Update app.py

app.py

@@ -1,63 +1,83 @@
 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 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  # You could use this for recency
+        }
+        for model in models
+    ]
+    return model_data
+
+# Normalize values for 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]
+
+# Get weighted recommendations based on user query and additional metrics
+def get_weighted_recommendations_from_hf(user_query, task_filter, weights=None):
+    if weights is None:
+        weights = {"similarity": 0.7, "downloads": 0.2, "likes": 0.1}  # Adjustable
+
+    model_data = fetch_models_from_hf(task_filter)
+    
+    model_ids = [model["model_id"] for model in model_data]
+    model_tags = [' '.join(model["tags"]) for model in model_data]
+    
+    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}, Final Score: {final_score:.4f}, "
+                      f"Similarity: {sim:.4f}, Downloads: {downloads:.4f}, Likes: {likes:.4f}")
+    
+    return '\n'.join(result)
+
+# Define a Gradio interface function
+def chatbot_interface(user_query, task_filter):
+    return get_weighted_recommendations_from_hf(user_query, task_filter)
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=chatbot_interface,
+    inputs=[
+        gr.inputs.Textbox(label="Enter your query", placeholder="What kind of model or tag are you looking for?"),
+        gr.inputs.Textbox(label="Task Filter (e.g., text-classification, summarization, atari)", placeholder="Enter the task"),
     ],
+    outputs="text",
+    title="Hugging Face Model Recommendation Chatbot",
+    description="This chatbot recommends models from Hugging Face based on your query."
 )
 
-
+# Launch the Gradio interface
 if __name__ == "__main__":
-    demo.launch(share=True)
+    interface.launch()