Initial commit

.gitattributes

@@ -0,0 +1,6 @@
+# Auto detect text files and perform LF normalization
+* text=auto
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text

.github/workflows/sync_to_hub.yml

@@ -0,0 +1,22 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # Allows manual run from Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        # REPLACE 'YourHFUsername/RescueAI' with your actual HF Space address below!
+        run: git push https://Tirush12:$HF_TOKEN@huggingface.co/spaces/Tirush12/ResQ-Agent main

.gitignore

@@ -0,0 +1,181 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor  
+#  Cursor is an AI-powered code editor.`.cursorignore` specifies files/directories to 
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore

LICENSE

@@ -0,0 +1,201 @@
+Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

app.py

@@ -0,0 +1,200 @@
+import streamlit as st
+import os
+from database import init_db, add_user, verify_user, create_session, get_user_sessions, get_session_details, save_message, get_session_messages
+from backend import get_image_data, chat_with_context
+
+# Initialize DB
+init_db()
+st.set_page_config(page_title="RescueAI Analyst", page_icon="🚁", layout="wide")
+
+# --- STATE ---
+if "logged_in" not in st.session_state: st.session_state.logged_in = False
+if "username" not in st.session_state: st.session_state.username = ""
+if "current_session_id" not in st.session_state: st.session_state.current_session_id = None
+
+# --- UI HELPER: LEGEND ---
+def display_floodnet_legend():
+    """Renders legend for the map."""
+    legend_html = """
+    <style>
+        .legend-container {
+            display: grid; grid-template-columns: repeat(2, 1fr); gap: 8px;
+            margin-top: 10px; padding: 10px; background-color: #f0f2f6;
+            border-radius: 8px; font-size: 14px; color: #000000; font-weight: 500;
+        }
+        .legend-item { display: flex; align-items: center; }
+        .color-box { width: 18px; height: 18px; margin-right: 8px; border-radius: 4px; border: 1px solid #ccc; }
+    </style>
+    <div class="legend-container">
+        <div class="legend-item"><div class="color-box" style="background: #FF0000;"></div>Flooded Bldg</div>
+        <div class="legend-item"><div class="color-box" style="background: #8B4513;"></div>Safe Bldg</div>
+        <div class="legend-item"><div class="color-box" style="background: #00008B;"></div>Flooded Road</div>
+        <div class="legend-item"><div class="color-box" style="background: #808080;"></div>Safe Road</div>
+        <div class="legend-item"><div class="color-box" style="background: #00BFFF;"></div>Water</div>
+        <div class="legend-item"><div class="color-box" style="background: #FFD700;"></div>Vehicle</div>
+        <div class="legend-item"><div class="color-box" style="background: #228B22;"></div>Tree</div>
+        <div class="legend-item"><div class="color-box" style="background: #00FFFF;"></div>Pool</div>
+    </div>
+    """
+    st.markdown(legend_html, unsafe_allow_html=True)
+
+# --- LOGIN ---
+def login_page():
+    st.title("🔐 RescueAI Analyst Login")
+    tab1, tab2 = st.tabs(["Login", "Sign Up"])
+    with tab1:
+        user = st.text_input("Username", key="l_user")
+        pw = st.text_input("Password", type="password", key="l_pw")
+        if st.button("Login"):
+            if verify_user(user, pw):
+                st.session_state.logged_in = True
+                st.session_state.username = user
+                st.rerun()
+            else:
+                st.error("Invalid credentials")
+    with tab2:
+        new_u = st.text_input("New Username", key="n_user")
+        new_p = st.text_input("New Password", type="password", key="n_pw")
+        if st.button("Create Account"):
+            if add_user(new_u, new_p):
+                st.success("Created! Log in now.")
+            else:
+                st.error("User exists.")
+
+# --- NEW ANALYSIS ---
+def start_new_analysis(uploaded_file):
+    save_dir = "uploaded_images"
+    os.makedirs(save_dir, exist_ok=True)
+    file_path = os.path.join(save_dir, uploaded_file.name)
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    with st.spinner("🚀 Analyzing Terrain & Structures..."):
+        try:
+            data = get_image_data(file_path)
+            
+            title = f"Scan: {uploaded_file.name}"
+            session_id = create_session(st.session_state.username, title, file_path)
+            
+            # --- FULL CONTEXT STRING ---
+            context_str = f"""
+            [DETAILED VISION REPORT]
