Update app.py

app.py

@@ -1,247 +1,247 @@
-import os
-import requests
-import tellurium as te
-import tempfile
-import ollama
-import gradio as gr
-from langchain_text_splitters import CharacterTextSplitter
-import chromadb
-
-# Constants and global variables
-GITHUB_OWNER = "sys-bio"
-GITHUB_REPO_CACHE = "BiomodelsCache"
-BIOMODELS_JSON_DB_PATH = "src/cached_biomodels.json"
-LOCAL_DOWNLOAD_DIR = tempfile.mkdtemp()
-
-cached_data = None
-db = None
-
-def fetch_github_json():
-    url = f"https://api.github.com/repos/{GITHUB_OWNER}/{GITHUB_REPO_CACHE}/contents/{BIOMODELS_JSON_DB_PATH}"
-    headers = {"Accept": "application/vnd.github+json"}
-    response = requests.get(url, headers=headers)
-    
-    if response.status_code == 200:
-        data = response.json()
-        if "download_url" in data:
-            file_url = data["download_url"]
-            json_response = requests.get(file_url)
-            return json_response.json()
-        else:
-            raise ValueError(f"Unable to fetch model DB from GitHub repository: {GITHUB_OWNER} - {GITHUB_REPO_CACHE}")
-    else:
-        raise ValueError(f"Unable to fetch model DB from GitHub repository: {GITHUB_OWNER} - {GITHUB_REPO_CACHE}")
-
-def search_models(search_str):
-    global cached_data
-    if cached_data is None:
-        cached_data = fetch_github_json()
-    
-    query_text = search_str.strip().lower()
-    models = {}
-    
-    for model_id, model_data in cached_data.items():
-        if 'name' in model_data:
-            name = model_data['name'].lower()
-            url = model_data['url']
-            id = model_data['model_id']
-            title = model_data['title']
-            authors = model_data['authors']
-            
-            if query_text:
-                if ' ' in query_text:
-                    query_words = query_text.split(" ")
-                    if all(word in ' '.join([str(v).lower() for v in model_data.values()]) for word in query_words):
-                        models[model_id] = {
-                            'ID': model_id,
-                            'name': name,
-                            'url': url,
-                            'id': id,
-                            'title': title,
-                            'authors': authors,
-                        }
-                else:
-                    if query_text in ' '.join([str(v).lower() for v in model_data.values()]):
-                        models[model_id] = {
-                            'ID': model_id,
-                            'name': name,
-                            'url': url,
-                            'id': id,
-                            'title': title,
-                            'authors': authors,
-                        }
-    
-    return models
-
-def download_model_file(model_url, model_id):
-    model_url = f"https://raw.githubusercontent.com/konankisa/BiomodelsStore/main/biomodels/{model_id}/{model_id}_url.xml"
-    response = requests.get(model_url)
-    
-    if response.status_code == 200:
-        os.makedirs(LOCAL_DOWNLOAD_DIR, exist_ok=True)
-        file_path = os.path.join(LOCAL_DOWNLOAD_DIR, f"{model_id}.xml")
-        
-        with open(file_path, 'wb') as file:
-            file.write(response.content)
-        
-        print(f"Model {model_id} downloaded successfully: {file_path}")
-        return file_path
-    else:
-        raise ValueError(f"Failed to download the model from {model_url}")
-
-def convert_sbml_to_antimony(sbml_file_path, antimony_file_path):
-    try:
-        r = te.loadSBMLModel(sbml_file_path)
-        antimony_str = r.getCurrentAntimony()
-        
-        with open(antimony_file_path, 'w') as file:
-            file.write(antimony_str)
-        
-        print(f"Successfully converted SBML to Antimony: {antimony_file_path}")
-    
-    except Exception as e:
-        print(f"Error converting SBML to Antimony: {e}")
-
-def split_biomodels(antimony_file_path):
-    text_splitter = CharacterTextSplitter(
-        separator="  // ",
-        chunk_size=1000,
-        chunk_overlap=20,
-        length_function=len,
-        is_separator_regex=False
-    )
-    
-    final_items = []
-    directory_path = os.path.dirname(os.path.abspath(antimony_file_path))
-    if not os.path.isdir(directory_path):
-        print(f"Directory not found: {directory_path}")
-        return final_items
-
-    files = os.listdir(directory_path)
-    for file in files:
-        file_path = os.path.join(directory_path, file)
-        try:
-            with open(file_path, 'r') as f:
-                file_content = f.read()
-                items = text_splitter.create_documents([file_content])
-                for item in items:
-                    final_items.append(item)
-                break
-        except Exception as e:
-            print(f"Error reading file {file_path}: {e}")
-
-    return final_items
-
-def create_vector_db(final_items):
-    global db
-    client = chromadb.Client()
-    db = client.create_collection(
-        name="BioModelsRAG",
-        metadata={"hnsw:space": "cosine"}
-    )
-    documents = []
-    
-    for item in final_items:
-        prompt = f"""
-        Summarize the following segment of Antimony in a clear and concise manner:
-        1. Provide a detailed summary using a limited number of words
-        2. Maintain all original values and include any mathematical expressions or values in full. 
