testing out

.gradio/flagged/dataset1.csv

@@ -0,0 +1,2 @@
+name,output,timestamp
+yash,Hello yash!!,2025-03-17 01:21:30.390134

.ipynb_checkpoints/app-checkpoint.ipynb

@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_id =  \"google/gemma-3-12b-it\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "processor = AutoProcessor.from_pretrained(model_id, padding_side=\"left\")\n",
+    "model = Gemma3ForConditionalGeneration.from_pretrained(\n",
+    "    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16, attn_implementation=\"eager\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97908a20851e455c895464dc7228dbb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8011adeae5247f89612a93c3ff9e963",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00001-of-00002.safetensors:   2%|1         | 83.9M/4.96G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b604d0bd3be24ddda117cc36a8752c8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00002-of-00002.safetensors:   3%|3         | 126M/3.64G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Error while downloading from https://cdn-lfs-us-1.hf.co/repos/83/76/8376859a3a783fbbf8c6b8aff73e386e0379657f480bf946d481f9a936d4ceab/fdde0e5aa5ced0fa203b3d50f4ab78168b7e3a3e08c6349f5cc9326666e1bb13?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27model-00002-of-00002.safetensors%3B+filename%3D%22model-00002-of-00002.safetensors%22%3B&Expires=1742502952&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0MjUwMjk1Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzgzLzc2LzgzNzY4NTlhM2E3ODNmYmJmOGM2YjhhZmY3M2UzODZlMDM3OTY1N2Y0ODBiZjk0NmQ0ODFmOWE5MzZkNGNlYWIvZmRkZTBlNWFhNWNlZDBmYTIwM2IzZDUwZjRhYjc4MTY4YjdlM2EzZTA4YzYzNDlmNWNjOTMyNjY2NmUxYmIxMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=h9yYRgT8Ui5-lDdTma5cMxNLJQn3a5qkQb1B9ICLWd2hPe68qnwhgkSyyze48E9LkP%7EstZozqs-6jQFBLFnbShc098FQdwXXw4NonedJMgex2loENeJ4Juaw7JVdhyUo1J1k8BM0j3t97kO%7ESsL2XN7mJ-DdqQqr6bbD5KmD3kxwaEMOt0Bo9EXSt%7EPUxFV1v9Z91m2XrJjjmgLKZEOY05K8p15-3VVhIFlQe1PcGVb72lpp3nMaXgPFoMJVY6NUVKIhszzJDFc6JhBfeO31Rb6Q9YmO-mm8duFSRCxUhqYzVMx-E4MN0iJO3lMrK0aTDXm6Ps8Fk1v8Rk4Bj6auew__&Key-Pair-Id=K24J24Z295AEI9: HTTPSConnectionPool(host='cdn-lfs-us-1.hf.co', port=443): Read timed out.\n",
+      "Trying to resume download...\n",
+      "Error while downloading from https://cdn-lfs-us-1.hf.co/repos/83/76/8376859a3a783fbbf8c6b8aff73e386e0379657f480bf946d481f9a936d4ceab/eb5fd5e97ddd07b56778733e9653c07312529cb00980a318fc3e1c4e3b5a8f1f?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27model-00001-of-00002.safetensors%3B+filename%3D%22model-00001-of-00002.safetensors%22%3B&Expires=1742502952&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0MjUwMjk1Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzgzLzc2LzgzNzY4NTlhM2E3ODNmYmJmOGM2YjhhZmY3M2UzODZlMDM3OTY1N2Y0ODBiZjk0NmQ0ODFmOWE5MzZkNGNlYWIvZWI1ZmQ1ZTk3ZGRkMDdiNTY3Nzg3MzNlOTY1M2MwNzMxMjUyOWNiMDA5ODBhMzE4ZmMzZTFjNGUzYjVhOGYxZj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=Vr9B3wdQIVtGgOocVA7IUl3gNH6pmGYUaeZ4TnqxQz7uO3BAXFQDsIcffFn1hePw-huNkRXwKBLMiPc7wD%7E6nAi4INXD7ydOfb8NDcIuzk0TZfHm0d4RheWz14OWuD4gohXh1QAa%7EF7dxn0X2OvOJ1C8hPlElE6G8g9NtGYncfWCASAkjsmFhyGMDg8i5MAt5hU5oscdJ0gpeckc1OjRNdqEISslG2jHSiTBeEn18UIyLMB3OGSxuaiguPoGPFpBUCTIkeSkUFbWCnnwOLkO7gD6hLCVw9F%7EiBZN4r92VdBlWCeRb6A%7ELH46Vo480H742XAACEEdXrqHSI2VWq9xAA__&Key-Pair-Id=K24J24Z295AEI9: HTTPSConnectionPool(host='cdn-lfs-us-1.hf.co', port=443): Read timed out.\n",
+      "Trying to resume download...\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "import torch\n",
+    "\n",
+    "pipe = pipeline(\n",
+    "    \"image-text-to-text\",\n",
+    "    model=\"google/gemma-3-4b-it\",\n",
