testing the old app.py

app.py

@@ -1,20 +1,90 @@
+# import gradio as gr
+# import os
+
+# def greet(name):
+#     return "Hello " + name + "!"
+
+# # Create the simplest possible Gradio interface
+# iface = gr.Interface(
+#     fn=greet,
+#     inputs="text",
+#     outputs="text",
+#     title="Test Gradio App",
+#     description="This is a simple test app to check if Gradio launches.",
+#     flagging_dir="/tmp/gradio_flagged_data" # <--- ADD THIS LINE BACK!
+# )
+
+# # Use a specific port for Gradio within the Docker container.
+# # This matches the EXPOSE 7860 in your Dockerfile.
+# # It also sets share=False for deployment contexts like Spaces.
+# iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
+
 import gradio as gr
-import os
+# from transformers import pipeline
+# from langchain_community.llms import OpenAI
+# from langchain.chains import LLMChain
+# from langchain.prompts import PromptTemplate
+# from langchain_community.document_loaders import PyPDFLoader
+
+def load_document(file_path):
+    """Loads a PDF document and returns its content."""
+    loader = PyPDFLoader(file_path)
+    pages = loader.load_and_split()
+    return "".join([page.page_content for page in pages])
+
+def summarize_text(text):
+    """Summarizes the given text using a pre-trained model."""
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    summary = summarizer(text, max_length=500, min_length=100, do_sample=False)
+    return summary[0]['summary_text']
+
+def identify_future_research(text):
+    """Uses a language model to identify future research scope."""
+    llm = OpenAI(temperature=0.7)  # You can also use open-source models from Hugging Face Hub
+
+    prompt_template = """
+    Based on the following research paper, identify and suggest potential areas for future research.
+    Be specific and provide actionable insights.
 
-def greet(name):
-    return "Hello " + name + "!"
+    Research Paper Content:
+    {paper_content}
+
+    Future Research Scope:
+    """
+
+    prompt = PromptTemplate(
+        input_variables=["paper_content"],
+        template=prompt_template
+    )
+
+    chain = LLMChain(llm=llm, prompt=prompt)
+    future_scope = chain.run(paper_content=text)
+    return future_scope
+
+
+
+def analyze_paper(file):
+    """The main function that orchestrates the analysis."""
+    if file is not None:
+        # paper_text = load_document(file.name)
+        # summary = summarize_text(paper_text)
+        # future_scope = identify_future_research(paper_text)
+        # return summary, future_scope
+        return "Dummy Summary Placeholder", "Dummy Future Scope Placeholder"
+    return "Please upload a research paper.", ""
 
-# Create the simplest possible Gradio interface
 iface = gr.Interface(
-    fn=greet,
-    inputs="text",
-    outputs="text",
-    title="Test Gradio App",
-    description="This is a simple test app to check if Gradio launches.",
-    flagging_dir="/tmp/gradio_flagged_data" # <--- ADD THIS LINE BACK!
+    fn=analyze_paper,
+    inputs=gr.File(label="Upload Research Paper (PDF)"),
+    outputs=[
+        gr.Textbox(label="Summary of the Paper"),
+        gr.Textbox(label="Scope for Further Research")
+    ],
+    flagging_dir="/tmp/gradio_flagged_data",
+    title="AI Research Assistant",
+    description="Upload a research paper to get a summary and identify potential areas for future research.",
+    theme="huggingface"
 )
 
-# Use a specific port for Gradio within the Docker container.
-# This matches the EXPOSE 7860 in your Dockerfile.
-# It also sets share=False for deployment contexts like Spaces.
-iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
+iface.launch()
+