+            OBJECTS DETECTED:
+            - Flooded Buildings: {data['flooded_bldgs']}
+            - Safe Buildings: {data['safe_bldgs']}
+            - Vehicles Trapped: {data['vehicles']}
+            - Swimming Pools: {data['pools_count']}
+
+            TERRAIN ANALYSIS (Coverage %):
+            - Tree Coverage: {data['trees_pct']}%
+            - Grass/Land: {data['grass_pct']}%
+            - Natural Water Body: {data['water_pct']}%
+
+            INFRASTRUCTURE STATUS:
+            - Building Damage Rate: {data['bldg_damage_pct']:.1f}%
+            - ROAD STATUS: {data['road_flood_severity_pct']:.1f}% of roads are SUBMERGED.
+            
+            MAP FILE: {data['map_path']}
+            """
+            save_message(session_id, "system", context_str)
+            
+            # REMOVED: No automatic greeting. 
+            # We just set the session and let the user speak first.
+            
+            st.session_state.current_session_id = session_id
+            st.rerun()
+            
+        except Exception as e:
+            st.error(f"Analysis Failed: {e}")
+
+# --- MAIN APP ---
+def main_app():
+    with st.sidebar:
+        st.header("🗂️ Case Files")
+        if st.button("➕ New Analysis", type="primary"):
+            st.session_state.current_session_id = None
+            st.rerun()
+        
+        st.divider()
+        sessions = get_user_sessions(st.session_state.username)
+        for s_id, title, date in sessions:
+            if st.button(title, key=s_id):
+                st.session_state.current_session_id = s_id
+                st.rerun()
+        
+        st.divider()
+        if st.button("Logout"):
+            st.session_state.logged_in = False
+            st.rerun()
+
+    if st.session_state.current_session_id is None:
+        st.title("🚁 New Disaster Analysis")
+        uploaded_file = st.file_uploader("Select Aerial Image", type=['jpg', 'png'])
+        if uploaded_file and st.button("Process Image"):
+            start_new_analysis(uploaded_file)
+
+    else:
+        s_id = st.session_state.current_session_id
+        details = get_session_details(s_id)
+        if not details:
+            st.error("Session error.")
+            return
+        
+        img_path, title = details
+        messages = get_session_messages(s_id)
+        
+        system_context = ""
+        chat_history_display = []
+        for role, content in messages:
+            if role == "system":
+                system_context = content
+            else:
+                chat_history_display.append((role, content))
+
+        col1, col2 = st.columns([1, 1])
+        
+        with col1:
+            st.subheader("👁️ Visual Intel")
+            if os.path.exists(img_path):
+                st.image(img_path, caption="Original Scene", use_container_width=True)
+                
+                try:
+                    if "MAP FILE: " in system_context:
+                        map_path = system_context.split("MAP FILE: ")[1].strip()
+                        if os.path.exists(map_path):
+                            st.image(map_path, caption="AI Segmentation Mask", use_container_width=True)
+                            display_floodnet_legend()
+                except:
+                    pass
+
+        with col2:
+            st.subheader("💬 Analyst Chat")
+            container = st.container(height=600)
+            
+            with container:
+                # If no history yet, show a welcome tip
+                if not chat_history_display:
+                    st.info("Analysis Complete. Ask me about damage, roads, or vehicles.")
+                else:
+                    for role, content in chat_history_display:
+                        with st.chat_message(role):
+                            st.write(content)
+
+            if user_input := st.chat_input("Ask about trees, pools, roads..."):
+                save_message(s_id, "user", user_input)
+                with container:
+                    with st.chat_message("user"):
+                        st.write(user_input)
+
+                with st.spinner("Consulting data..."):
+                    response = chat_with_context(system_context, user_input)
+                    save_message(s_id, "assistant", response)
+                    with container:
+                        with st.chat_message("assistant"):
+                            st.write(response)
+                    st.rerun()
+
+if st.session_state.logged_in:
+    main_app()
+else:
+    login_page()

