Update app.py

app.py

@@ -1,63 +1,104 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import LLaMAForConditionalGeneration, LLaMATokenizer
+import torch
+import pandas as pd
+from PyPDF2 import PdfFileReader
+from googleapiclient.discovery import build
+from google_auth_oauthlib.flow import InstalledAppFlow
+from google.auth.transport.requests import Request
+import pickle
+import pydub
+
+# Set up LLaMA model and tokenizer
+model = LLaMAForConditionalGeneration.from_pretrained("facebook/llama-3.1-base")
+tokenizer = LLaMATokenizer.from_pretrained("facebook/llama-3.1-base")
+
+# Set up Google API credentials
+SCOPES = ['https://www.googleapis.com/auth/drive']
+creds = None
+if creds is None or not creds.valid:
+    if creds and creds.expired and creds.refresh_token:
+        creds.refresh(Request())
+    else:
+        flow = InstalledAppFlow.from_client_secrets_file(
+            'credentials.json', SCOPES)
+        creds = flow.run_local_server(port=0)
+drive_service = build('drive', 'v3', credentials=creds)
+
+# Define function to process uploaded files
+def process_file(file):
+    if file.name.endswith('.pdf'):
+        pdf_file = PdfFileReader(file)
+        text = ''
+        for page in range(pdf_file.numPages):
+            text += pdf_file.getPage(page).extractText()
+        return text
+    elif file.name.endswith('.csv') or file.name.endswith('.xlsx'):
+        if file.name.endswith('.csv'):
+            df = pd.read_csv(file)
+        else:
+            df = pd.read_excel(file)
+        return str(df)
+    elif file.name.endswith('.docx'):
+        # You need to implement a function to extract text from Word documents
+        # For simplicity, this example just returns an error message
+        return "Error: Word document support not implemented"
+    elif file.name.endswith('.gsheet'):
+        spreadsheet_id = file.name.split('/')[-1]
+        range_name = 'Sheet1!A1:Z1000'  # You can change this range as needed
+        service = build('sheets', 'v4', credentials=creds)
+        sheet = service.spreadsheets()
+        result = sheet.values().get(spreadsheetId=spreadsheet_id,
+                                    range=range_name).execute()
+        values = result.get('values', [])
+        return str(values)
+    elif file.name.endswith('.gdoc'):
+        document_id = file.name.split('/')[-1]
+        service = build('docs', 'v1', credentials=creds)
+        doc = service.documents().get(documentId=document_id).execute()
+        text = ''
+        for element in doc.get('body').get('content'):
+            if 'paragraph' in element:
+                text += element.get('paragraph').get('elements')[0].get('textRun').get('content')
+        return text
+    elif file.name.endswith('.mp3'):
+        audio = pydub.AudioSegment.from_mp3(file)
+        text = ''
+        # You need to implement a function to transcribe audio
+        # For simplicity, this example just returns an error message
+        return "Error: Audio transcription support not implemented"
+    else:
+        return "Error: File type not supported"
+
+# Define function to answer questions about the uploaded content
+def answer_question(content, question):
+    inputs = tokenizer.encode(question, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=answer_question,
+    inputs=["file", "text"],
+    outputs="text",
+    title="LLaMA Chatbot",
+    description="Upload a file or paste some text, and ask a question about the content.",
 )
 
+# Define function to update the Gradio interface with the uploaded file's content
+def update_interface(file):
+    content = process_file(file)
+    demo.update(inputs=[content])
+
+# Create Gradio interface with file upload
+demo_with_upload = gr.Interface(
+    fn=update_interface,
+    inputs=["file"],
+    outputs=None,
+    title="LLaMA Chatbot",
+    description="Upload a file to analyze its content.",
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch Gradio interface
+demo_with_upload.launch()