Update app.py

app.py

@@ -1,63 +1,95 @@
 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
+import json
+import os
+import requests
+
+# Load Google Sheets credentials from secrets
+creds_json = os.getenv("GOOGLE_SHEETS_KEY_JSON")
+creds_dict = json.loads(creds_json)
+
+# Google Sheets setup
+scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
+creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_dict, scope)
+client = gspread.authorize(creds)
+sheet = client.open("QF - Quant Request").sheet1  # Use the correct sheet name
+
+def validate_repo(link):
+    """Check if the repository exists on Hugging Face."""
+    # Extract repo_id from the link (format: {username}/{model_name} or {dataset_name})
+    repo_id = link.split("https://huggingface.co/")[-1].strip("/")
+    url = f"https://huggingface.co/api/models/{repo_id}"  # Adjust the URL for datasets if needed
+    
+    response = requests.head(url)
+    return response.status_code == 200
+
+def check_quant_exists(link):
+    """Check if the quantization request already exists in the Google Sheet."""
+    extracted_text = link.split("https://huggingface.co/")[-1]
+    existing_texts = sheet.col_values(1)  # Assuming the extracted text is in the first column
+    return extracted_text in existing_texts
+
+def check_quant_factory_quant_exists(link):
+    """Check if a GGUF quantized model exists in the QuantFactory repository."""
+    # Extract the model name from the original link
+    model_name = link.split('/')[-1]  # Get the last item from the split list, which is the model name
+
+    # Create the QuantFactory GGUF quant link
+    quant_factory_link = f"https://huggingface.co/QuantFactory/{model_name}-GGUF"
+
+    # Check if the QuantFactory GGUF repo exists
+    response = requests.head(quant_factory_link)
+
+    if response.status_code == 200:
+        return True, quant_factory_link
+    else:
+        return False, quant_factory_link
+
+def submit_link(link):
+    # Normalize the input link
+    if not link.startswith("https://huggingface.co"):
+        link = f"https://huggingface.co/{link}"
+    
+    # Validate the repo
+    if not validate_repo(link):
+        return "Invalid model or repository link. Please provide a valid Hugging Face link."
+
+    # Check if the GGUF quantized model already exists in QuantFactory
+    quant_exists, quant_link = check_quant_factory_quant_exists(link)
+    if quant_exists:
+        return f"Quant already exists at [QuantFactory/{link.split('/')[-1]}-GGUF]({quant_link})."
+
+    # Check if the quant request already exists in the Google Sheet
+    if check_quant_exists(link):
+        return "Quant requests have already been made for this model."
+
+    # Extract text after "huggingface.co/"
+    extracted_text = link.split("https://huggingface.co/")[-1]
+    
+    # Append the row with the extracted text and default status "Pending"
+    row_index = len(sheet.get_all_values()) + 1
+    sheet.append_row(["", ""])  # Append an empty row first to ensure correct row index
+    
+    # Set the hyperlink formula in the first column
+    sheet.update_cell(row_index, 1, f'=HYPERLINK("{link}", "{extracted_text}")')
+    
+    # Copy content from cell B2 to the new row in column B
+    b2_value = sheet.cell(2, 2).value
+    sheet.update_cell(row_index, 2, b2_value)
+
+    return "Request submitted successfully."
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1):  # Left column
+            gr.Markdown("## QuantFactory - Model Request")
+            link_input = gr.Textbox(label="Hugging Face Link")
+            submit_button = gr.Button("Submit")
+            result = gr.Textbox(label="Result", interactive=False)
+            submit_button.click(fn=submit_link, inputs=link_input, outputs=result)
+        with gr.Column(scale=1):  # Right column
+            gr.Image("image.png")  # Display the image on the right
+
+demo.launch()