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.env

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+GROQ_API_KEY=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
+BRIGHT_API_KEY=ded1bc00-4d84-4dee-b4d6-c896cadf2417
+BRIGHT_ZONE=web_unlocker2
+OPENAI_API_KEY= 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

app.py

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+import os
+import json
+import requests
+import re
+import tempfile
+import torch
+import soundfile as sf
+
+from bs4 import BeautifulSoup
+from groq import Groq
+import gradio as gr
+from dotenv import load_dotenv
+from youtube_transcript_api import YouTubeTranscriptApi
+
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+
+load_dotenv()
+
+# API KEYS
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+BRIGHT_API_KEY = os.getenv("BRIGHT_API_KEY")
+BRIGHT_ZONE = os.getenv("BRIGHT_ZONE")
+
+client = Groq(api_key=GROQ_API_KEY)
+
+CHAT_FILE = "chat_history.json"
+PREF_FILE = "preferences.json"
+
+
+# SAFE JSON FUNCTIONS
+
+def load_json(file, default):
+    if os.path.exists(file):
+        try:
+            with open(file, "r") as f:
+                return json.load(f)
+        except:
+            return default
+    return default
+
+
+def save_json(file, data):
+    with open(file, "w") as f:
+        json.dump(data, f, indent=4)
+
+
+conversation_history = load_json(CHAT_FILE, [])
+user_preferences = load_json(PREF_FILE, {"style": "Default"})
+
+
+
+# LOAD TTS MODEL (Version 4)
+
+
+print("Loading SpeechT5 model...")
+
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+speaker_embeddings = torch.randn(1, 512)
+
+print("TTS Model Loaded")
+
+
+
+# VERSION 1 — WEBSITE SCRAPER
+
+
+def brightdata_request(target_url):
+
+    response = requests.post(
+        "https://api.brightdata.com/request",
+        headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {BRIGHT_API_KEY}"
+        },
+        json={
+            "zone": BRIGHT_ZONE,
+            "url": target_url,
+            "format": "raw"
+        }
+    )
+
+    return response.text
+
+
+def scrape_goodreads(url):
+
+    html = brightdata_request(url)
+
+    soup = BeautifulSoup(html, "html.parser")
+
+    books = []
+
+    rows = soup.find_all("tr")
+
+    for row in rows:
+
+        title_tag = row.find("a", class_="bookTitle")
+        author_tag = row.find("a", class_="authorName")
+        rating_tag = row.find("span", class_="minirating")
+
+        if title_tag and author_tag and rating_tag:
+
+            books.append({
+                "title": title_tag.get_text(strip=True),
+                "author": author_tag.get_text(strip=True),
+                "rating": rating_tag.get_text(strip=True)
+            })
+
+    return books[:10]
+
+
+def qa_bot(url, question):
+
+    books = scrape_goodreads(url)
+
+    if not books:
+        return "No book data found."
+
+    context = "\n".join(
+        [f"{i+1}. {b['title']} by {b['author']} - {b['rating']}"
+         for i, b in enumerate(books)]
+    )
+
+    system_prompt = f"""
+You are a helpful assistant.
+Answer ONLY using the following scraped Goodreads data.
+
+{context}
+"""
+
+    response = client.chat.completions.create(
+        model="llama-3.1-8b-instant",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": question}
+        ]
+    )
+
+    return response.choices[0].message.content
+
+
+
+# VERSION 2 — YOUTUBE QA
+
+
+def extract_video_id(url):
+
+    pattern = r"(?:v=|youtu\.be/)([a-zA-Z0-9_-]{11})"
+
+    match = re.search(pattern, url)
+
+    return match.group(1) if match else None
+
+
+def get_youtube_transcript(url):
+
+    video_id = extract_video_id(url)
+
+    if not video_id:
+        return "Invalid YouTube URL."
+
+    try:
+        transcript = YouTubeTranscriptApi.get_transcript(video_id)
+
+        full_text = " ".join([entry["text"] for entry in transcript])
+
+        return full_text
+
+    except:
+        return "No transcript available for this video."
