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| import streamlit as st | |
| import torch | |
| from transformers import AutoTokenizer | |
| import whisper | |
| import os | |
| import pandas as pd | |
| from dl import PitchEvaluationModel # Import model | |
| def transcribe_video(video_file): | |
| """Transcribe video using Whisper.""" | |
| try: | |
| model = whisper.load_model("tiny") # or 'base.en' | |
| result = model.transcribe(video_file) | |
| return result["text"] | |
| except Exception as e: | |
| st.error("❌ An error occurred during transcription.") | |
| return "" | |
| def load_model(): | |
| """Load the trained model.""" | |
| try: | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = PitchEvaluationModel("bert-base-uncased").to(device) | |
| model.load_state_dict(torch.load("best_pitch_model.pt", map_location=device)) | |
| model.eval() | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
| return model, tokenizer, device | |
| except Exception as e: | |
| st.error("❌ Failed to load the model.") | |
| return None, None, None | |
| def evaluate_pitch(transcript, model, tokenizer, device): | |
| """Evaluate transcript using the trained model.""" | |
| try: | |
| inputs = tokenizer(transcript, return_tensors="pt", truncation=True, padding="max_length", max_length=512) | |
| input_ids, attention_mask = inputs["input_ids"].to(device), inputs["attention_mask"].to(device) | |
| with torch.no_grad(): | |
| clarity, team, traction = model(input_ids, attention_mask) | |
| return torch.argmax(clarity).item() + 1, torch.argmax(team).item() + 1, torch.argmax(traction).item() + 1 | |
| except Exception as e: | |
| st.error("❌ Error in evaluation process.") | |
| return None, None, None | |
| # Streamlit App UI | |
| st.set_page_config(page_title="Pitch Evaluation App", layout="wide") | |
| st.title("🚀 Pitch Evaluation") | |
| 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: | |
| 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) | |
| 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!") | |