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| import gradio as gr | |
| import pyttsx3 | |
| import PyPDF2 | |
| import os | |
| import time | |
| import uuid | |
| import numpy as np | |
| from gtts import gTTS | |
| from playsound import playsound | |
| from sentence_transformers import SentenceTransformer | |
| import chromadb | |
| from groq import Groq | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| GROQ_API_KEY = os.environ.get("GROQ_API_KEY") | |
| groq_client = Groq(api_key=GROQ_API_KEY) | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| client = chromadb.Client() | |
| collection = client.create_collection("echo_study") | |
| PDF_FOLDER = "." | |
| #PDF_FOLDER = "pdfs" | |
| loaded_files = {} | |
| pdf_texts = {} | |
| current_file = {"name": None} | |
| QUESTIONS = { | |
| "embedded systems": [ | |
| "How does the lecture define an Embedded System?", | |
| "What are the primary resource constraints in embedded systems?", | |
| "How do embedded systems interact with the physical world?" | |
| ], | |
| "dynamic programming": [ | |
| "What is the simplest way to define Dynamic Programming?", | |
| "How many times does DP solve each subproblem?", | |
| "What is the simple formula for Dynamic Programming" | |
| ], | |
| "mongol history": [ | |
| "Why did the Empire's huge size lead to its fall?", | |
| "What was the original goal of the British East India Company?" | |
| ] | |
| } | |
| def speak_system(text): | |
| engine = pyttsx3.init() | |
| engine.setProperty('rate', 140) | |
| engine.say(text) | |
| engine.runAndWait() | |
| def speak_user(text): | |
| audio_path = f"C:/Users/hnaal/Desktop/Echo_study/user_{uuid.uuid4()}.mp3" | |
| tts = gTTS(text=text, lang='en') | |
| tts.save(audio_path) | |
| playsound(audio_path) | |
| os.remove(audio_path) | |
| def load_all_pdfs(): | |
| speak_system("Welcome back! Ready to tackle your studies?") | |
| yield "⏳ Processing Embeddings..." | |
| for filename in os.listdir(PDF_FOLDER): | |
| if filename.endswith(".pdf"): | |
| filepath = os.path.join(PDF_FOLDER, filename) | |
| with open(filepath, "rb") as f: | |
| reader = PyPDF2.PdfReader(f) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| pdf_texts[filename] = text | |
| embedding = model.encode(text[:2000]).tolist() | |
| collection.add( | |
| documents=[text[:2000]], | |
| embeddings=[embedding], | |
| ids=[filename], | |
| metadatas=[{"source": filename}] | |
| ) | |
| name = filename.replace(".pdf", "").replace("_", " ").lower() | |
| loaded_files[name] = filename | |
| yield f"⏳ Processing: {filename}..." | |
| speak_system("All files loaded successfully.") | |
| yield "✅ Loaded: " + ", ".join(loaded_files.keys()) | |
| def update_questions(pdf_name): | |
| pdf_key = pdf_name.lower() | |
| for key in QUESTIONS: | |
| if any(word in pdf_key for word in key.split()): | |
| return gr.Dropdown(choices=QUESTIONS[key], value=QUESTIONS[key][0]) | |
| return gr.Dropdown(choices=[], value=None) | |
| def find_best_chunk(question, pdf_text): | |
| chunks = [] | |
| words = pdf_text.split() | |
| for i in range(0, len(words), 80): | |
| chunk = " ".join(words[i:i+80]) | |
| chunks.append(chunk) | |
| if not chunks: | |
| return pdf_text[:500] | |
| question_embedding = model.encode(question) | |
| chunk_embeddings = [model.encode(chunk) for chunk in chunks] | |
| similarities = [ | |
| np.dot(question_embedding, ce) / (np.linalg.norm(question_embedding) * np.linalg.norm(ce)) | |
| for ce in chunk_embeddings | |
| ] | |
| best_idx = similarities.index(max(similarities)) | |
| return chunks[best_idx] | |
| def ask_groq(question, context, file_name): | |
