first

.github/workflows/deploy.yml

@@ -0,0 +1,28 @@
+name: Deploy AI Model
+
+on:
+  push:
+    branches:
+      - main  # ya jo bhi branch tum use kar rahe ho
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.9'  # Ya jo version tum use kar rahe ho
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: Run the model deployment script
+        run: |
+          python model.py  # Tumhare model code ko run karo

app.py

@@ -0,0 +1,7 @@
+import gradio as gr
+from model import prediction
+
+def predict(text):
+    return prediction(text)
+
+gr.Interface(fn=predict, inputs="text", outputs="text").launch()

model.py

@@ -0,0 +1,32 @@
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+def prediction(file_paths,uploaded_text):
+    sentences = []
+    for file_path in file_paths:
+        with open(file_path, 'r') as file:
+            text = file.read()
+            sentences.append(text)
+
+    sentences.append(uploaded_text)
+            
+            # Load the pre-trained BERT-based model
+    model_name = 'sentence-transformers/bert-base-nli-mean-tokens'
+    model = SentenceTransformer(model_name)
+    
+    # Use the model to encode the sentences
+    sentence_embeddings = model.encode(sentences)
+    
+    # Calculate cosine similarity between the uploaded text and the rest of the sentence embeddings
+    query_embedding = sentence_embeddings[-1]  # Uploaded text embedding
+    candidate_embeddings = sentence_embeddings[:-1]  # Embeddings of other files
+    cosine_similarities = cosine_similarity([query_embedding], candidate_embeddings)[0]
+    
+    # Combine the candidate sentences with their corresponding cosine similarities
+    predictions = list(zip(file_paths, cosine_similarities))
+    
+    # Sort the predictions based on cosine similarity (highest to lowest)
+    predictions.sort(key=lambda x: x[1], reverse=True)
+
+    return predictions
+        

requirements.txt

@@ -0,0 +1,5 @@
+transformers==4.47.1
+torch==2.5.1
+sentence-transformers==3.3.1
+scikit-learn==1.6.0
+gradio==4.26.0