-        3. Ensure that all variable names and their values are clearly presented. 
-        4. Write the summary in paragraph format, putting an emphasis on clarity and completeness. 
-        
-        Here is the antimony segment to summarize: {item}
-        """
-        documents5 = ollama.generate(model="llama3", prompt=prompt)
-        documents2 = documents5['response']
-        documents.append(documents2)
-    
-    if final_items:
-        db.add(
-            documents=documents,
-            ids=[f"id{i}" for i in range(len(final_items))]
-        )
-    return db
-
-def generate_response(db, query_text, previous_context):
-    query_results = db.query(
-        query_texts=query_text,
-        n_results=5,
-    )
-    
-    if not query_results.get('documents'):
-        return "No results found."
-    
-    best_recommendation = query_results['documents']
-    
-    prompt_template = f"""
-    Using the context provided below, answer the following question. If the information is insufficient to answer the question, please state that clearly. 
-    
-    Context:
-    {previous_context} {best_recommendation}
-    
-    Instructions:
-    1. Cross-Reference: Use all provided context to define variables and identify any unknown entities. 
-    2. Mathematical Calculations: Perform any necessary calculations based on the context and available data. 
-    3. Consistency: Remember and incorporate previous responses if the question is related to earlier information. 
-    
-    Question: 
-    {query_text}
-
-    """
-    response = ollama.generate(model="llama3", prompt=prompt_template)
-    final_response = response.get('response', 'No response generated')
-    return final_response
-
-def gradio_interface(search_str, selected_names, user_query):
-    models = search_models(search_str)
-    
-    if not models:
-        return "No models found for the given search query.", None
-    
-    selected_model_ids = [model_id for model_id, model_data in models.items() if model_data['name'] in selected_names]
-    
-    if not selected_model_ids:
-        return "No models selected for analysis.", None
-    
-    all_final_items = []
-    for model_id in selected_model_ids:
-        model_data = models[model_id]
-        
-        model_url = model_data['url']
-        model_file_path = download_model_file(model_url, model_id)
-        antimony_file_path = model_file_path.replace(".xml", ".antimony")
-        
-        convert_sbml_to_antimony(model_file_path, antimony_file_path)
-        
-        final_items = split_biomodels(antimony_file_path)
-        if not final_items:
-            return "No content found in the biomodel.", None
-        
-        all_final_items.extend(final_items)
-    
-    global db
-    db = create_vector_db(all_final_items)
-    
-    if db:
-        response = generate_response(db, user_query, "")
-        return "Models have been processed and added to the database.", response
-    
-    return "Database creation failed.", None
-
-def main():
-    gr.Interface(
-        fn=gradio_interface,
-        inputs=[
-            gr.Textbox(label="Search Query", placeholder="Enter search query..."),
-            gr.CheckboxGroup(label="Select biomodels", choices=[]),
-            gr.Textbox(label="Ask a question about the biomodels", placeholder="Enter your question...")