+
+
+def youtube_qa(video_url, question):
+
+    transcript = get_youtube_transcript(video_url)
+
+    if transcript.startswith("No") or transcript.startswith("Invalid"):
+        return transcript
+
+    system_prompt = f"""
+You are a helpful assistant.
+Answer ONLY using this transcript.
+
+Transcript:
+{transcript[:6000]}
+"""
+
+    response = client.chat.completions.create(
+        model="llama-3.1-8b-instant",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": question}
+        ]
+    )
+
+    return response.choices[0].message.content
+
+
+
+# VERSION 3 — MEMORY CHAT
+
+
+def chat_with_memory(user_message, preference_text):
+
+    global conversation_history, user_preferences
+
+    if preference_text and preference_text.strip():
+
+        user_preferences["style"] = preference_text
+
+        save_json(PREF_FILE, user_preferences)
+
+    system_prompt = f"""
+You are a helpful AI assistant.
+
+User Preferences:
+{user_preferences.get("style","Default")}
+
+Follow the preferred style in all responses.
+Maintain conversational memory.
+"""
+
+    messages = [{"role": "system", "content": system_prompt}]
+
+    messages.extend(conversation_history)
+
+    messages.append({"role": "user", "content": user_message})
+
+    response = client.chat.completions.create(
+        model="llama-3.1-8b-instant",
+        messages=messages
+    )
+
+    assistant_reply = response.choices[0].message.content
+
+    conversation_history.append({"role": "user", "content": user_message})
+
+    conversation_history.append({"role": "assistant", "content": assistant_reply})
+
+    save_json(CHAT_FILE, conversation_history)
+
+    return assistant_reply
+
+
+def clear_memory():
+
+    global conversation_history
+
+    conversation_history = []
+
+    save_json(CHAT_FILE, [])
+
+    return []
+
+
+
+# VERSION 4 — VOICE AI
+
+
+def transcribe_audio(audio_path):
+
+    with open(audio_path, "rb") as audio_file:
+
+        transcription = client.audio.transcriptions.create(
+            file=audio_file,
+            model="whisper-large-v3"
+        )
+
+    return transcription.text
+
+
+def text_to_speech(text):
+
+    inputs = processor(text=text, return_tensors="pt")
+
+    speech = tts_model.generate_speech(
+        inputs["input_ids"],
+        speaker_embeddings,
+        vocoder=vocoder
+    )
+
+    temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+
+    sf.write(temp_audio.name, speech.numpy(), samplerate=16000)
+
+    return temp_audio.name
+
+
+def process_text(user_message, preference_text, chat_display):
+
+    if not user_message.strip():
+        return "", chat_display, None
+
+    assistant_reply = chat_with_memory(user_message, preference_text)
+
+    chat_display.append({"role": "user", "content": user_message})
+    chat_display.append({"role": "assistant", "content": assistant_reply})
+
+    audio_output = text_to_speech(assistant_reply)
+
+    return "", chat_display, audio_output
+
+
+def process_voice(audio_file, preference_text, chat_display):
+
+    if audio_file is None:
+        return chat_display, None
+
+    user_text = transcribe_audio(audio_file)
+
+    assistant_reply = chat_with_memory(user_text, preference_text)
+
+    chat_display.append({"role": "user", "content": user_text})
+    chat_display.append({"role": "assistant", "content": assistant_reply})
+
+    audio_output = text_to_speech(assistant_reply)
+
+    return chat_display, audio_output
+
+
+
+# GRADIO UI
+
+
+with gr.Blocks() as demo:
+
+    gr.Markdown("# 🚀 Full AI Assistant (V1 + V2 + V3 + V4)")
+
+    # TAB 1
+    with gr.Tab("🌐 Website Scraper Q&A"):
+
+        url_input = gr.Textbox(
+            label="Enter Goodreads URL",
+            value="https://www.goodreads.com/list/show/1.Best_Books_Ever"
+        )
+
+        question_input = gr.Textbox(label="Ask your question")
+
+        output1 = gr.Textbox(label="Answer")
+
+        btn1 = gr.Button("Submit")
+
+        btn1.click(
+            qa_bot,