| response = groq_client.chat.completions.create( | |
| model="llama-3.3-70b-versatile", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": f"""You are EchoStudy, a warm and encouraging study partner for blind students. | |
| When answering: | |
| 1. Start with a different warm phrase each time, like: Great question!, Interesting!, Good thinking!, Let me help you with that! | |
| 2. Use a simple real-life analogy to explain if needed. | |
| 3. Answer in 2 short sentences only, very simple and brief. | |
| 4. Avoid markdown symbols like stars or hashtags. | |
| 5. End with: Would you like more details?""" | |
| }, | |
| { | |
| "role": "user", | |
| "content": f"Context from {file_name}: {context}\n\nQuestion: {question}" | |
| } | |
| ], | |
| max_tokens=80 | |
| ) | |
| return response.choices[0].message.content | |
| def demo_interaction(pdf_name, question): | |
| log = "" | |
| if pdf_name.strip().lower() != current_file["name"]: | |
| speak_system("Please say the name of your PDF file.") | |
| log += "🔊 System: Please say the name of your PDF file.\n" | |
| yield log, "" | |
| time.sleep(1) | |
| speak_user(pdf_name) | |
| log += f"🎤 Student: {pdf_name}\n" | |
| yield log, "" | |
| time.sleep(1) | |
| found = None | |
| for name in loaded_files: | |
| if any(word.lower() in name.lower() for word in pdf_name.split()): | |
| found = name | |
| break | |
| if not found: | |
| speak_system("Sorry, I could not find that file.") | |
| log += "🔊 System: Sorry, I could not find that file.\n" | |
| yield log, "Not found" | |
| return | |
| current_file["name"] = pdf_name.strip().lower() | |
| speak_system(f"Found {found}. Ready for your question.") | |
| log += f"🔊 System: Found {found}. Ready for your question.\n" | |
| yield log, found | |
| time.sleep(1) | |
| else: | |
| found = None | |
| for name in loaded_files: | |
| if any(word.lower() in name.lower() for word in pdf_name.split()): | |
| found = name | |
| break | |
| if not found: | |
| yield "File not found.", "Not found" | |
| return | |
| speak_user(question) | |
| log += f"🎤 Student: {question}\n" | |
| yield log, found | |
| time.sleep(1) | |
| target_file = loaded_files[found] | |
| pdf_text = pdf_texts[target_file] | |
| context = find_best_chunk(question, pdf_text) | |
| answer = ask_groq(question, context, found) | |
| speak_system(answer) | |
| log += f"🔊 System: {answer}\n" | |
| yield log, found | |
| with gr.Blocks() as app: | |
| gr.Markdown("# 🎓 Echo Study – Voice-First Study Assistant") | |
| gr.Markdown("♿ Designed for visually impaired students") | |
| with gr.Row(): | |
| load_btn = gr.Button("📂 Load Study Materials") | |
| load_status = gr.Textbox(label="Status") | |
| gr.Markdown("### 🎤 Demo Interaction") | |
| pdf_input = gr.Dropdown( | |
| choices=["embedded systems", "dynamic programming", "mongol history"], | |
| value="embedded systems", | |
| label="📄 Select PDF" | |
| ) | |
| question_input = gr.Dropdown( | |
| choices=QUESTIONS["embedded systems"], | |
| value=QUESTIONS["embedded systems"][0], | |
| label="❓ Select Question" | |
| ) | |
| selected_file = gr.Textbox(label="📂 Selected File", interactive=False) | |
| start_btn = gr.Button("▶️ Start Demo", variant="primary") | |
| conversation_log = gr.Textbox(label="Conversation Log", lines=10) | |
| pdf_input.change(update_questions, inputs=pdf_input, outputs=question_input) | |
| load_btn.click(load_all_pdfs, outputs=load_status, show_progress=False) | |
| start_btn.click(demo_interaction, inputs=[pdf_input, question_input], outputs=[conversation_log, selected_file]) | |
| app.launch() |