Delete app.py

app.py

@@ -1,198 +0,0 @@
-import gradio as gr
-import os
-import requests
-from gtts import gTTS
-import tempfile
-import uuid
-import datetime
-from fpdf import FPDF
-import json
-import whisper
-
-# Load Whisper model
-model = whisper.load_model("base")
-
-# Gemini setup
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-
-def gemini_chat(text):
-    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key={GEMINI_API_KEY}"
-    headers = {"Content-Type": "application/json"}
-    payload = {"contents": [{"parts": [{"text": text}]}]}
-    res = requests.post(url, headers=headers, json=payload)
-    if res.ok:
-        return res.json()["candidates"][0]["content"]["parts"][0]["text"]
-    return "[Gemini error]"
-
-# PDF generation
-def generate_pdf(chat_history, session_id):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.set_auto_page_break(True, margin=10)
-    for turn in chat_history:
-        pdf.multi_cell(0, 10, f"You: {turn['user']}\nAI: {turn['ai']}\n")
-    path = f"{session_id}_chat.pdf"
-    pdf.output(path)
-    return path
-
-# Main function
-def voice_chat(audio_file, chat_history, session_id):
-    if not audio_file:
-        return None, chat_history, session_id, None
-
-    if not session_id:
-        session_id = str(uuid.uuid4())
-
-    # Transcribe
-    result = model.transcribe(audio_file)
-    user_text = result["text"]
-
-    # Gemini response
-    ai_reply = gemini_chat(user_text)
-
-    # TTS
-    tts = gTTS(ai_reply)
-    tts_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
-    tts.save(tts_path.name)
-
-    # Update chat
-    chat_history.append({"user": user_text, "ai": ai_reply})
-
-    # Format for display
-    formatted = ""
-    for turn in chat_history:
-        formatted += f"🗣️ **You**: {turn['user']}\n\n🤖 **AI**: {turn['ai']}\n\n"
-
-    return tts_path.name, chat_history, session_id, formatted
-
-# PDF button handler
-def download_pdf(chat_history, session_id):
-    if not chat_history or not session_id:
-        return None
-    return generate_pdf(chat_history, session_id)
-
-# UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 🎙️ Real-Time Voice Chat with AI (Gemini)")
-
-    session_id = gr.State()
-    chat_history = gr.State([])
-
-    with gr.Row():
-        mic = gr.Audio(type="filepath", label="🎤 Speak (up to 30s)")
-        submit_btn = gr.Button("Send")
-
-    with gr.Row():
-        audio_out = gr.Audio(label="🔊 AI Response")
-        chatbox = gr.Markdown()
-
-    with gr.Row():
-        pdf_btn = gr.Button("📥 Download Chat as PDF")
-        pdf_file = gr.File()
-
-    submit_btn.click(fn=voice_chat, inputs=[mic, chat_history, session_id],
-                     outputs=[audio_out, chat_history, session_id, chatbox])
-
-    pdf_btn.click(fn=download_pdf, inputs=[chat_history, session_id], outputs=[pdf_file])
-
-demo.launch()
-import gradio as gr
-import os
-import requests
-from gtts import gTTS
-import tempfile
-import uuid
-import datetime
-from fpdf import FPDF
-import json
-import whisper
-
-# Load Whisper model
-model = whisper.load_model("base")
-
-# Gemini setup
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-
-def gemini_chat(text):
-    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key={GEMINI_API_KEY}"
-    headers = {"Content-Type": "application/json"}
-    payload = {"contents": [{"parts": [{"text": text}]}]}
-    res = requests.post(url, headers=headers, json=payload)
-    if res.ok:
-        return res.json()["candidates"][0]["content"]["parts"][0]["text"]
-    return "[Gemini error]"
-
-# PDF generation
-def generate_pdf(chat_history, session_id):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.set_auto_page_break(True, margin=10)
-    for turn in chat_history:
-        pdf.multi_cell(0, 10, f"You: {turn['user']}\nAI: {turn['ai']}\n")
-    path = f"{session_id}_chat.pdf"
-    pdf.output(path)
-    return path
-
-# Main function
-def voice_chat(audio_file, chat_history, session_id):
-    if not audio_file:
-        return None, chat_history, session_id, None
-
-    if not session_id:
-        session_id = str(uuid.uuid4())
-
-    # Transcribe
-    result = model.transcribe(audio_file)
-    user_text = result["text"]
-
-    # Gemini response
-    ai_reply = gemini_chat(user_text)
-
-    # TTS
-    tts = gTTS(ai_reply)
-    tts_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
-    tts.save(tts_path.name)
-
-    # Update chat
-    chat_history.append({"user": user_text, "ai": ai_reply})
-
-    # Format for display
-    formatted = ""
-    for turn in chat_history:
-        formatted += f"🗣️ **You**: {turn['user']}\n\n🤖 **AI**: {turn['ai']}\n\n"
-
-    return tts_path.name, chat_history, session_id, formatted
-
-# PDF button handler
-def download_pdf(chat_history, session_id):
-    if not chat_history or not session_id:
-        return None
-    return generate_pdf(chat_history, session_id)
-
-# UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 🎙️ Real-Time Voice Chat with AI (Gemini)")
-
-    session_id = gr.State()
-    chat_history = gr.State([])
-
-    with gr.Row():
-        mic = gr.Audio(type="filepath", label="🎤 Speak (up to 30s)")
-        submit_btn = gr.Button("Send")
-
-    with gr.Row():
-        audio_out = gr.Audio(label="🔊 AI Response")
-        chatbox = gr.Markdown()
-
-    with gr.Row():
-        pdf_btn = gr.Button("📥 Download Chat as PDF")
-        pdf_file = gr.File()
-
-    submit_btn.click(fn=voice_chat, inputs=[mic, chat_history, session_id],
-                     outputs=[audio_out, chat_history, session_id, chatbox])
-
-    pdf_btn.click(fn=download_pdf, inputs=[chat_history, session_id], outputs=[pdf_file])
-
-demo.launch()