Create app.py

app.py

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+import os
+from groq import Groq
+import pandas as pd
+import speech_recognition as sr
+import pyttsx3
+import streamlit as st
+
+# API_KEY
+API = "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"
+
+# Set up the Groq client
+client = Groq(api_key=API)
+
+# Function to process user input with Llama model
+def process_prompt(prompt, model="llama-3.3-70b-versatile"):
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model=model,
+        stream=False
+    )
+    return chat_completion.choices[0].message.content
+
+# Function to process uploaded files
+def process_file(file):
+    if file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
+        data = pd.read_excel(file)
+    elif file.type == "text/csv":
+        data = pd.read_csv(file)
+    else:
+        return "Unsupported file format."
+    return data
+
+# Function to generate human-friendly responses for file data
+def analyze_file_data(data):
+    summary = f"File contains {data.shape[0]} rows and {data.shape[1]} columns."
+    response = f"""
+    **File Analysis:**
+    - 📄 Total Rows: {data.shape[0]}
+    - 📊 Total Columns: {data.shape[1]}
+    - First Few Rows:
+    {data.head().to_markdown()}
+    """
+    return response
+
+# Function for speech-to-text
+def speech_to_text():
+    recognizer = sr.Recognizer()
+    with sr.Microphone() as source:
+        st.write("🎙️ Listening...")
+        audio = recognizer.listen(source)
+    try:
+        return recognizer.recognize_google(audio)
+    except sr.UnknownValueError:
+        return "Sorry, I didn't catch that."
+
+# Function for text-to-speech
+def text_to_speech(text):
+    engine = pyttsx3.init()
+    engine.say(text)
+    engine.runAndWait()
+
+# Streamlit UI
+def chatbot_ui():
+    st.title("📢 Real-Time AI Chatbot")
+
+    # File upload feature
+    st.sidebar.header("📂 File Management")
+    uploaded_file = st.sidebar.file_uploader("Upload a file (CSV/Excel)", type=["csv", "xlsx"])
+    if uploaded_file:
+        data = process_file(uploaded_file)
+        st.sidebar.write(analyze_file_data(data))
+        if st.sidebar.button("Delete File"):
+            uploaded_file = None
+            st.sidebar.write("File deleted.")
+
+    # Chat section
+    st.header("💬 Chat Section")
+    chat_history = st.session_state.get("chat_history", [])
+    
+    with st.form("chat_form", clear_on_submit=True):
+        user_input = st.text_input("Type your message or prompt here...")
+        submitted = st.form_submit_button("Send")
+        if submitted and user_input:
+            response = process_prompt(user_input)
+            chat_history.append(("User", user_input))
+            chat_history.append(("Bot", response))
+            st.session_state["chat_history"] = chat_history
+
+    # Display chat history
+    for sender, message in chat_history:
+        if sender == "User":
+            st.write(f"**👤 You:** {message}")
+        else:
+            st.write(f"**🤖 Bot:** {message}")
+
+    # Save/download chat
+    if st.button("Download Chat"):
+        chat_file = "\n".join([f"{sender}: {message}" for sender, message in chat_history])
+        st.download_button("Download", chat_file, "chat_history.txt", "text/plain")
+
+    # Speech-to-Text Section
+    st.header("🎙️ Speech-to-Text")
+    if st.button("Start Speech Recognition"):
+        st.write(f"**👂 You said:** {speech_to_text()}")
+
+    # Text-to-Speech Section
+    st.header("🔊 Text-to-Speech")
+    tts_text = st.text_input("Enter text to convert to speech:")
+    if st.button("Speak Text"):
+        text_to_speech(tts_text)
+
+if __name__ == "__main__":
+    chatbot_ui()