Update app.py

app.py

@@ -1,84 +1,92 @@
 import gradio as gr
-import openai
 import os
+import openai
 import pdfplumber
 import docx
 import pandas as pd
 from PIL import Image
-import whisper
+from io import BytesIO
+import base64
 import tempfile
+import whisper
 
-# Load Whisper model for transcription
-whisper_model = whisper.load_model("base")
+openai.api_key = os.environ.get("GROQ_API_KEY")
 
-# Set your OpenAI API key
-openai.api_key = os.getenv("OPENAI_API_KEY")
+# Load Whisper model
+whisper_model = whisper.load_model("base")
 
-# Helper functions
 def extract_text_from_file(file):
-    file_name = file.name
-    if file_name.endswith(".pdf"):
-        with pdfplumber.open(file_name) as pdf:
-            return "\n".join([page.extract_text() or "" for page in pdf.pages])
-    elif file_name.endswith(".docx"):
-        doc = docx.Document(file_name)
-        return "\n".join([para.text for para in doc.paragraphs])
-    elif file_name.endswith(".csv"):
-        df = pd.read_csv(file_name)
-        return df.to_string()
-    elif file_name.endswith(".xlsx"):
-        df = pd.read_excel(file_name)
-        return df.to_string()
-    elif file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
-        return "[Image received. Please describe what you'd like me to do with it.]"
+    if file.name.endswith(".pdf"):
+        with pdfplumber.open(file.name) as pdf:
+            text = "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())
+    elif file.name.endswith(".docx"):
+        doc = docx.Document(file.name)
+        text = "\n".join(p.text for p in doc.paragraphs)
+    elif file.name.endswith(".xlsx"):
+        df = pd.read_excel(file.name)
+        text = df.to_string()
+    elif file.name.endswith((".png", ".jpg", ".jpeg")):
+        img = Image.open(file.name)
+        buffer = BytesIO()
+        img.save(buffer, format="PNG")
+        encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
+        text = f"[Image uploaded: data:image/png;base64,{encoded[:100]}... (truncated)]"
     else:
-        return "[Unsupported file format.]"
+        with open(file.name, "r", encoding="utf-8", errors="ignore") as f:
+            text = f.read()
+    return text
 
 def transcribe_audio(audio_path):
-    try:
-        result = whisper_model.transcribe(audio_path)
-        return result["text"]
-    except Exception as e:
-        return f"Transcription error: {str(e)}"
+    result = whisper_model.transcribe(audio_path)
+    return result["text"]
 
-def chat_with_openai(messages):
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages
-        )
-        return response.choices[0].message.content.strip()
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
+def generate_reply(history):
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
 
-def respond(user_message, chatbot, file_upload, audio_input):
-    messages = chatbot.copy() if chatbot else []
+    response = openai.ChatCompletion.create(
+        model="llama3-8b-8192",
+        messages=messages,
+        temperature=0.7
+    )
+    reply = response.choices[0].message.content
+    return reply
 
-    # Add user message or file content
-    if file_upload is not None:
-        file_content = extract_text_from_file(file_upload)
-        messages.append({"role": "user", "content": f"File uploaded. Content:\n{file_content}"})
-    elif audio_input is not None:
-        transcript = transcribe_audio(audio_input)
-        messages.append({"role": "user", "content": transcript})
-    else:
-        messages.append({"role": "user", "content": user_message})
+def respond(message, history):
+    reply = generate_reply(history + [[message, ""]])
+    history.append([message, reply])
+    return history, ""
+
+def handle_file_upload(file, message):
+    if file is None:
+        return message
+    file_content = extract_text_from_file(file)
+    return f"{message}\n\n--- File Content Start ---\n{file_content}\n--- File Content End ---"
+
+def handle_audio_upload(audio, message):
+    if audio is None:
+        return message
+    transcription = transcribe_audio(audio)
+    return f"{message}\n\n--- Transcription ---\n{transcription}"
+
+with gr.Blocks(css="body { background-color: white; color: black }") as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Neobot</h1>")
+    chatbot = gr.Chatbot(label="Chat", elem_id="chatbox", height=450, type="messages")
 
-    reply = chat_with_openai(messages)
-    messages.append({"role": "assistant", "content": reply})
-    return messages, None, None  # Clear inputs after response
+    with gr.Row():
+        txt = gr.Textbox(placeholder="Type a message or edit transcribed/file content here...", scale=5, show_label=False)
+        send_btn = gr.Button("Send", scale=1)
 
-# Gradio Interface
-with gr.Blocks(css=".gradio-container { font-family: 'Segoe UI'; }") as demo:
-    gr.Markdown("# 🤖 Neobot — Your AI Assistant")
+    with gr.Row():
+        upload_btn = gr.File(label="📎 Upload File", file_types=[".pdf", ".docx", ".txt", ".xlsx", ".png", ".jpg", ".jpeg"])
+        audio_in = gr.Audio(label="🎙️ Upload Audio", type="filepath")
 
-    chatbot = gr.Chatbot(type="messages", elem_id="chatbox", label="Chat")
-    user_input = gr.Textbox(placeholder="Type a message...", label=None)
-    file_upload = gr.File(label="📎 Upload File", file_types=[".pdf", ".docx", ".csv", ".xlsx", ".jpg", ".jpeg", ".png"])
-    audio_input = gr.Audio(type="filepath", label="🎤 Record Voice")
-    submit_btn = gr.Button("Send")
+    history = gr.State([])
 
-    submit_btn.click(respond, [user_input, chatbot, file_upload, audio_input], [chatbot, file_upload, audio_input])
+    send_btn.click(respond, [txt, history], [chatbot, txt])
+    upload_btn.change(handle_file_upload, [upload_btn, txt], txt)
+    audio_in.change(handle_audio_upload, [audio_in, txt], txt)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()