Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import list_models
+from sentence_transformers import SentenceTransformer, util
+import numpy as np
+
+# Load sentence transformer model for similarity calculation
+semantic_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Function to fetch models from Hugging Face based on dynamic task filter
+def fetch_models_from_hf(task_filter, limit=10):
+    models = list_models(filter=task_filter, limit=limit)
+    model_data = [
+        {
+            "model_id": model.modelId,
+            "tags": model.tags,
+            "downloads": model.downloads,
+            "likes": model.likes,
+            "last_modified": model.lastModified
+        }
+        for model in models
+    ]
+    return model_data
+
+# Function to normalize a list of values to a 0-1 range
+def normalize(values):
+    min_val, max_val = min(values), max(values)
+    return [(v - min_val) / (max_val - min_val) if max_val > min_val else 0 for v in values]
+
+# Function to get weighted recommendations based on task filter and additional metrics
+def get_weighted_recommendations_from_hf(task_filter, weights=None):
+    if weights is None:
+        weights = {"similarity": 0.7, "downloads": 0.2, "likes": 0.1}
+
+    model_data = fetch_models_from_hf(task_filter)
+
+    if len(model_data) == 0:
+        return "No models found for the specified task filter."
+
+    model_ids = [model["model_id"] for model in model_data]
+    model_tags = [' '.join(model["tags"]) for model in model_data]
+
+    # Use a fixed user query based on task filter
+    user_query = f"best model for {task_filter}"
+
+    model_embeddings = semantic_model.encode(model_tags)
+    user_embedding = semantic_model.encode(user_query)
+
+    similarities = util.pytorch_cos_sim(user_embedding, model_embeddings)[0].numpy()
+
+    downloads = normalize([model["downloads"] for model in model_data])
+    likes = normalize([model["likes"] for model in model_data])
+
+    final_scores = []
+    for i in range(len(model_data)):
+        score = (
+            weights["similarity"] * similarities[i] +
+            weights["downloads"] * downloads[i] +
+            weights["likes"] * likes[i]
+        )
+        final_scores.append((model_ids[i], score, similarities[i], downloads[i], likes[i]))
+
+    ranked_recommendations = sorted(final_scores, key=lambda x: x[1], reverse=True)
+
+    result = []
+    for rank, (model_id, final_score, sim, downloads, likes) in enumerate(ranked_recommendations, 1):
+        result.append(f"Rank {rank}: Model ID: {model_id}")
+    
+    return '\n'.join(result)
+
+# Gradio chatbot interface
+def respond(task_filter, history=None, weights=None):
+    # Provide model recommendations based on the task filter
+    return get_weighted_recommendations_from_hf(task_filter, weights)
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(label="Task Filter", placeholder="Enter the task, e.g., text-classification, atari, question-answering"),
+        gr.Textbox(value="You are using the Hugging Face model recommender system.", label="System message")
+    ],
+    outputs=gr.Textbox(label="Model Recommendations"),
+    title="Hugging Face Model Recommender",
+    description="This chatbot recommends models from Hugging Face based on the task or tag you're interested in. It combines various attributes of a model on hub like downloads, likes, etc. to suggest models with ranks from 1-10. In general term basically it intelligently combines the search filter for recommendation"
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)