Update app.py

app.py

@@ -2,24 +2,10 @@ import streamlit as st
 import torch
 from transformers import AutoTokenizer
 import whisper
-import subprocess
 import os
 import pandas as pd
 from dl import PitchEvaluationModel  # Import model
 
-def download_youtube_video(url, output_file="pitch_video.mp4"):
-    """Download YouTube video using yt-dlp."""
-    if "youtube.com" not in url and "youtu.be" not in url:
-        st.error("❌ Invalid URL! Please enter a valid YouTube link.")
-        return None
-    try:
-        command = ["yt-dlp", "-f", "mp4", "-o", output_file, url]
-        subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        return output_file
-    except subprocess.CalledProcessError:
-        st.error("❌ Failed to download the video. Please check the URL and try again.")
-        return None
-
 def transcribe_video(video_file):
     """Transcribe video using Whisper."""
     try:
@@ -59,45 +45,35 @@ def evaluate_pitch(transcript, model, tokenizer, device):
 st.set_page_config(page_title="Pitch Evaluation App", layout="wide")
 st.title("🚀 Pitch Evaluation")
 
-option = st.radio("Choose Input Method", ("YouTube URL", "Upload File"), horizontal=True)
-
-if option == "YouTube URL":
-    url = st.text_input("🎥 Enter YouTube URL")
-    if st.button("Download and Transcribe", use_container_width=True):
-        video_file = download_youtube_video(url)
-        if video_file:
-            transcript = transcribe_video(video_file)
-            st.text_area("📜 Transcript", transcript, height=200)
-elif option == "Upload File":
-    uploaded_file = st.file_uploader("📂 Upload Video", type=["mp4"], help="Upload a video file for transcription and evaluation.")
-    if uploaded_file is not None:
-        if uploaded_file.type != "video/mp4":
-            st.error("❌ Invalid file format! Please upload an MP4 file.")
-        else:
-            with open("uploaded_video.mp4", "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            transcript = transcribe_video("uploaded_video.mp4")
-            st.text_area("📜 Transcript", transcript, height=200)
+uploaded_file = st.file_uploader("📂 Upload Video", type=["mp4"], help="Upload a video file for transcription and evaluation.")
+if uploaded_file is not None:
+    if uploaded_file.type != "video/mp4":
+        st.error("❌ Invalid file format! Please upload an MP4 file.")
+    else:
+        with open("uploaded_video.mp4", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        transcript = transcribe_video("uploaded_video.mp4")
+        st.text_area("📜 Transcript", transcript, height=200)
 
-if 'transcript' in locals() and transcript:
-    model, tokenizer, device = load_model()
-    if model is not None:
-        clarity, team, traction = evaluate_pitch(transcript, model, tokenizer, device)
-        if None not in (clarity, team, traction):
-            # Create a DataFrame for the scoring table
-            categories = ["Clarity & Conciseness", "Team-Market Fit", "Traction / Validation"]
-            scores = [clarity, team, traction]
-            descriptions = [
-                "Extremely clear, direct, and easy to follow;no fluff, just essential details." if clarity == 5 else "Mostly clear, with only minor unnecessary details." if clarity == 4 else "Somewhat clear but includes extra details or minor distractions." if clarity == 3 else "Lacks clarity; hard to follow; too much fluff or filler." if clarity == 2 else "Unclear, rambling, and difficult to understand.",
-                "Founders have highly relevant skills & experience to execute this successfully." if team == 5 else "Founders have good experience but may lack some key skills." if team == 4 else "Some relevant experience but gaps in expertise." if team == 3 else "Limited relevant experience; execution ability is questionable." if team == 2 else "No clear expertise in this space; team seems unqualified.",
-                "Strong proof of demand (users, revenue, engagement, partnerships, etc.)." if traction == 5 else "Good early validation with promising signs of demand." if traction == 4 else "Some traction but not yet convincing." if traction == 3 else "Weak or vague traction, with little evidence of demand." if traction == 2 else "No validation or proof that people want this."
-            ]
-            df = pd.DataFrame({"Category": categories, "Score (1-5)": scores, "Evaluation": descriptions})
+        if transcript:
+            model, tokenizer, device = load_model()
+            if model is not None:
+                clarity, team, traction = evaluate_pitch(transcript, model, tokenizer, device)
+                if None not in (clarity, team, traction):
+                    # Create a DataFrame for the scoring table
+                    categories = ["Clarity & Conciseness", "Team-Market Fit", "Traction / Validation"]
+                    scores = [clarity, team, traction]
+                    descriptions = [
+                        "Extremely clear, direct, and easy to follow; no fluff, just essential details." if clarity == 5 else "Mostly clear, with only minor unnecessary details." if clarity == 4 else "Somewhat clear but includes extra details or minor distractions." if clarity == 3 else "Lacks clarity; hard to follow; too much fluff or filler." if clarity == 2 else "Unclear, rambling, and difficult to understand.",
+                        "Founders have highly relevant skills & experience to execute this successfully." if team == 5 else "Founders have good experience but may lack some key skills." if team == 4 else "Some relevant experience but gaps in expertise." if team == 3 else "Limited relevant experience; execution ability is questionable." if team == 2 else "No clear expertise in this space; team seems unqualified.",
+                        "Strong proof of demand (users, revenue, engagement, partnerships, etc.)." if traction == 5 else "Good early validation with promising signs of demand." if traction == 4 else "Some traction but not yet convincing." if traction == 3 else "Weak or vague traction, with little evidence of demand." if traction == 2 else "No validation or proof that people want this."
+                    ]
+                    df = pd.DataFrame({"Category": categories, "Score (1-5)": scores, "Evaluation": descriptions})
 
-            st.write("## 📊 Evaluation Results")
-            st.table(df)
+                    st.write("## 📊 Evaluation Results")
+                    st.table(df)
 
-        if ((clarity + team + traction)/3) >=3.5:
-            st.write("## 🎉 Congrats! You have a high possibility to be accepted")
-        else:
-            st.write("## 🙌 Need More Practice, but don't give up!")
+                if ((clarity + team + traction)/3) >= 3.5:
+                    st.write("## 🎉 Congrats! You have a high possibility to be accepted")
+                else:
+                    st.write("## 🙌 Need More Practice, but don't give up!")