Create app.py

app.py

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+
+import pandas as pd
+from fuzzywuzzy import process
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import gradio as gr
+
+# Load dataset from GitHub
+url = "https://raw.githubusercontent.com/devbernie/Medimatch/refs/heads/main/dataset.csv"
+data = pd.read_csv(url)
+
+# Preprocess data
+columns_to_process = ['Composition', 'Uses', 'Medicine Name']
+for column in columns_to_process:
+    data[column] = data[column].fillna('').str.lower()
+
+# Fuzzy search function
+def fuzzy_search(query, data_column, threshold=80):
+    matches = process.extract(query.lower(), data_column, limit=5)
+    results = [match[0] for match in matches if match[1] >= threshold]
+    return results
+
+# Vectorize data
+vectorizer_composition = TfidfVectorizer()
+composition_matrix = vectorizer_composition.fit_transform(data['Composition'])
+
+vectorizer_uses = TfidfVectorizer()
+uses_matrix = vectorizer_uses.fit_transform(data['Uses'])
+
+# Calculate similarities
+composition_similarity = cosine_similarity(composition_matrix)
+uses_similarity = cosine_similarity(uses_matrix)
+overall_similarity = (composition_similarity + uses_similarity) / 2
+
+# Recommendation function
+def recommend_medicine(input_name, top_n=5):
+    matches = fuzzy_search(input_name, data['Medicine Name'])
+    if not matches:
+        return f"Không tìm thấy thuốc gần đúng với '{input_name}'. Vui lòng thử lại."
+    
+    selected_medicine = matches[0]
+    idx = data[data['Medicine Name'] == selected_medicine].index[0]
+    sim_scores = list(enumerate(overall_similarity[idx]))
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+    
+    recommendations = []
+    for i in sim_scores[1:top_n+1]:
+        med_info = data.iloc[i[0]]
+        recommendations.append({
+            "Medicine Name": med_info['Medicine Name'],
+            "Composition": med_info['Composition'],
+            "Uses": med_info['Uses'],
+            "Side Effects": med_info['Side_effects'],
+            "Manufacturer": med_info['Manufacturer'],
+            "Image URL": med_info['Image URL']
+        })
+    return recommendations
+
+# Gradio interface function
+def gradio_recommend(input_name):
+    results = recommend_medicine(input_name)
+    if isinstance(results, str):  # Error message
+        return results
+    
+    output = ""
+    for med in results:
+        output += f"### Tên thuốc: {med['Medicine Name'].capitalize()}\n"
+        output += f"**Thành phần:** {med['Composition']}\n"
+        output += f"**Công dụng:** {med['Uses']}\n"
+        output += f"**Tác dụng phụ:** {med['Side Effects']}\n"
+        output += f"**Nhà sản xuất:** {med['Manufacturer']}\n"
+        if med['Image URL']:
+            output += f"![Hình ảnh thuốc]({med['Image URL']})\n"
+        output += "\n"
+    return output
+
+# Gradio app
+interface = gr.Interface(
+    fn=gradio_recommend,
+    inputs="text",
+    outputs="markdown",
+    title="Gợi ý Thuốc Tương Tự",
+    description="Nhập tên thuốc để nhận danh sách các thuốc tương tự dựa trên thành phần và công dụng."
+)
+
+if __name__ == "__main__":
+    interface.launch()