+    "    device=\"cuda\",\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    "    cache_dir=\"F:\\\\huggingface_cache\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install accelerate\n",
+    "print(\"Hi\")\n",
+    "from transformers import AutoProcessor, Gemma3ForConditionalGeneration\n",
+    "import requests\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "\n",
+    "print(\"Done\")\n",
+    "model_id = \"google/gemma-3-4b-it\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "pip install bitsandbytes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import Gemma3ForConditionalGeneration, AutoProcessor\n",
+    "from transformers import BitsAndBytesConfig\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "model_id = \"google/gemma-3-4b-it\"\n",
+    "\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_8bit=True)\n",
+    "# Load the model and move it to the correct device\n",
+    "model = Gemma3ForConditionalGeneration.from_pretrained(\n",
+    "    model_id,\n",
+    "    cache_dir=\"F:\\\\huggingface_cache\",\n",
+    "    device_map=\"auto\",  # Automatically assigns layers to available devices\n",
+    "    quantization_config=quantization_config\n",
+    ").eval()\n",
+    "\n",
+    "# Load the processor\n",
+    "processor = AutoProcessor.from_pretrained(model_id, cache_dir=\"F:\\\\huggingface_cache\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"system\",\n",
+    "        \"content\": [{\"type\": \"text\", \"text\": \"You are a helpful assistant.\"}]\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            \n",
+    "            {\"type\": \"text\", \"text\": \"Whats the color of sky?.\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = processor.apply_chat_template(\n",
+    "    messages, add_generation_prompt=True, tokenize=True,\n",
+    "    return_dict=True, return_tensors=\"pt\"\n",
+    ").to(model.device, dtype=torch.bfloat16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_len = inputs[\"input_ids\"].shape[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.inference_mode():\n",
+    "    generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)\n",
+    "    generation = generation[0][input_len:]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "with torch.inference_mode():\n",
+    "    generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)\n",
+    "    generation = generation[0][input_len:]\n",
+    "\n",
+    "decoded = processor.decode(generation, skip_special_tokens=True)\n",
+    "print(decoded)\n",
+    "\n",
+    "# **Overall Impression:** The image is a close-up shot of a vibrant garden scene, \n",
+    "# focusing on a cluster of pink cosmos flowers and a busy bumblebee. \n",
+    "# It has a slightly soft, natural feel, likely captured in daylight.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "access_token='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'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use a pipeline as a high-level helper\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "messages = [\n",
+    "    {\"role\": \"user\", \"content\": \"Who are you?\"},\n",
+    "]\n",
+    "pipe = pipeline(\"image-text-to-text\", model=\"google/gemma-3-4b-it\")\n",
+    "pipe(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hf_env_kernel",
+   "language": "python",
+   "name": "hf_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

app.py

@@ -1,7 +1,141 @@
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration
 import gradio as gr
+# from PIL import Image
+# import requests
+# import torch
+# import os
+# from transformers import Gemma3ForConditionalGeneration, AutoProcessor
+# print("hey")
+# # Set the cache directory
+# cache_dir = "F:\\huggingface_cache"
 
