Update app.py

app.py

@@ -1,5 +1,3 @@
-# ✅ Stock Recommendation Extractor from YouTube Audio (with full error reporting)
-
 import os
 import gradio as gr
 import tempfile
@@ -15,8 +13,7 @@ try:
 except:
     WHISPER_AVAILABLE = False
 
-# ✅ Download audio using working logic
-
+# ✅ Download audio from YouTube
 def download_audio(url, cookies_path=None):
     try:
         temp_dir = tempfile.mkdtemp()
@@ -54,7 +51,6 @@ def download_audio(url, cookies_path=None):
         return None, f"❌ Download error: {str(e)}"
 
 # ✅ Transcribe audio using Whisper
-
 def transcribe_audio(path):
     if not WHISPER_AVAILABLE:
         return "❌ Whisper not available. Please install openai-whisper."
@@ -66,8 +62,7 @@ def transcribe_audio(path):
         traceback.print_exc()
         return f"❌ Transcription failed: {str(e)}"
 
-# ✅ Extract stock-related information from transcript
-
+# ✅ Extract stock-related information
 def extract_stock_info(text):
     try:
         companies = re.findall(r'\b[A-Z][a-z]+(?: [A-Z][a-z]+)*\b', text)
@@ -85,7 +80,6 @@ def extract_stock_info(text):
         if actions:
             result += f"📊 Actions: {', '.join(set(actions[:10]))}\n"
 
-        # Highlight potential recommendations
         recommendations = []
         for line in text.split("."):
             if any(word in line.lower() for word in ['buy', 'sell', 'target', 'hold']):
@@ -105,30 +99,23 @@ def extract_stock_info(text):
         return f"❌ Stock info extraction failed: {str(e)}"
 
 # ✅ Save uploaded cookies.txt
-
 def save_cookies(file):
     if file is None:
         return None
 
     temp_path = tempfile.mktemp(suffix=".txt")
-
     try:
-        # Handle both file-like object and NamedString
-        if hasattr(file, "read"):  # File-like
+        if hasattr(file, "read"):
             with open(temp_path, "wb") as f:
                 f.write(file.read())
-        else:  # NamedString (str path)
+        else:
             shutil.copy(file, temp_path)
-
         return temp_path
-
     except Exception as e:
         print(f"❌ Failed to handle cookies.txt: {e}")
         return None
 
-
-# ✅ Full pipeline with error traceback
-
+# ✅ YouTube pipeline
 def run_pipeline(url, cookies_file):
     try:
         if not WHISPER_AVAILABLE:
@@ -153,23 +140,52 @@ def run_pipeline(url, cookies_file):
         print(tb)
         return f"❌ Unhandled Error:\n{tb}", ""
 
-# ✅ Gradio Interface
-with gr.Blocks(title="Stock Insights from YouTube Audio") as demo:
-    gr.Markdown("""
-    # 🎧 Extract Stock Recommendations from YouTube Audio
-    This app downloads the audio from a YouTube video, transcribes it with Whisper,
-    and extracts stock trading recommendations, sentiments, and symbols.
-    """)
+# ✅ Audio file upload pipeline
+def run_pipeline_audio(audio_file):
+    try:
+        if not WHISPER_AVAILABLE:
+            return "❌ Whisper is not installed. Run: pip install openai-whisper", ""
+        if audio_file is None:
+            return "❌ No audio file uploaded", ""
+
+        temp_audio_path = tempfile.mktemp(suffix=os.path.splitext(audio_file.name)[-1])
+        with open(temp_audio_path, "wb") as f:
+            f.write(audio_file.read())
+
+        transcript = transcribe_audio(temp_audio_path)
+        if transcript.startswith("❌"):
+            return transcript, ""
+
+        stock_info = extract_stock_info(transcript)
+        return "✅ Complete", stock_info
 
-    with gr.Row():
-        url_input = gr.Textbox(label="🎥 YouTube Video URL")
-        cookie_input = gr.File(label="cookies.txt (optional)", file_types=[".txt"])
+    except Exception as e:
+        tb = traceback.format_exc()
+        print(tb)
+        return f"❌ Unhandled Error:\n{tb}", ""
 
-    run_btn = gr.Button("🚀 Extract Stock Info")
-    status_output = gr.Textbox(label="Status")
-    result_output = gr.Textbox(label="Stock Info", lines=12)
+# ✅ Gradio UI
+with gr.Blocks(title="Stock Insights from YouTube or Audio") as demo:
+    gr.Markdown("""
+    # 🎧 Extract Stock Recommendations from YouTube or Uploaded Audio
+    Upload a YouTube URL or an audio file. We'll transcribe it and extract stock-related insights!
+    """)
 
-    run_btn.click(fn=run_pipeline, inputs=[url_input, cookie_input], outputs=[status_output, result_output])
+    with gr.Tab("📺 From YouTube Video"):
+        with gr.Row():
+            url_input = gr.Textbox(label="🎥 YouTube Video URL")
+            cookie_input = gr.File(label="cookies.txt (optional)", file_types=[".txt"])
+        yt_run_btn = gr.Button("🚀 Extract from YouTube")
+        yt_status = gr.Textbox(label="Status")
+        yt_result = gr.Textbox(label="Stock Info", lines=12)
+        yt_run_btn.click(fn=run_pipeline, inputs=[url_input, cookie_input], outputs=[yt_status, yt_result])
+
+    with gr.Tab("🎵 From Uploaded Audio"):
+        audio_input = gr.File(label="Upload Audio File", file_types=[".mp3", ".wav", ".m4a", ".webm"])
+        audio_run_btn = gr.Button("🚀 Extract from Audio")
+        audio_status = gr.Textbox(label="Status")
+        audio_result = gr.Textbox(label="Stock Info", lines=12)
+        audio_run_btn.click(fn=run_pipeline_audio, inputs=[audio_input], outputs=[audio_status, audio_result])
 
 if __name__ == "__main__":
     demo.launch(debug=True)