+            inputs=[url_input, question_input],
+            outputs=output1
+        )
+
+    # TAB 2
+    with gr.Tab("🎥 YouTube Transcript Q&A"):
+
+        video_input = gr.Textbox(label="Enter YouTube URL")
+
+        yt_question = gr.Textbox(label="Ask your question")
+
+        output2 = gr.Textbox(label="Answer")
+
+        btn2 = gr.Button("Submit")
+
+        btn2.click(
+            youtube_qa,
+            inputs=[video_input, yt_question],
+            outputs=output2
+        )
+
+    # TAB 3
+    with gr.Tab("🧠 Memory Chatbot"):
+
+        chatbot = gr.Chatbot(label="Conversation", value=conversation_history)
+
+        preference_input = gr.Textbox(
+            label="User Preferences (Optional)"
+        )
+
+        user_message = gr.Textbox(label="Your Message")
+
+        send_btn = gr.Button("Send")
+
+        clear_btn = gr.Button("Clear Memory")
+
+        def chat_interface(user_message, preference_text, chat_display):
+
+            if not user_message.strip():
+                return "", chat_display
+
+            assistant_reply = chat_with_memory(user_message, preference_text)
+
+            chat_display.append({"role": "user", "content": user_message})
+
+            chat_display.append({"role": "assistant", "content": assistant_reply})
+
+            return "", chat_display
+
+        send_btn.click(
+            chat_interface,
+            inputs=[user_message, preference_input, chatbot],
+            outputs=[user_message, chatbot]
+        )
+
+        clear_btn.click(
+            clear_memory,
+            outputs=chatbot
+        )
+
+    # TAB 4
+    with gr.Tab("🎙️ Voice AI Assistant"):
+
+        chatbot_v4 = gr.Chatbot(label="Conversation", value=conversation_history)
+
+        preference_input_v4 = gr.Textbox(
+            label="User Preferences (Optional)"
+        )
+
+        user_message_v4 = gr.Textbox(label="Type your message")
+
+        audio_input = gr.Audio(
+            sources=["microphone"],
+            type="filepath",
+            label="Speak your question"
+        )
+
+        audio_output = gr.Audio(label="Assistant Voice Response")
+
+        send_btn_v4 = gr.Button("Send Text")
+
+        voice_btn = gr.Button("Send Voice")
+
+        clear_btn_v4 = gr.Button("Clear Memory")
+
+        send_btn_v4.click(
+            process_text,
+            inputs=[user_message_v4, preference_input_v4, chatbot_v4],
+            outputs=[user_message_v4, chatbot_v4, audio_output]
+        )
+
+        voice_btn.click(
+            process_voice,
+            inputs=[audio_input, preference_input_v4, chatbot_v4],
+            outputs=[chatbot_v4, audio_output]
+        )
+
+        clear_btn_v4.click(
+            clear_memory,
+            outputs=chatbot_v4
+        )
+
+
+if __name__ == "__main__":
+
+    demo.launch()

chat_history.json

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+[
+    {
+        "role": "user",
+        "content": " In the hall of."
+    },
+    {
+        "role": "assistant",
+        "content": "mirrors, I sense a sense of unease. What draws you to this place, with its endless reflections and whispers of the past?"
+    },
+    {
+        "role": "user",
+        "content": "can you tell me my name?"
+    },
+    {
+        "role": "assistant",
+        "content": "I'm a little curious now, but unfortunately, I don't have any information about your identity. We just started our little conversation, and I don't seem to recall any previous encounters. Can you give me a hint or tell me your name yourself? I'm all ears... or should I say, all circuitry?"
+    }
+]

preferences.json

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+{
+    "style": "hello"
+}

requirements.txt

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+gradio
+groq
+requests
+beautifulsoup4
+python-dotenv
+youtube-transcript-api
+gradio
+groq
+openai
+python-dotenv
+gradio
+groq
+python-dotenv
+torch
+transformers
+datasets
+soundfile