backend.py

@@ -0,0 +1,121 @@
+import os
+import json
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+from tools import object_detection_tool, semantic_segmentation_tool
+
+# Load Env
+load_dotenv()
+
+# --- SETUP GROQ ---
+api_key = "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"
+
+if not api_key:
+    # Default for local testing
+    print("⚠️ Warning: GROQ_API_KEY not found in .env")
+
+llm = ChatGroq(
+    api_key=api_key,
+    model_name="llama-3.3-70b-versatile",
+    temperature=0.3
+)
+
+# FloodNet Class Mapping (ID -> Name)
+SEG_CLASSES = {
+    0: "Background", 
+    1: "Building Flooded", 
+    2: "Building Non-Flooded",
+    3: "Road Flooded", 
+    4: "Road Non-Flooded", 
+    5: "Water",
+    6: "Tree", 
+    7: "Vehicle", 
+    8: "Pool", 
+    9: "Grass"
+}
+
+def get_image_data(image_path):
+    """
+    Runs Vision Tools and returns a comprehensive data dictionary.
+    """
+    print(f"🚀 Extracting Full Data from: {image_path}")
+    
+    # --- 1. RUN PERCEPTION TOOLS ---
+    det_json = object_detection_tool.invoke(image_path)
+    det_data = json.loads(det_json)
+    
+    seg_json = semantic_segmentation_tool.invoke(image_path)
+    seg_data = json.loads(seg_json)
+    
+    # --- 2. PROCESS YOLO COUNTS (Objects) ---
+    counts = det_data.get('counts', {})
+    
+    # Specific Counts
+    flooded_bldgs = counts.get('Building Flooded', 0)
+    safe_bldgs = counts.get('Building Non-Flooded', 0)
+    vehicles = counts.get('Vehicle', 0)
+    pools = counts.get('Pool', 0)
+    
+    total_buildings = flooded_bldgs + safe_bldgs
+    if total_buildings > 0:
+        bldg_damage_pct = (flooded_bldgs / total_buildings) * 100
+    else:
+        bldg_damage_pct = 0.0
+
+    # --- 3. PROCESS SEGMENTATION (Terrain & Roads) ---
+    pixel_counts = seg_data.get('pixel_counts', {})
+    total_pixels = sum(pixel_counts.values()) if pixel_counts else 1
+    
+    # Calculate Area Percentages
+    area_stats = {}
+    for cls_id, cls_name in SEG_CLASSES.items():
+        px_count = pixel_counts.get(str(cls_id), pixel_counts.get(cls_id, 0))
+        pct = (px_count / total_pixels) * 100
+        area_stats[cls_name] = round(pct, 2)
+
+    # Road Specific Analysis
+    road_flooded_area = area_stats.get("Road Flooded", 0)
+    road_safe_area = area_stats.get("Road Non-Flooded", 0)
+    total_road_area = road_flooded_area + road_safe_area
+    
+    if total_road_area > 0.1: 
+        road_flood_severity = (road_flooded_area / total_road_area) * 100
+    else:
+        road_flood_severity = 0.0
+
+    # --- 4. COMPILE FINAL DATA PACKET ---
+    return {
+        "flooded_bldgs": flooded_bldgs,
+        "safe_bldgs": safe_bldgs,
+        "vehicles": vehicles,
+        "pools_count": pools,
+        "bldg_damage_pct": bldg_damage_pct,
+        "road_flood_severity_pct": road_flood_severity,
+        "trees_pct": area_stats.get("Tree", 0),
+        "grass_pct": area_stats.get("Grass", 0),
+        "water_pct": area_stats.get("Water", 0),
+        "map_path": seg_data.get('map_path', "")
+    }
+
+def chat_with_context(system_context, user_input):
+    prompt = f"""
+    SYSTEM INSTRUCTIONS:
+    You are an advanced Disaster Response AI. You have access to precise sensor data from a drone.
+    
+    RESPONSE GUIDELINES:
+    1. **VISUAL AWARENESS:** The user can see the "Visual Intel" panel on the left side of their screen. 
+       - If they ask "Show me the map" or "Where is the flood?", say: "I have already visualized the flood extent for you. Please check the **AI Segmentation Mask** in the left panel."
+    2. **DIRECT ANSWER:** Answer questions directly. Do not explain your logic.
+    3. **BE SPECIFIC:** Use the provided counts (e.g., "5 vehicles") instead of vague terms ("some cars").
+    4. **RELEVANCE:** Only mention road safety if relevant or if roads are totally blocked.
+    5. **TONE:** Professional, concise, and helpful.
+
+    SENSOR DATA CONTEXT:
+    {system_context}
+
+    USER QUESTION:
+    {user_input}
+    """
+    
+    response = llm.invoke(prompt)
+    return response.content

