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README.md

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 ---
-title: Instagram Caption Generator Updated
-emoji: 🏃
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 4.44.1
-app_file: app.py
-pinned: false
+base_model: microsoft/git-base
+library_name: peft
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.1

ig.py

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+from transformers import AutoProcessor, AutoModelForCausalLM
+import gradio as gr
+import torch
+
+# Load the processor and model
+processor = AutoProcessor.from_pretrained("microsoft/git-base")
+model = AutoModelForCausalLM.from_pretrained("./ig_caption")
+
+def predict(image):
+    try:
+        # Prepare the image using the processor
+        inputs = processor(images=image, return_tensors="pt")
+
+        # Move inputs to the appropriate device
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+        model.to(device)
+
+        # Generate the caption
+        outputs = model.generate(**inputs)
+
+        # Decode the generated caption
+        caption = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+
+        return caption
+
+    except Exception as e:
+        print("Error during prediction:", str(e))
+        return "Error: " + str(e)
+
+# https://www.gradio.app/guides
+with gr.Blocks() as demo:
+    image = gr.Image(type="pil")
+    predict_btn = gr.Button("Predict", variant="primary")
+    output = gr.Label(label="Generated Caption")
+
+    inputs = [image]
+    outputs = [output]
+
+    predict_btn.click(predict, inputs=inputs, outputs=outputs)
+
+if __name__ == "__main__":
+    demo.launch()  # Local machine only
+    # demo.launch(server_name="0.0.0.0")  # LAN access to local machine
+    # demo.launch(share=True)  # Public access to local machine

run.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7862\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import AutoProcessor, AutoModelForCausalLM\n",
+    "import gradio as gr\n",
+    "import torch\n",
+    "\n",
+    "# Load the processor and model\n",
+    "processor = AutoProcessor.from_pretrained(\"microsoft/git-base\")\n",
+    "model = AutoModelForCausalLM.from_pretrained(\"./\")\n",
+    "\n",
+    "def predict(image):\n",
+    "    try:\n",
+    "        # Prepare the image using the processor\n",
+    "        inputs = processor(images=image, return_tensors=\"pt\")\n",
+    "\n",
+    "        # Move inputs to the appropriate device\n",
+    "        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "        inputs = {key: value.to(device) for key, value in inputs.items()}\n",
+    "        model.to(device)\n",
+    "\n",
+    "        # Generate the caption\n",
+    "        outputs = model.generate(**inputs)\n",
+    "\n",
+    "        # Decode the generated caption\n",
+    "        caption = processor.batch_decode(outputs, skip_special_tokens=True)[0]\n",
+    "\n",
+    "        return caption\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(\"Error during prediction:\", str(e))\n",
+    "        return \"Error: \" + str(e)\n",
+    "\n",
+    "# https://www.gradio.app/guides\n",
+    "with gr.Blocks() as demo:\n",
+    "    image = gr.Image(type=\"pil\")\n",
+    "    predict_btn = gr.Button(\"Predict\", variant=\"primary\")\n",
+    "    output = gr.Label(label=\"Generated Caption\")\n",
+    "\n",
+    "    inputs = [image]\n",
+    "    outputs = [output]\n",
+    "\n",
+    "    predict_btn.click(predict, inputs=inputs, outputs=outputs)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch()  # Local machine only\n",
+    "    # demo.launch(server_name=\"0.0.0.0\")  # LAN access to local machine\n",
+    "    # demo.launch(share=True)  # Public access to local machine\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}