-        ],
-        outputs=[
-            gr.Textbox(label="Status"),
-            gr.Textbox(label="Response")
-        ],
-        live=True
-    ).launch(share = True)
-
-if __name__ == "__main__":
-    main()
+import os
+import requests
+import tellurium as te
+import tempfile
+import ollama
+import gradio as gr
+from langchain_text_splitters import CharacterTextSplitter
+import chromadb
+
+# Constants and global variables
+GITHUB_OWNER = "sys-bio"
+GITHUB_REPO_CACHE = "BiomodelsCache"
+BIOMODELS_JSON_DB_PATH = "src/cached_biomodels.json"
+LOCAL_DOWNLOAD_DIR = tempfile.mkdtemp()
+
+cached_data = None
+db = None
+
+def fetch_github_json():
+    url = f"https://api.github.com/repos/{GITHUB_OWNER}/{GITHUB_REPO_CACHE}/contents/{BIOMODELS_JSON_DB_PATH}"
+    headers = {"Accept": "application/vnd.github+json"}
+    response = requests.get(url, headers=headers)
+    
+    if response.status_code == 200:
+        data = response.json()
+        if "download_url" in data:
+            file_url = data["download_url"]
+            json_response = requests.get(file_url)
+            return json_response.json()
+        else:
+            raise ValueError(f"Unable to fetch model DB from GitHub repository: {GITHUB_OWNER} - {GITHUB_REPO_CACHE}")
+    else:
+        raise ValueError(f"Unable to fetch model DB from GitHub repository: {GITHUB_OWNER} - {GITHUB_REPO_CACHE}")
+
+def search_models(search_str):
+    global cached_data
+    if cached_data is None:
+        cached_data = fetch_github_json()
+    
+    query_text = search_str.strip().lower()
+    models = {}
+    
+    for model_id, model_data in cached_data.items():
+        if 'name' in model_data:
+            name = model_data['name'].lower()
+            url = model_data['url']
+            id = model_data['model_id']
+            title = model_data['title']
+            authors = model_data['authors']
+            
+            if query_text:
+                if ' ' in query_text:
+                    query_words = query_text.split(" ")
+                    if all(word in ' '.join([str(v).lower() for v in model_data.values()]) for word in query_words):
+                        models[model_id] = {
+                            'ID': model_id,
+                            'name': name,
+                            'url': url,
+                            'id': id,
+                            'title': title,
+                            'authors': authors,
+                        }
+                else:
+                    if query_text in ' '.join([str(v).lower() for v in model_data.values()]):
+                        models[model_id] = {
+                            'ID': model_id,
+                            'name': name,
+                            'url': url,
+                            'id': id,
+                            'title': title,
+                            'authors': authors,
+                        }
+    
+    return models
+
+def download_model_file(model_url, model_id):
+    model_url = f"https://raw.githubusercontent.com/konankisa/BiomodelsStore/main/biomodels/{model_id}/{model_id}_url.xml"
+    response = requests.get(model_url)
+    
+    if response.status_code == 200:
+        os.makedirs(LOCAL_DOWNLOAD_DIR, exist_ok=True)
+        file_path = os.path.join(LOCAL_DOWNLOAD_DIR, f"{model_id}.xml")
+        
+        with open(file_path, 'wb') as file:
+            file.write(response.content)
+        
+        print(f"Model {model_id} downloaded successfully: {file_path}")
+        return file_path
+    else:
+        raise ValueError(f"Failed to download the model from {model_url}")
+
+def convert_sbml_to_antimony(sbml_file_path, antimony_file_path):
+    try:
+        r = te.loadSBMLModel(sbml_file_path)
+        antimony_str = r.getCurrentAntimony()
+        
+        with open(antimony_file_path, 'w') as file:
+            file.write(antimony_str)
+        
+        print(f"Successfully converted SBML to Antimony: {antimony_file_path}")
+    
+    except Exception as e:
+        print(f"Error converting SBML to Antimony: {e}")
+
+def split_biomodels(antimony_file_path):
+    text_splitter = CharacterTextSplitter(
+        separator="  // ",
+        chunk_size=1000,
+        chunk_overlap=20,
+        length_function=len,
+        is_separator_regex=False
+    )
+    
+    final_items = []
+    directory_path = os.path.dirname(os.path.abspath(antimony_file_path))
+    if not os.path.isdir(directory_path):
+        print(f"Directory not found: {directory_path}")
+        return final_items
+
+    files = os.listdir(directory_path)
+    for file in files:
+        file_path = os.path.join(directory_path, file)
+        try:
+            with open(file_path, 'r') as f:
+                file_content = f.read()
+                items = text_splitter.create_documents([file_content])
+                for item in items:
+                    final_items.append(item)
+                break
+        except Exception as e:
+            print(f"Error reading file {file_path}: {e}")
+
+    return final_items
+
+def create_vector_db(final_items):
+    global db
+    client = chromadb.Client()
+    db = client.create_collection(
+        name="BioModelsRAG",
+        metadata={"hnsw:space": "cosine"}
+    )
+    documents = []
+    
+    for item in final_items:
+        prompt = f"""
+        Summarize the following segment of Antimony in a clear and concise manner:
+        1. Provide a detailed summary using a limited number of words
+        2. Maintain all original values and include any mathematical expressions or values in full. 
