Upload 3 files

src/app (3).py

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+import streamlit as st
+from library_summarizer import llm_lib_summarizer_v1
+from command_generator import llm_lib_installer_v1
+
+st.title("📂 Compatible Library Summarizer")
+
+imported_libraries = st.text_area("Enter the libraries you are importing in your ipynb file:", height=200)
+api_key = st.text_input("Enter your Groq API Key:", type="password")
+task = st.text_input("Enter the task you are working on (e.g., data analysis, machine learning):", value="Basic ML task")
+python_version = st.selectbox("Select your Python version:", options=["3.7", "3.8", "3.9", "3.10", "3.11","3.12"], index=3)
+
+# File uploader widget
+uploaded_file = st.file_uploader("Choose a file", type=["txt"])
+
+if uploaded_file is not None:
+    st.success(f"File uploaded: {uploaded_file.name}")
+
+    # Read the file (decode for text files)
+    
+if st.button("Generate bash command"):
+    if imported_libraries and uploaded_file:
+        library = imported_libraries
+        libraries = uploaded_file.read().decode("utf-8")
+        with st.spinner("Generating ideal pip install command..."):
+            try:
+                summary = llm_lib_summarizer_v1(import_string=library, api_key=api_key)
+                st.subheader("Summary of Libraries:")
+                st.code(summary)
+                st.subheader("Generated pip install command:")
+                command = llm_lib_installer_v1(imported_libs=summary, python_version="3.10", task="general", api_key=api_key, libraries=libraries)
+                st.markdown(command)
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+    else:
+        st.error("Please enter text or upload a file.")
+

src/command_generator.py

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+def llm_lib_installer_v1(imported_libs: str, python_version: str, task: str, api_key: str, libraries: str, model = 'openai/gpt-oss-20b'):
+    '''
+    imported_libs: Give all the imports made in the current session of the notebook
+    api_key: Generated groq api key
+    task: The type of task being executed in the session notebook
+    model: choose the groq model. Default is GPT OSS
+    '''
+    from groq import Groq
+    import streamlit as st
+    client = Groq(api_key=api_key)
+    completion = client.chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "user",
+                "content": f'''Assume we have imported a few libraries in the google colab environment: {imported_libs}. 
+                Generate a manual !pip install command for the imported libraries for the task: {task}. Take into account the current libraries: {libraries}
+                Make sure there are no conflicts in the library versions even if the libraries are not imported. for instance opencv is dependent on the version of numpy. 
+                Suggest a version install for that as well even if it is not imported. In a similar way check all library dependencies. Try to take the latest libraries as far as possible.
+                Suggest downgrades only if there is no other choice taking in consideration the python version in the environment is: {python_version}.
+                Otherwise try and use the latest versions of the libraries.
+                Use the "~=" symbol if needed to allow for backward compatibility between the installed libraries.'''
+            }
+        ],
+        temperature=0,
+        max_completion_tokens=8192,
+        top_p=1,
+        reasoning_effort="medium",
+        stream=True,
+        stop=None
+        )
+    
+    response_content = ""
+
+    for chunk in completion:
+        response_content+=chunk.choices[0].delta.content or ""
+    
+    return response_content

src/library_summarizer.py

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+def llm_lib_summarizer_v1(import_string: str, api_key: str, model = 'openai/gpt-oss-20b'):
+    '''
+    import_string: All the imports made in the notebook
+    api_key: Generated groq api key
+    model: choose the groq model. Default is GPT OSS
+    '''
+    from groq import Groq
+
+    client = Groq(api_key=api_key)
+    completion = client.chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "user",
+                "content": f'''Assume we are in the Google colab environment and I have imported the following libraries and classes: {import_string}.
+                Tell me on a high level summarize the main library packages that were imported 
+                and the other major packages that are dependant on these libraries even if they are not imported. Give the output as a json. For example:
+                sklearn.model_selection, sklearn.metrics should return only sklearn.
+                
+                output should always look like this:
+                ```json
+                    'libraries':[
+                        'sklearn',
+                        'pandas',
+                        'numpy'....
+                        ]
+                ```
+                Make sure to return only the json and nothing else.'''
+            }
+            ],
+        temperature=0,
+        max_completion_tokens=8192,
+        top_p=1,
+        reasoning_effort="medium",
+        stream=True,
+        stop=None
+        )
+    response_content = ""
+    for chunk in completion:
+        response_content += chunk.choices[0].delta.content or ""
+    response_content = response_content.split('\n')
+    response_content = ''.join(response_content[1:-1])
+    return response_content