database.py

@@ -0,0 +1,99 @@
+import sqlite3
+import hashlib
+import uuid
+from datetime import datetime
+
+DB_NAME = "app.db"
+
+def init_db():
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    
+    # Users Table
+    c.execute('''CREATE TABLE IF NOT EXISTS users 
+                 (username TEXT PRIMARY KEY, password TEXT)''')
+    
+    # Sessions Table (New!) - Stores the conversation metadata
+    c.execute('''CREATE TABLE IF NOT EXISTS sessions 
+                 (session_id TEXT PRIMARY KEY, username TEXT, 
+                  title TEXT, image_path TEXT, created_at DATETIME)''')
+                  
+    # Messages Table - Linked to Session ID
+    c.execute('''CREATE TABLE IF NOT EXISTS messages 
+                 (id INTEGER PRIMARY KEY AUTOINCREMENT, 
+                  session_id TEXT, role TEXT, content TEXT, 
+                  timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
+    
+    conn.commit()
+    conn.close()
+
+def add_user(username, password):
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    hashed_pw = hashlib.sha256(password.encode()).hexdigest()
+    try:
+        c.execute("INSERT INTO users (username, password) VALUES (?, ?)", (username, hashed_pw))
+        conn.commit()
+        return True
+    except sqlite3.IntegrityError:
+        return False
+    finally:
+        conn.close()
+
+def verify_user(username, password):
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    hashed_pw = hashlib.sha256(password.encode()).hexdigest()
+    c.execute("SELECT * FROM users WHERE username=? AND password=?", (username, hashed_pw))
+    user = c.fetchone()
+    conn.close()
+    return user is not None
+
+# --- Session Management ---
+
+def create_session(username, title, image_path):
+    """Creates a new chat session."""
+    session_id = str(uuid.uuid4())
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    c.execute("INSERT INTO sessions (session_id, username, title, image_path, created_at) VALUES (?, ?, ?, ?, ?)",
+              (session_id, username, title, image_path, datetime.now()))
+    conn.commit()
+    conn.close()
+    return session_id
+
+def get_user_sessions(username):
+    """Returns list of sessions for the sidebar."""
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    c.execute("SELECT session_id, title, created_at FROM sessions WHERE username=? ORDER BY created_at DESC", (username,))
+    rows = c.fetchall()
+    conn.close()
+    return rows
+
+def get_session_details(session_id):
+    """Get image path for a session."""
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    c.execute("SELECT image_path, title FROM sessions WHERE session_id=?", (session_id,))
+    row = c.fetchone()
+    conn.close()
+    return row
+
+def save_message(session_id, role, content):
+    """Save a message to a specific session."""
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    c.execute("INSERT INTO messages (session_id, role, content) VALUES (?, ?, ?)", 
+              (session_id, role, content))
+    conn.commit()
+    conn.close()
+
+def get_session_messages(session_id):
+    """Get full chat history for a session."""
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    c.execute("SELECT role, content FROM messages WHERE session_id=? ORDER BY id ASC", (session_id,))
+    rows = c.fetchall()
+    conn.close()
+    return rows

models/segformer_custom/config.json

@@ -0,0 +1,93 @@
+{
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    32,
+    64,
+    160,
+    256
+  ],
+  "id2label": {
+    "0": "Background",
+    "1": "Building Flooded",
+    "2": "Building Non-Flooded",
+    "3": "Road Flooded",
+    "4": "Road Non-Flooded",
+    "5": "Water",
+    "6": "Tree",
+    "7": "Vehicle",
+    "8": "Pool",
+    "9": "Grass"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "Background": 0,
+    "Building Flooded": 1,
+    "Building Non-Flooded": 2,
+    "Grass": 9,
+    "Pool": 8,
+    "Road Flooded": 3,
+    "Road Non-Flooded": 4,
+    "Tree": 6,
+    "Vehicle": 7,
+    "Water": 5
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "transformers_version": "4.57.1"
+}

models/segformer_custom/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68e3a4c70f61f670610e2075510ef32868b85e66921d99e06b5c5bd32e9cb60d
+size 14893008

models/segformer_custom/preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_reduce_labels": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "SegformerImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 512,
+    "width": 512
+  }
+}

models/yolov8_floodnet.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afa4b42a5f0f35c1f7a3f91fa3c56cd7ea88f4501a14a2ccc1f34bdba42f8fb7
+size 52077074

requirements.txt

@@ -0,0 +1,17 @@
+streamlit
+gradio
+torch
+torchvision
+transformers
+accelerate
+bitsandbytes
+langchain
+langchain-community
+langchain-groq
+langchain-huggingface
+ultralytics
+sahi
+opencv-python-headless
+sqlalchemy
+python-dotenv
+pillow

