Update app.py

app.py

@@ -1,82 +1,108 @@
 import gradio as gr
-import whisper
+import openai
 import os
-import uuid
+import tempfile
+import whisper
 import pdfplumber
 import docx
 import pandas as pd
 from PIL import Image
 
-# Load Whisper model locally
+# Load Whisper model
 whisper_model = whisper.load_model("base")
 
-def transcribe_audio(audio):
-    if audio is None:
-        return "⚠️ No audio file uploaded."
+# Set OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
-    try:
-        result = whisper_model.transcribe(audio)
-        return result["text"]
-    except Exception as e:
-        return f"❌ Whisper error: {str(e)}"
+# Global store for chat messages
+chat_history = []
 
+# --- File Processor ---
 def extract_text_from_file(file):
     if file is None:
-        return "⚠️ No file uploaded."
+        return ""
+    filename = file.name
+    ext = os.path.splitext(filename)[-1].lower()
+
+    if ext == ".pdf":
+        with pdfplumber.open(file) as pdf:
+            return "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())
+
+    elif ext in [".doc", ".docx"]:
+        doc = docx.Document(file)
+        return "\n".join([para.text for para in doc.paragraphs])
 
-    name, ext = os.path.splitext(file.name)
-    ext = ext.lower()
+    elif ext in [".xls", ".xlsx"]:
+        df = pd.read_excel(file)
+        return df.to_string(index=False)
 
+    elif ext in [".png", ".jpg", ".jpeg"]:
+        image = Image.open(file)
+        return "Image uploaded. Please ask a question about the image."
+
+    else:
+        return file.read().decode("utf-8")
+
+
+# --- Transcription ---
+def transcribe_audio(audio_file):
+    if audio_file is None:
+        return ""
     try:
-        if ext == ".pdf":
-            with pdfplumber.open(file) as pdf:
-                text = "\n".join(page.extract_text() or "" for page in pdf.pages)
-        elif ext in [".docx"]:
-            doc = docx.Document(file)
-            text = "\n".join(paragraph.text for paragraph in doc.paragraphs)
-        elif ext in [".xlsx", ".xls"]:
-            df = pd.read_excel(file)
-            text = df.to_string(index=False)
-        elif ext in [".png", ".jpg", ".jpeg"]:
-            image = Image.open(file)
-            text = "🖼️ Image uploaded. Please describe what you'd like me to do with it."
-        else:
-            text = "⚠️ Unsupported file type."
-        return text
+        result = whisper_model.transcribe(audio_file)
+        return result['text']
     except Exception as e:
-        return f"❌ File processing error: {str(e)}"
-
-def chatbot_response(message, chat_history):
-    # Echo-style placeholder response
-    bot_reply = f"🤖 You said: {message}"
-    chat_history.append((message, bot_reply))
-    return "", chat_history
-
-with gr.Blocks(css="""
-#chatbox { height: 500px; overflow: auto; }
-.message-input { height: 40px; border-radius: 6px; }
-.sidebar { width: 25%; overflow-y: auto; }
-.main { width: 75%; }
-""") as demo:
-    gr.Markdown("""<h2 style="text-align:center;">🤖 Neobot - Always Listening</h2>""")
+        return f"❌ Transcription error: {str(e)}"
 
-    with gr.Row():
-        with gr.Column(scale=1, min_width=200):
-            saved_chats = gr.Textbox(label="Saved Chats (auto-listed soon)", interactive=False)
-        with gr.Column(scale=4):
-            chatbot = gr.Chatbot(elem_id="chatbox")
+
+# --- Chatbot Logic ---
+def chat_with_gpt(message, uploaded_file):
+    file_text = extract_text_from_file(uploaded_file)
+
+    system_prompt = """
+    You are Neobot, an intelligent assistant that can answer questions, transcribe audio, and analyze uploaded documents.
+    """
+
+    user_prompt = message
+    if file_text:
+        user_prompt = f"This is the content of the uploaded file:\n{file_text}\n\nUser's question: {message}"
+
+    chat_history.append({"role": "user", "content": user_prompt})
+
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4o",
+            messages=[{"role": "system", "content": system_prompt}] + chat_history
+        )
+        reply = response.choices[0].message.content
+        chat_history.append({"role": "assistant", "content": reply})
+        return [(msg['content'], None) if msg['role'] == 'user' else (None, msg['content']) for msg in chat_history]
+    except Exception as e:
+        return [[(None, f"❌ Error: {str(e)}")]]
+
+
+# --- Gradio UI ---
+with gr.Blocks(css="body { background-color: white; color: black; }") as demo:
+    gr.Markdown("""<h1 style='text-align: center;'>Neobot</h1>""")
+    chatbot = gr.Chatbot(type="messages", elem_id="chatbox")
 
     with gr.Row():
-        msg = gr.Textbox(placeholder="Type here or use mic...", elem_classes="message-input")
-        send_btn = gr.Button("Send")
+        msg_box = gr.Textbox(placeholder="Type a message...", show_label=False, scale=8)
+        record_btn = gr.Audio(type="filepath", label="Record Voice", show_label=False, scale=2)
 
     with gr.Row():
-        record_btn = gr.Audio(source="microphone", type="filepath", label="Record Voice")
-        upload_btn = gr.File(label="Upload File", file_types=[".pdf", ".docx", ".xlsx", ".xls", ".jpg", ".jpeg", ".png"])
+        upload_file = gr.File(label="Upload File", file_types=[".pdf", ".docx", ".xlsx", ".txt", ".png", ".jpg"])
+
+    # --- Callbacks ---
+    def handle_voice_input(audio):
+        text = transcribe_audio(audio)
+        return text
+
+    def respond(message, file):
+        return chat_with_gpt(message, file)
 
-    # Event functions
-    send_btn.click(fn=chatbot_response, inputs=[msg, chatbot], outputs=[msg, chatbot])
-    record_btn.change(fn=transcribe_audio, inputs=record_btn, outputs=msg)
-    upload_btn.change(fn=extract_text_from_file, inputs=upload_btn, outputs=msg)
+    record_btn.change(handle_voice_input, inputs=record_btn, outputs=msg_box)
+    msg_box.submit(respond, [msg_box, upload_file], chatbot)
 
+# --- Launch ---
 demo.launch()