import gradio as gr
 from huggingface_hub import hf_hub_download
 
-# Download the model from Hugging Face Hub
+# --- Load model from Hugging Face Hub ---
 MODEL_REPO = "sangambhamare/TruthDetection"
 MODEL_FILENAME = "model.joblib"
-
 model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
 model = joblib.load(model_path)
 
-# MFCC extraction
+# --- Load interactive report HTML (must be in same directory) ---
+report_html = ""
+if os.path.exists("interactive_report.html"):
+    with open("interactive_report.html", "r", encoding="utf-8") as f:
+        report_html = f.read()
+
+# --- MFCC feature extraction ---
 def extract_mfcc(file_path):
     y, sr = librosa.load(file_path, sr=None)
     mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
     return np.mean(mfcc, axis=1)
 
-# Prediction
+# --- Prediction function ---
 def predict_audio(audio_file):
     try:
         features = extract_mfcc(audio_file).reshape(1, -1)
@@ -27,7 +32,7 @@ def predict_audio(audio_file):
     except Exception as e:
         return f"Error: {e}"
 
-# Gradio UI
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
     gr.Markdown("<h1 style='text-align: center;'>Truth Detection from Audio Stories</h1>")
     gr.Markdown(
@@ -36,10 +41,17 @@ with gr.Blocks() as demo:
         "based on MFCC features and a trained Random Forest classifier."
         "</p>"
     )
+
     audio_input = gr.Audio(type="filepath", label="Upload WAV Audio File (30 seconds)")
     output = gr.Textbox(label="Prediction")
     submit_btn = gr.Button("Predict")
     submit_btn.click(fn=predict_audio, inputs=audio_input, outputs=output)
+
+    if report_html:
+        gr.Markdown("<hr>")
+        gr.Markdown("<h3 style='text-align: center;'>Interactive Report</h3>")
+        gr.HTML(value=report_html)
+
     gr.Markdown("<p style='text-align: center; font-size: 12px; color: gray;'>Developed by Sangam Sanjay Bhamare, 2025.</p>")
 
 if __name__ == "__main__":