tools.py

@@ -0,0 +1,114 @@
+import torch
+from PIL import Image
+import numpy as np
+import json
+import os
+import cv2
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+from sahi import AutoDetectionModel
+from sahi.predict import get_sliced_prediction
+from langchain_core.tools import tool
+
+# --- CONFIGURATION ---
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# FloodNet Color Palette
+FLOODNET_PALETTE = np.array([
+    [0, 0, 0],        # 0: Background
+    [255, 0, 0],      # 1: Building Flooded (Red)
+    [139, 69, 19],    # 2: Building Non-Flooded (Brown)
+    [0, 0, 139],      # 3: Road Flooded (Dark Blue)
+    [128, 128, 128],  # 4: Road Non-Flooded (Gray)
+    [0, 191, 255],    # 5: Water (Light Blue)
+    [34, 139, 34],    # 6: Tree (Green)
+    [255, 215, 0],    # 7: Vehicle (Yellow)
+    [0, 255, 255],    # 8: Pool (Cyan)
+    [50, 205, 50]     # 9: Grass (Lime)
+], dtype=np.uint8)
+
+# --- LOAD MODELS ---
+YOLO_PATH = os.path.join("models", "yolov8_floodnet.pt")
+SEG_PATH = os.path.join("models", "segformer_custom")
+
+print(f"Loading Models on {DEVICE}...")
+try:
+    detection_model = AutoDetectionModel.from_pretrained(
+        model_type='yolov8', model_path=YOLO_PATH, confidence_threshold=0.5, device=DEVICE
+    )
+    
+    seg_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0", do_reduce_labels=False)
+    seg_model = SegformerForSemanticSegmentation.from_pretrained(SEG_PATH)
+    seg_model.to(DEVICE)
+    if DEVICE == "cuda": seg_model.half()
+except Exception as e:
+    print(f"⚠️ Model Load Error: {e}")
+    detection_model = None
+    seg_model = None
+
+@tool
+def object_detection_tool(image_path: str) -> str:
+    """Detects objects (Flooded Building, Vehicle, etc.)."""
+    try:
+        if not os.path.exists(image_path): return json.dumps({"error": "File not found"})
+        
+        result = get_sliced_prediction(
+            image_path, detection_model, slice_height=2000, slice_width=2000,
+            overlap_height_ratio=0.05, overlap_width_ratio=0.05
+        )
+
+        detections = []
+        counts = {}
+        for obj in result.object_prediction_list:
+            if obj.score.value < 0.5: continue
+            label = obj.category.name
+            detections.append({"label": label, "confidence": round(obj.score.value, 2)})
+            counts[label] = counts.get(label, 0) + 1
+
+        return json.dumps({"counts": counts, "detections": detections})
+    except Exception as e:
+        return json.dumps({"error": str(e)})
+
+@tool
+def semantic_segmentation_tool(image_path: str) -> str:
+    """
+    Segments terrain.
+    Returns JSON: {"map_path": "...", "pixel_counts": {...}}
+    """
+    try:
+        image = Image.open(image_path).convert("RGB")
+        orig_size = image.size
+        
+        # Resize for inference
+        inputs = seg_processor(images=image.resize((768, 768), Image.BILINEAR), return_tensors="pt").to(DEVICE)
+        if DEVICE == "cuda": inputs['pixel_values'] = inputs['pixel_values'].half()
+
+        with torch.no_grad():
+            outputs = seg_model(**inputs)
+            
+        upsampled = torch.nn.functional.interpolate(outputs.logits, size=(768, 768), mode='bilinear', align_corners=False)
+        pred_seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8)
+        
+        # Resize mask to original size
+        final_mask = cv2.resize(pred_seg, orig_size, interpolation=cv2.INTER_NEAREST)
+        
+        # --- NEW: CALCULATE PIXEL STATS ---
+        # Count pixels for each class (0-9)
+        unique, counts = np.unique(final_mask, return_counts=True)
+        pixel_stats = dict(zip(unique.tolist(), counts.tolist()))
+        
+        # Save Colored Map
+        color_mask = FLOODNET_PALETTE[final_mask]
+        save_dir = "output_maps"
+        os.makedirs(save_dir, exist_ok=True)
+        filename = os.path.basename(image_path).replace('.jpg', '_seg_mask.png')
+        out_path = os.path.join(save_dir, filename)
+        Image.fromarray(color_mask).save(out_path)
+        
+        # Return BOTH path and stats
+        return json.dumps({
+            "map_path": out_path,
+            "pixel_counts": pixel_stats # Dictionary of {class_id: count}
+        })
+        
+    except Exception as e:
+        return json.dumps({"error": str(e)})