+        3. Ensure that all variable names and their values are clearly presented. 
+        4. Write the summary in paragraph format, putting an emphasis on clarity and completeness. 
+        
+        Here is the antimony segment to summarize: {item}
+        """
+        documents5 = ollama.generate(model="llama3", prompt=prompt)
+        documents2 = documents5['response']
+        documents.append(documents2)
+    
+    if final_items:
+        db.add(
+            documents=documents,
+            ids=[f"id{i}" for i in range(len(final_items))]
+        )
+    return db
+
+def generate_response(db, query_text, previous_context):
+    query_results = db.query(
+        query_texts=query_text,
+        n_results=5,
+    )
+    
+    if not query_results.get('documents'):
+        return "No results found."
+    
+    best_recommendation = query_results['documents']
+    
+    prompt_template = f"""
+    Using the context provided below, answer the following question. If the information is insufficient to answer the question, please state that clearly. 
+    
+    Context:
+    {previous_context} {best_recommendation}
+    
+    Instructions:
+    1. Cross-Reference: Use all provided context to define variables and identify any unknown entities. 
+    2. Mathematical Calculations: Perform any necessary calculations based on the context and available data. 
+    3. Consistency: Remember and incorporate previous responses if the question is related to earlier information. 
+    
+    Question: 
+    {query_text}
+
+    """
+    response = ollama.generate(model="llama3", prompt=prompt_template)
+    final_response = response.get('response', 'No response generated')
+    return final_response
+
+def gradio_interface(search_str, selected_names, user_query):
+    models = search_models(search_str)
+    
+    if not models:
+        return "No models found for the given search query.", None
+    
+    selected_model_ids = [model_id for model_id, model_data in models.items() if model_data['name'] in selected_names]
+    
+    if not selected_model_ids:
+        return "No models selected for analysis.", None
+    
+    all_final_items = []
+    for model_id in selected_model_ids:
+        model_data = models[model_id]
+        
+        model_url = model_data['url']
+        model_file_path = download_model_file(model_url, model_id)
+        antimony_file_path = model_file_path.replace(".xml", ".antimony")
+        
+        convert_sbml_to_antimony(model_file_path, antimony_file_path)
+        
+        final_items = split_biomodels(antimony_file_path)
+        if not final_items:
+            return "No content found in the biomodel.", None
+        
+        all_final_items.extend(final_items)
+    
+    global db
+    db = create_vector_db(all_final_items)
+    
+    if db:
+        response = generate_response(db, user_query, "")
+        return "Models have been processed and added to the database.", response
+    
+    return "Database creation failed.", None
+
+def main():
+    gr.Interface(
+        fn=gradio_interface,
+        inputs=[
+            gr.Textbox(label="Search Query", placeholder="Enter search query..."),
+            gr.CheckboxGroup(label="Select biomodels", choices=[]),
+            gr.Textbox(label="Ask a question about the biomodels", placeholder="Enter your question...")
+        ],
+        outputs=[
+            gr.Textbox(label="Status"),
+            gr.Textbox(label="Response")
+        ],
+        live=True
+    ).launch()
+
+if __name__ == "__main__":
+    main()