+# # Set environment variables for good measure
+# # os.environ["TRANSFORMERS_CACHE"] = cache_dir
+# # os.environ["HF_HOME"] = cache_dir
+# # os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir
+
+# # Model ID
+# model_id = "gemma3:latest"
+
+# from ollama import chat
+# from ollama import ChatResponse
+
+# def _get_response(message):
+#     messages = [
+#         {
+#             'role': 'user',
+#             'content': message,
+#         },
+#     ]
+#     response: ChatResponse = chat(model=model_id, messages=messages)
+#     return response.message.content
+
+
+
+# import requests
+# import base64
+# # Function to encode image to Base64
+# def encode_image_to_base64(image_path):
+#     with open(image_path, "rb") as image_file:
+#         return base64.b64encode(image_file.read()).decode("utf-8")
+
+# def image_process():
+#     image_path = r"F:\HF\gemma-examples\WhatsApp Image 2025-03-21 at 10.05.06 PM.jpeg"  # Replace with your image path
+
+#     # Encode the image
+#     image_base64 = encode_image_to_base64(image_path)
+
+#     # Ollama API endpoint
+#     OLLAMA_URL = "http://localhost:11434/api/generate"
+
+#     # Payload for the API request
+#     payload = {
+#         "model": model_id,  # Specify the model version
+#         "prompt": "Given image is a handwritten text in english language, read it carefully and extract all the text mentioned in it.",
+#         "images": [image_base64],  # List of Base64-encoded images
+#         "stream": False
+#     }
+
+#     # Headers for the request
+#     headers = {
+#         "Content-Type": "application/json"
+#     }
+
+#     # Send the POST request
+#     response = requests.post(OLLAMA_URL, json=payload, headers=headers)
+
+#     # Check the response
+#     if response.status_code == 200:
+#         data = response.json()
+#         print("Response from Gemma 3:")
+#         print(data.get("response", "No response field in the API response."))
+#     else:
+#         print(f"Error: {response.status_code}")
+#         print(response.text)
+#     return response.text
+
+#     # Path to your image
+
+
+
+# def _hit_endpoint(name):
+#     import requests
+#     import json
+
+#     # Define the URL of the Ollama server
+#     OLLAMA_URL = "http://localhost:11434/api/generate"
+
+#     # Define the request payload
+#     payload = {
+#         "model": model_id,  # Change this to your desired model
+#         "prompt": name,
+#         "stream": False
+#     }
+
+#     # Make the request
+#     response = requests.post(OLLAMA_URL, json=payload)
+
+#     # Parse and print the response
+#     if response.status_code == 200:
+#         data = response.json()
+#         print(data["response"])  # Extracting the generated text
+#         return data["response"]
+#     else:
+#         print(f"Error: {response.status_code} - {response.text}")
+#         return "An error occurred!"
+import os
+import torch
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
+model_id = os.getenv("MODEL_ID", "google/gemma-3-12b-it")
+processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
+model = Gemma3ForConditionalGeneration.from_pretrained(
+    model_id, device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager"
+)
+
+def run_fn(message):
+    messages = []
+
+    messages.append({"role": "user", "content": (message)})
+
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    ).to(device=model.device, dtype=torch.bfloat16)
+    streamer = TextIteratorStreamer(processor, timeout=30.0, skip_prompt=True, skip_special_tokens=True)
+    max_new_tokens = 100
+    generate_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+    )
+    outputs = model.generate(**generate_kwargs)
+    return outputs
+    # return None
 def greet(name):
-    return "Hello " + name + "!!"
+    return run_fn()
 
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+demo.launch()